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Showing posts with label Article. Show all posts

Converting between binary, decimal, and hexadecimal notations

Converting between binary, decimal, and hexadecimal notations

Conversion Code - Chart
DECIMAL0123456789
HEX0123456789
BINARY0000000100100011010001010110011110001001
Conversion Code - Chart
DECIMAL101112131415
HEXABCDEF
BINARY10101011 1100110111101111

Ever since the Stone Age we humans have counted on our ten fingers. Our system of numbers shows it. We have ten elementary numbers called "digits"; a word that also means fingers. We count on our fingers 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9. To go further, we use place notation, allowing us to represent larger numbers as sequences of digits. Each digit in such a sequence has a different value depending on its place. For example, 13 means ten x 1 + 3, a number 4 greater than 9. We usually call the number thirteen, an alteration of "three ten". When we run out of two-digit numbers at 99, we go to three digits, with 100, which means 1 x hundred + 0 x ten + 0. A hundred is ten times ten. Similarly, the number 1000, with a one in the fourth place left of the end, means ten times ten times ten, or ten times a hundred, or a thousand. Place notation allows us to represent any conceivable number as a sequence of digits.

This may be fine for human beings who twiddle fingers all the time, but the idea of tenness is unknown to computers. All they understand is on and off. Either a bit (a core in memory or a place with a charge) is charged or discharged; magnetized or unmagnetized, and so forth. That is just two values. Therefore, computers are built to be based on a number system where one counts by twos, the binary system.

In binary, there are just two digits, 0 and 1. We may have .jpg files, HTML browsers, Power Point presentations, and DLLs, but when you get down to the lowest layer of any of these, they turn out to be just strings of ones and zeroes. It turns out you don't need ten digits to count all conceivable numbers. All you need is just two digits, or bits (short for binary digits). So one can start counting with 0, 1. What's next? Use place notation. The next number can't be 2, since we are not allowing use of that digit. So we must go to the next place and call it 10. This is not ten. It is two. That says the one in the second place means 1 times two. We continue counting, and go 0, 1, 10, 11. Now we run out just like we did at 99 in decimal. So we go to the third place and get 100, which we call "four". So the third place represents fours. Each place is double the previous one, so the next one is eights, then sixteens, thirty-twos, and so forth.

Take a binary number like 10101011. This means 1 x 128 + 0 x 64 + 1 x 32 + 0 x 16 + 1 x 8 + 0 x 4 + 1 x 2 + 1 x 1, which is 128+32+8+2+1 or 171 in decimal. (I used decimal numbers like "16" since that is just about the only way we know them).


After working with them a while, we can remember that 1100 is twelve and 101 is five and so forth, but what is 110100101010110101000100001111010101010100010101? I am not sure even by glancing at it how big that number is! Human beings are just not built for binary numbers. We can conceive of a concept with ten values, like a digit, but we can hold only 6 or 7 digits in our mind at any time. This limits us in binary to about 128.

To make it easier to comprehend computer numbers, another base is used: sixteen, which is two to the fourth power, or 10000 in binary; 16 in decimal. In this system we count by sixteens. So we count 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, like in decimal. We need more digits to get to sixteen. Human culture has not provided us with any digits, so we need to make up our own. Usually we use the other common type of character in our culture, the alphabet letter. So we continue A, B, C, D, E, F. This means that A is ten, B is eleven, and so forth to F which is fifteen. After that comes 10, meaning sixteen. Then 11, 12, ..., 100 (which is sixteen times sixteen or 256), and so forth.

We could do all our arithmetic in base sixteen. We would have to learn new addition and multiplication tables (4 + 8 = C; 7 x 9 = 3F, and so forth), and there would be more facts to learn. However, decimal is likely to remain supreme for humans in the near future. But why bring up base sixteen, also called hexadecimal, which gets Latin about it? Because it is easy to convert from binary to sixteen. It turns out that hexadecimal digits correspond to groups of four binary digits, so that 5 is 0101, B is 1011 and so forth. This means that 10110101 = 1011 0101 is B5 in hexadecimal.

Still if we use base 2 and sixteen for computers, there is the need for converting these between each other and decimal. It is not obvious that 171 is 10101011 in binary or AB in hexadecimal. We need a quick way of converting back and forth.

I find these methods to be quickest by hand. I will explain first how to go between decimal and binary. Then I will describe how to go between either of these and hexadecimal. Given a decimal number like 233. How do you convert it to binary? First write down 233 and write half the number below it (116). If there is a remainder, ignore it. Repeat the process; that is, write half of 116 below the 116. That is 58. Continue until you get to 1. The result is:


233
116
58
29
14
7
3
1

Now write to the right of each number in this column a 1 if the number is odd and a 0 if the number is even. The result is:

233 1
116 0
58 0
29 1
14 0
7 1
3 1
1 1

Now read off the binary representation, going up from the bottom, to get 11101001.

To convert a binary number to decimal, one takes this process in reverse. Suppose one wants to convert 101110101101 to decimal. Write the number in a column like this, and place a zero to the upper left of the top of the column:

0
1
0
1
1
1
0
1
0
1
1
0
1

Starting with the 0, double it down the column. If the digit to the right is a 0, simply double the previous number. If it is a 1, double the previous number and add 1. Since the first digit is a 1, double the 0 and add 1. The result is 1. The next digit is a 0. So simply double the 1 to get 2. Since the next digit is a 1, double the 2 and add one to get 5. Keep doing this down the column and the last number in the column on the left will be the decimal number that corresponds to binary number 101110101101. The result is:

0
1 1
2 0
5 1
11 1
23 1
46 0
93 1
186 0
373 1
747 1
1494 0
2989 1

So the decimal equivalent is 2,989.

Now how about hexadecimal? First of all, converting between binary and hexadecimal. The first thing to do is to memorize this table. Know it by heart. When you see 1010, automatically think "A". 0110 should bring you visions of "6". If you know Morse Code, it is the same sort of drill, but with only 16 digits instead of 26 letters. Memorize this table:

0000 0
0001 1
0010 2
0011 3
0100 4
0101 5
0110 6
0111 7
1000 8
1001 9
1010 A
1011 B
1100 C
1101 D
1110 E
1111 F

Once you have this table memorized, it is simple to convert from binary to hexadecimal. Simply split the binary number into groups of four and translate them into hexadecimal digits that you just memorized. For example, to convert 101110101101 to hexadecimal, split it into fours:

1011 1010 1101
Then translate the fours into hexadecimal:
1011 1010 1101
B A D

so the hexadecimal for 101110101101 is BAD. No that does not mean there is something wrong with it. BAD is the hexadecimal number "eleven ten thirteen".

To convert from hexadecimal to binary, do the process in reverse. Replace each hexadecimal number with its binary equivalent. For example, let's ring out the old DECADE. No, let's just convert it to binary for now. The result is:

D E C A D E
1101 1110 1100 1010 1101 1110

so the binary is 110111101100101011011110.

To convert between decimal and hexadecimal, convert to binary first. For example, to convert 1001, a thousand and one, to hexadecimal, first convert 1001 to binary:

1001 1
500 0
250 0
125 1
62 0
31 1
15 1
7 1
3 1
1 1

The binary is 1111101001. This has ten digits, which is not divisible by four, so add zeroes to make it divisible by four. In this case, add two zeroes: 001111101001. Then split it up and obtain the hexadecimal:

0011 1110 1001
3 E 9

so the hexadecimal is 3E9. The hexadecimal for 1001 is 3E9.
To convert the other way, from hexadecimal, convert it to binary and then to decimal. For example, to convert 2B2B to decimal, convert it to binary first:

2 B 2 B
0010 1011 0010 1011

The binary for 2B2B is 0010101100101011, or 10101100101011, since the initial zeroes don't add anything. Convert it to decimal:

0
1 1
2 0
5 1
10 0
21 1
43 1
86 0
172 0
345 1
690 0
1381 1
2762 0
5525 1
11051 1


The decimal for 2B2B is 11,051.

With these methods in mind, once you memorize that table above, you can quickly convert from decimal to binary and back again. Good luck converting!

Ref : http://www.mindspring.com/~jimvb/binary.htm
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Microsoft CEO: " No interest in buying Yahoo "



SYDNEY, Australia - Yahoo Inc. shares dived nearly 13 percent after the chief executive of Microsoft Corp. said Friday the software giant is not interested in renewing its bid for the struggling Internet company.

Microsoft's Steve Ballmer told a business lunch in Sydney that he had moved on after Yahoo rejected its takeover bid in the spring. He did suggest a partnership in the search engine market is possible.

"We made an offer, we made another offer, and it was clear that Yahoo didn't want to sell the business to us and we moved on," Ballmer said. "We are not interested in going back and re-looking at an acquisition. I don't know why they would be either, frankly. They turned us down at $33 a share."

Yahoo shares fell $1.76 to close Friday at $12.20.

Yahoo's co-founder and chief executive, Jerry Yang, said Wednesday that Microsoft should make another bid for his company, which runs the world's No. 2 search engine. His appeal came after top search engine Google Inc. backed out of an Internet advertising partnership with Yahoo to avoid a challenge from the U.S. Justice Department, which said it would sue to block the deal on antitrust grounds.

Yahoo had been counting on the Google Inc. deal to boost its finances and placate shareholders still incensed by management's decision to reject the $47.5 billion takeover bid from Microsoft six months ago.

"I'm sure there are still some opportunities for some kind of partnership around search, but I think (an) acquisition is a thing of the past," Ballmer said.

He also told the Australian audience that Microsoft saw an opportunity to reinvent the online search process.

"If anybody thinks the future of search is going to look like the present search, that's crazy," Ballmer said. "The user interface on search hasn't changed for six years. You still get the same dull, boring 10 blue links, for God's sake. Can't we do any better than that?"

Microsoft shares gained 3 percent to $21.50.
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What is Pay Per Click?

Pay per click (PPC) is an Internet advertising model used on search engines, advertising networks, and content websites, such as blogs, where advertisers only pay when a user actually clicks on an advertisement to visit the advertisers' website. With search engines, advertisers typically bid on keyword phrases relevant to their target market. When a user types a keyword query matching an advertiser's keyword list, or views a webpage with relevant content, the advertisements may be displayed. Such advertisements are called sponsored links or sponsored ads, and appear adjacent to or above the "natural" or organic results on search engine results pages, or anywhere a webmaster or blogger chooses on a content page. Content websites commonly charge a fixed price for a click rather than use a bidding mechanism.


Although many PPC providers exist, Google AdWords, Yahoo! Search Marketing, and Microsoft adCenter are the largest network operators as of 2007. Minimum prices per click, often referred to as costs per click (CPC), vary depending on the search engine and the level of competition for a particular phrase or keyword list—with some CPCs as low as US$0.01. Very popular search terms can cost much more on popular search engines. The PPC advertising model is open to abuse through click fraud, although Google and other search engines have implemented automated systems to guard against abusive clicks by competitors or corrupt webmasters.[1]




Categories

Pay per click campaigns can be categorized into two major categories: sponsored match (or keyword) and content match. Sponsored match campaigns involve the display of advertisements on search engine results pages, whereas content match campaigns involve the display of advertisements on publisher websites, newsletters, and e-mails.[2]

There are other types of pay per click programs that target product or service searches and product comparison sites. Search engine companies may participate in more than one category. PPC programs do not generate any revenue solely from Web traffic for websites that display the advertisements: Revenue is generated only when a user clicks on the advertisement itself.

Keyword-based PPC

Keyword-based pay per click advertisers bid on search terms—keywords consisting of words or phrases, and possibly product model numbers. When a user searches for a particular keyword, the list of advertiser links appears, where the ordering of those links is based on the amount bid for the given keyword. Keywords are the very heart of PPC advertising, and are guarded as highly-valued trade secrets by the advertisers. Many advertising firms offer software or services to help advertisers develop keyword strategies. Content Match, a service offered by Yahoo!, distributes the keyword ad to the search engine's partner sites and/or publishers that have distribution agreements with the search engine company.

As of 2007, the following are notable PPC keyword search engines:

Ask.com
Baidu
Google AdWords
LookSmart
Microsoft adCenter
MIVA
Yahoo! Search Marketing
Yandex

Product engines


Product engines (a.k.a. product comparison engines or price comparison engines) are search engines for products, and let advertisers provide "feeds" of their product databases. When a user searches for a product, links to advertisers are displayed for that particular product. More prominence is given to advertisers who pay more; however, the user can typically sort by price.

Some product engines such as Shopping.com use a pay per click model and have a defined rate card.[3] Other engines such as Google Product Search, part of Google Base (previously known as Froogle), do not charge for the listing, but still require an active product feed to function.[4]

The following are notable PPC product engines:

NexTag
PriceGrabber
Shopping.com
Shopzilla

Service engines


Service engines allow advertisers to provide feeds of their service databases. When a user searches for a service, links to advertisers are displayed for that particular service. More prominence is given to advertisers who pay more; however, the user can typically sort by price or other criteria. Some pay per click product engines have expanded into the service space, while other service engines operate in specific vertical markets.

The following are notable PPC service engines:

NexTag
SideStep
TripAdvisor

Pay per call
Pay-per-call is a business model for advertisement listings in search engines and directories that allows publishers to charge local advertisers on a per-telephone-call basis for each sales lead (i.e., call) the publishers generate. The term "pay per call" is sometimes confused with click-to-call, which along with call tracking, is a technology that enables the pay-per-call business model. Pay per call is not restricted only to local advertisers: Many of the pay per call search engines allow advertisers with a national presence to create advertisements with local telephone numbers. According to the Kelsey Group, the pay per call market is expected to reach US$3.7 billion by 2010.[5]

Pay per delivery

Pay per delivery is a variation on pay per click used in e-mail marketing. E-mail marketing campaigns are charged only on the basis of e-mails that are delivered successfully.


Pay per action

Pay per action (PPA) is a variation on pay per click adopted by many search engines. An advertiser pays a specified amount upon successful completion of some action (e.g., conversion, sales lead, or sale). PPA was a beta test for advertising distribution within the Google Content Network. However, Google announced in July 2008 that the program will be discontinued in August 2008.


History
In February 1998 Jeffrey Brewer of Goto.com, a 25-employee startup company (later Overture, now part of Yahoo!), presented a pay per click search engine proof-of-concept to the TED8 conference in California.[6] This presentation and the events that followed created the PPC advertising system. Credit for the concept of the PPC model is generally given to Idealab and Goto.com founder, Bill Gross.

Google started search engine advertising in December 1999. It was not until October 2000 before the AdWords system was introduced, allowing advertisers to create text ads for placement on the Google search engine. However, PPC was only introduced in 2002; until then, advertisements were charged at cost-per-thousand impressions. Yahoo! advertisements have always been PPC-based since their introduction in 1998.

For a more in-depth presentation of PPC's history, see Fain and Pedersen (2006).[7]


Use in "paid to surf" websites

Pay per click search engines enlist members, known as affiliates, to display webpages called portals, which display various keywords. PPC affiliates will advertise their portals to paid to read (PTR) websites, in the hopes that the PTR affiliates will click a keyword, and then click one of the results. The PPC affiliates receive a small commission for each search.

Owners of paid to surf websites may advertise their own search portals, sending more advertisements—and essentially more money—to their affiliates who click more keywords and search results. However, this process is frowned upon, as many consider these webmasters to be forcing their affiliates into committing click fraud, as the majority of the paid to surf website's revenue may come from the webmasters' PPC commissions.

ref:http://en.wikipedia.org/wiki/Pay_per_click

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GOOGLE CHROME

Google Chrome is an open source, free web browser developed by Google that has about 1% share of browser market. It was first released as a beta version for Microsoft Windows on 2 September 2008. The name is derived from the graphical user interface frame, or "chrome", of web browsers.
Chromium is the open source project behind Google Chrome, and is released under the BSD license. It implements the same feature set, but has a slightly different logo.


History

Announcement
The release announcement was originally scheduled for 3 September 2008, and a comic by Scott McCloud was to be sent to journalists and bloggers explaining the features of and motivations for the new browser.[6] Copies intended for Europe were shipped early and German blogger Philipp Lenssen of Google Blogoscoped[7] made a scanned copy of the 38-page comic available on his website after receiving it on 1 September 2008.[8] Google subsequently made the comic available on Google Books and their site[9] and mentioned it on its official blog along with an explanation for the early release.[10]

Public release

The browser was first publicly released for Microsoft Windows (XP and later only) on 2 September 2008 in 43 languages, officially a beta version. Chrome quickly gained about 1% market share. Mac OS X and Linux versions are under development.[11][12][13][14]
On 2 September, a CNET news item[15] drew attention to a passage in the terms of service for the initial beta release, which seemed to grant to Google a license to all content transferred via the Chrome browser. The passage in question was inherited from the general Google terms of service.[16] On the same day, Google responded to this criticism by stating that the language used was borrowed from other products, and removed the passage in question from the Terms of Service.[17] Google noted that this change would "apply retroactively to all users who have downloaded Google Chrome."[18] There were subsequent concerns about the browser's use of an unusual tracking feature that sends information about visited websites back to Google. The company stated that this is only enabled when users opt in by checking the option "help make Google Chrome better by automatically sending usage statistics and crash reports to Google" when the browser is installed.[19]
The first release of Google Chrome passed the Acid1 and Acid2 tests. While it did not pass the Acid3 test, it scored 78 out of 100 required to pass the test. This is higher than both Internet Explorer 7 (14) and Firefox 3 (71), but lower than Opera (84).[20] When compared to development builds, Chrome scored lower than Firefox (85), Opera (99), and Safari (100), but still higher than Internet Explorer (21).[20]
[edit]Unofficial Chromium releases, workarounds and mod utilities
On 15 September 2008, CodeWeavers released an unofficial bundle of a WINE derivative and Chromium Developer Build 21 for Linux and Mac OS X, which they dubbed "CrossOver Chromium".[21][22]
An unofficial workaround for use with Windows 2000 was referenced on one of Chromium's issue discussion pages.[23]
An unofficial patch was also released to fix a scrolling bug, which affected certain mouse software.[24]
Iron is a release of Chromium software that explicitly disables the collection and transmission of usage information to Google which is optional within Chrome.[25]

Development

Primary design goals were improvements in security, speed, and stability compared to existing browsers. There also were extensive changes in the user interface.[9] Chrome was assembled from 26 different code libraries from Google and others from third parties such as Netscape.[26]

Security

Chrome periodically downloads updates of two blacklists (one for phishing and one for malware), and warns users when they attempt to visit a harmful site. This service is also made available for use by others via a free public API called "Google Safe Browsing API". Google notifies the owners of listed sites who may not be aware of the presence of the harmful software.[9]
Chrome will typically allocate each tab to fit into its own process to "prevent malware from installing itself" or "using what happens in one tab to affect what happens in another", however the actual process allocation model is more complex.[27] Following the principle of least privilege, each process is stripped of its rights and can compute, but can not write files or read from sensitive areas (e.g. documents, desktop)—this is similar to the "Protected Mode" that is used by Internet Explorer 7 on Windows Vista. The Sandbox Team is said to have "taken this existing process boundary and made it into a jail";[28] for example, malicious software running in one tab is unable to sniff credit card numbers, interact with the mouse, or tell "Windows to run an executable on start-up" and it will be terminated when the tab is closed. This enforces a simple computer security model whereby there are two levels of multilevel security (user and sandbox) and the sandbox can only respond to communication requests initiated by the user.[29]
Typically, Plugins such as Adobe Flash Player are not standardized and as such, cannot be sandboxed as tabs can be. These often need to run at, or above, the security level of the browser itself. To reduce exposure to attack, plugins are run in separate processes that communicate with the renderer, itself operating at "very low privileges" in dedicated per-tab processes. Plugins will need to be modified to operate within this software architecture while following the principle of least privilege.[9] Chrome supports the Netscape Plugin Application Programming Interface (NPAPI),[30][31] but does not support the embedding of ActiveX controls.[31] Also, Chrome does not have an extension system such as Mozilla's XPInstall architecture.[32] Java applets support is available in Chrome as part of the pending Java 6 update 10, which currently is in Release Candidate testing.[33]
A private browsing feature called Incognito mode is provided that prevents the browser from storing any history information or cookies from the websites visited. This is similar to the private browsing feature available in Apple's Safari and the latest beta version of Internet Explorer 8.[34]
A denial-of-service vulnerability was found that allowed a malicious web page to crash the whole web browser.[35][36] However, Google Chrome developers confirmed the flaw, and it was fixed in the 0.2.149.29 release.[37]

Speed

The JavaScript virtual machine was considered a sufficiently important project to be split off (as was Adobe/Mozilla's Tamarin) and handled by a separate team in Denmark. Existing implementations were designed "for small programs, where the performance and interactivity of the system weren't that important," but web applications such as Gmail "are using the web browser to the fullest when it comes to DOM manipulations and Javascript." The resulting V8 JavaScript engine has features such as hidden class transitions, dynamic code generation, and precise garbage collection.[9] Tests by Google showed that V8 was about twice as fast as Firefox 3 and the Safari 4 beta.[38]
Several websites have performed benchmark tests using the SunSpider JavaScript Benchmark[1] tool as well as Google's own set of computationally intense benchmarks, which includes ray tracing and constraint solving.[39] They unanimously report that Chrome performs much faster than all competitors against which it has been tested, including Safari, Firefox 3, Internet Explorer 7, and Internet Explorer 8.[40][41][42][43] While Opera has not been compared to Chrome yet, in previous tests, it has been shown to be slightly slower than Firefox 3, which in turn, is slower than Chrome.[44][45] Another blog post by Mozilla developer Brendan Eich compared Chrome's V8 engine to his own TraceMonkey Javascript engine which is newly introduced in Firefox 3.1alpha, stating that some tests are faster in one engine and some are faster in the other, with Firefox 3.1a faster overall.[46] John Resig, Mozilla's JavaScript evangelist, further commented on the performance of different browsers on Google's own suite, finding Chrome "decimating" other browsers, but he questions whether Google's suite is representative of real programs. He states that Firefox performs poorly on recursion intensive benchmarks, such as those of Google, because the Mozilla team has not implemented recursion-tracing yet.[47]
Chrome also uses DNS prefetching to speed up website lookups.[48]

Stability

The Gears team was considering a multithreaded browser (noting that a problem with existing web browser implementations was that they are inherently single-threaded) and Chrome implemented this concept with a multi-process architecture,[49] similar to Loosely Coupled Internet Explorer (LCIE) recently implemented by Internet Explorer 8.[50] By default, a separate process is allocated to each site instance and plugin.[51] This prevents tasks from interfering with each other, which is good for security and stability; an attacker successfully gaining access to one application does not gain access to all, and failure in one application results in a Sad Tab screen of death, similar to the well-known Sad Mac, except only one single tab crashes instead of the whole application. This strategy exacts a fixed per-process cost up front, but results in less memory bloat overall as fragmentation is confined to each process and no longer results in further memory allocations.[52]
Chrome features a process management utility called the Task Manager which allows the user to "see what sites are using the most memory, downloading the most bytes and abusing [their] CPU" (as well as the plugins which run in separate processes) and terminate them.[9] Some users have reported a conflict with Internet Explorer, often resulting in the blue screen error on Windows.[53

User interface

The main user interface includes back, forward, refresh, bookmark, go, and cancel options. The options are similar to Safari, while the location of the settings is similar to versions of Internet Explorer starting with version 7. The design of the window is based on Windows Vista.
When the window is not maximized, the tab bar appears directly under the title bar. When maximized, the title bar disappears, and instead, the tab bar is shown at the very top of the window. Unlike other browsers such as Internet Explorer or Firefox which also have a full-screen mode that hides the operating system's interface completely, Chrome can only be maximized like a standard Windows application. Therefore, the Windows task bar, system tray, and start menu link still take space at all times unless they have been configured to hide at all times.
Chrome includes Gears, which adds features for web developers typically relating to the building of web applications (including offline support).[9]
Chrome replaces the browser home page which is displayed when a new tab is created with a New Tab Page. This shows[54] thumbnails of the nine most visited web sites along with the sites most often searched, recent bookmarks, and recently closed tabs.[9]
The Omnibox is the URL box at the top of each tab, which combines the functionalities of both URL box and search box. It includes autocomplete functionality, but only will autocomplete URLs that were manually entered (rather than all links), search suggestions, top pages (previously visited), popular pages (unvisited), and text search over history. Search engines also can be captured by the browser when used via the native user interface by pressing Tab.[9]
Popup windows "are scoped to the tab they came from" and will not appear outside the tab unless the user explicitly drags them out.[9] Popup windows do not run in their own process.[citation needed]
Chrome uses the WebKit rendering engine to display web pages, on advice from the Android team.[9] Like most browsers, Chrome was extensively tested internally before release with unit testing, "automated user interface testing of scripted user actions" and fuzz testing, as well as WebKit's layout tests (99% of which Chrome is claimed to have passed). New browser builds are automatically tested against tens of thousands of commonly accessed websites inside of the Google index within 20-30 minutes.[9]
Tabs are the primary component of Chrome's user interface and as such, have been moved to the top of the window rather than below the controls. This subtle change contrasts with many existing tabbed browsers which are based on windows and contain tabs. Tabs (including their state) can be transferred seamlessly between window containers by dragging. Each tab has its own set of controls, including the Omnibox.[9]
Chrome allows users to make local desktop shortcuts that open web applications in the browser. The browser, when opened in this way, contains none of the regular interface except for the title bar, so as not to "interrupt anything the user is trying to do." This allows web applications to run alongside local software (similar to Mozilla Prism and Fluid).[9]
By default, the status bar is hidden whenever it is not being used. However, it appears at the bottom left corner whenever a page is loading and when a hyperlink is hovered over.
For web developers, Chrome features an element inspector similar to the one in Firebug.[4

Usage Tracking and Communication

Google Chrome identifies each installation with a unique ID and collects usage statistics including keystrokes. Chrome's usage tracking option enables the software to regularly transmit this information to Google[55][not in citation given]. Usage tracking is an option presented to the user during the software's installation. Once accepted, it is possible to disable the transmission of this information by modifying Chrome's "Under the Hood" options.[56] Freeware programs such as UnChrome can remove the unique ID without having to change the browser.[57] Unofficial builds, such as SRWare Iron, seek to remove these features from the browser altogether[58].

Plugins & Themes

Recently, people have started releasing different plugins and themes (Similar to how Firefox uses themes and plugins) for the Chrome browser. Although there is no "official" program to install them with, users have found a way to use them.

Reception

The Daily Telegraph's Matthew Moore summarizes the verdict of early reviewers: "Google Chrome is attractive, fast and has some impressive new features, but may not—yet—be a threat to its Microsoft rival."[59]
Microsoft reportedly "played down the threat from Chrome" and "predicted that most people will embrace Internet Explorer 8."[60] Opera Software said that "Chrome will strengthen the Web as the biggest application platform in the world."[60] Mozilla said that Chrome's introduction into the web browser market comes as "no real surprise", that "Chrome is not aimed at competing with Firefox", — and furthermore, should not affect Google's financing of Firefox.[61][62]
Chrome’s design bridges the gap between desktop and so-called “cloud computing.” At the touch of a button, Chrome lets you make a desktop, Start menu, or Quick Launch shortcut to any Web page or Web application, blurring the line between what’s online and what’s inside your PC. For example, I created a desktop shortcut for Google Maps. When you create a shortcut for a Web application, Chrome strips away all of the toolbars and tabs from the window, leaving you with something that feels much more like a desktop application than like a Web application or page.
—PC World [63]
On September 9, 2008 the German Federal Office for Information Security (BSI) issued a statement about their first examination of Chrome, expressing a concern over the prominent download links on Google's German web page, because "beta versions should not be employed for general use applications" and browser manufactures should provide appropriate instructions regarding the use of pre-released software. They did, however, praise the browser's technical contribution to improving security on the web.[64]
Concern about Chrome's optional usage collection and tracking have been noted in several publications.[65][66]





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Google










Google Inc. is an American public corporation, earning revenue from advertising related to its Internet search, e-mail, online mapping, office productivity, social networking, and video sharing services as well as selling advertising-free versions of the same technologies. The Google headquarters, the Googleplex, is located in Mountain View, California. As of 30 June 2008 the company has 19,604 full-time employees.[3]





Google Inc. is an American public corporation, earning revenue from advertising related to its Internet search, e-mail, online mapping, office productivity, social networking, and video sharing services as well as selling advertising-free versions of the same technologies. The Google headquarters, the Googleplex, is located in Mountain View, California. As of 30 June 2008 the company has 19,604 full-time employees.[3]

Google was co-founded by Larry Page and Sergey Brin while they were students at Stanford University and the company was first incorporated as a privately held company on 4 September 1998. The initial public offering took place on 19 August 2004, raising US$1.67 billion, making it worth US$23 billion. Google has continued its growth through a series of new product developments, acquisitions, and partnerships. Environmentalism, philanthropy, and positive employee relations have been important tenets during the growth of Google, the latter resulting in being identified multiple times as Fortune Magazine's #1 Best Place to Work.[4] The unofficial company slogan is "Don't be evil", although criticism of Google includes concerns regarding the privacy of personal information, copyright, censorship, and discontinuation of services.

History of Google

Google began in January 1996, as a research project by Larry Page, who was soon joined by Sergey Brin, two Ph.D. students at Stanford University in California.[5] They hypothesized that a search engine that analyzed the relationships between websites would produce better ranking of results than existing techniques, which ranked results according to the number of times the search term appeared on a page.[6] Their search engine was originally nicknamed "BackRub" because the system checked backlinks to estimate the importance of a site.[7] A small search engine called Rankdex was already exploring a similar strategy.[8]
Convinced that the pages with the most links to them from other highly relevant web pages must be the most relevant pages associated with the search, Page and Brin tested their thesis as part of their studies, and laid the foundation for their search engine. Originally, the search engine used the Stanford University website with the domain google.stanford.edu. The domain google.com was registered on 15 September 1997,[9] and the company was incorporated as Google Inc. on 4 September 1998 at a friend's garage in Menlo Park, California. The total initial investment raised for the new company amounted to almost US$1.1 million, including a US$100,000 check by Andy Bechtolsheim, one of the founders of Sun Microsystems.[10]
In March 1999, the company moved into offices in Palo Alto, home to several other noted Silicon Valley technology startups.[11] After quickly outgrowing two other sites, the company leased a complex of buildings in Mountain View at 1600 Amphitheatre Parkway from Silicon Graphics (SGI) in 2003.[12] The company has remained at this location ever since, and the complex has since come to be known as the Googleplex (a play on the word googolplex). In 2006, Google bought the property from SGI for US$319 million.[13]
The Google search engine attracted a loyal following among the growing number of Internet users, who liked its simple design and usability.[14] In 2000, Google began selling advertisements associated with search keywords.[5] The ads were text-based to maintain an uncluttered page design and to maximize page loading speed.[5] Keywords were sold based on a combination of price bid and clickthroughs, with bidding starting at US$.05 per click.[5] This model of selling keyword advertising was pioneered by Goto.com (later renamed Overture Services, before being acquired by Yahoo! and rebranded as Yahoo! Search Marketing).[15][16][17] Goto.com was an Idealab spin off created by Bill Gross, and was the first company to successfully provide a pay-for-placement search service. Overture Services later sued Google over alleged infringements of Overture's pay-per-click and bidding patents by Google's AdWords service. The case was settled out of court, with Google agreeing to issue shares of common stock to Yahoo! in exchange for a perpetual license.[18]. Thus, while many of its dot-com rivals failed in the new Internet marketplace, Google quietly rose in stature while generating revenue.[5]
The name "Google" originated from a common misspelling of the word "googol",[19][20] which refers to 10100, the number represented by a 1 followed by one hundred zeros. Having found its way increasingly into everyday language, the verb "google", was added to the Merriam Webster Collegiate Dictionary and the Oxford English Dictionary in 2006, meaning "to use the Google search engine to obtain information on the Internet."[21][22]
A patent describing part of the Google ranking mechanism (PageRank) was granted on 4 September 2001.[23] The patent was officially assigned to Stanford University and lists Lawrence Page as the inventor.

Financing and initial public offering
The first funding for Google as a company was secured in 1998, in the form of a US$100,000 contribution from Andy Bechtolsheim, co-founder of Sun Microsystems, given to a corporation which did not yet exist.[24] Around six months later, a much larger round of funding was announced, with the major investors being rival venture capital firms Kleiner Perkins Caufield & Byers (John Doerr) and Sequoia Capital (Michael Moritz), Google being a rare co-investment by the two rivals.[24]
The Google IPO took place on 19 August 2004. 19,605,052 shares were offered at a price of US$85 per share.[25][26] Of that, 14,142,135 (another mathematical reference as √2 ≈ 1.4142135) were floated by Google, and the remaining 5,462,917 were offered by existing stockholders. The sale of US$1.67 billion gave Google a market capitalization of more than US$23 billion.[27] The vast majority of the 271 million shares remained under the control of Google. Many Google employees became instant paper millionaires. Yahoo!, a competitor of Google, also benefited from the IPO because it owned 8.4 million shares of Google as of 9 August 2004, ten days before the IPO.[28]
The stock performance of Google after its first IPO launch has gone well, with shares hitting US$700 for the first time on 31 October 2007,[29] due to strong sales and earnings in the advertising market, as well as the release of new features such as the desktop search function and its iGoogle personalized home page.[30] The surge in stock price is fueled primarily by individual investors, as opposed to large institutional investors and mutual funds.[30]
The company is listed on the NASDAQ stock exchange under the ticker symbol GOOG and under the London Stock Exchange under the ticker symbol GGEA.

Growth
While the primary business interest is in the web content arena, Google has begun experimenting with other markets, such as radio and print publications. On 17 January 2006, Google announced that its purchase of a radio advertising company "dMarc", which provides an automated system that allows companies to advertise on the radio.[31] This will allow Google to combine two niche advertising media—the Internet and radio—with Google's ability to laser-focus on the tastes of consumers. Google has also begun an experiment in selling advertisements from its advertisers in offline newspapers and magazines, with select advertisements in the Chicago Sun-Times.[32] They have been filling unsold space in the newspaper that would have normally been used for in-house advertisements.

Acquisitions
Since 2001, Google has acquired several small start-up companies.
In 2004, Google acquired a company called Keyhole, Inc. [33], which developed a product called Earth Viewer which was renamed in 2005 to Google Earth
In February 2006, software company Adaptive Path sold Measure Map, a weblog statistics application, to Google. Registration to the service has since been temporarily disabled. The last update regarding the future of Measure Map was made on 6 April 2006 and outlined many of the known issues of the service.[34]
In late 2006, Google bought online video site YouTube for US$1.65 billion in stock.[35] Shortly after, on 31 October 2006, Google announced that it had also acquired JotSpot, a developer of wiki technology for collaborative Web sites.[36]
On 13 April 2007, Google reached an agreement to acquire DoubleClick. Google agreed to buy the company for US$3.1 billion.[37]
On 9 July 2007, Google announced that it had signed a definitive agreement to acquire enterprise messaging security and compliance company

Partnerships
In 2005, Google entered into partnerships with other companies and government agencies to improve production and services. Google announced a partnership with NASA Ames Research Center to build up 1,000,000 square feet (93,000 m2) of offices and work on research projects involving large-scale data management, nanotechnology, distributed computing, and the entrepreneurial space industry.[39] Google also entered into a partnership with Sun Microsystems in October to help share and distribute each other's technologies.[40] The company entered into a partnership with AOL of Time Warner,[41] to enhance each other's video search services.
The same year, the company became a major financial investor of the new .mobi top-level domain for mobile devices, in conjunction with several other companies, including Microsoft, Nokia, and Ericsson among others.[42] In September 2007, Google launched, "Adsense for Mobile", a service for its publishing partners which provides the ability to monetize their mobile websites through the targeted placement of mobile text ads,[43] and acquired the mobile social networking site, Zingku.mobi, to "provide people worldwide with direct access to Google applications, and ultimately the information they want and need, right from their mobile devices."[44]
In 2006, Google and Fox Interactive Media of News Corp. entered into a US$900 million agreement to provide search and advertising on the popular social networking site, MySpace.[45]
Google has developed a partnership with GeoEye to launch a satellite providing Google with high-resolution (0.41m black and white, 1.65m color) imagery for Google Earth. The satellite was launched from Vandenberg Air Force Base on 6 September 2008.[46]

Products and services

Google appliance as shown at RSA Conference 2008
Main article: List of Google products
Google has created services and tools for the general public and business environment alike; including Web applications, advertising networks and solutions for businesses.

Advertising
99% of Google's revenue is derived from its advertising programs[47]. For the 2006 fiscal year, the company reported US$10.492 billion in total advertising revenues and only US$112 million in licensing and other revenues.[48] Google is able to precisely track users' interests across affiliated sites using DoubleClick technology[49] and Google Analytics.[50] Google's advertisements carry a lower price tag when their human ad-rating team working around the world believes the ads improve the company's user experience.[51] Google AdWords allows Web advertisers to display advertisements in Google's search results and the Google Content Network, through either a cost-per-click or cost-per-view scheme. Google AdSense website owners can also display adverts on their own site, and earn money every time ads are clicked.
Google has also been criticized by advertisers regarding its inability to combat click fraud, when a person or automated script is used to generate a charge on an advertisement without really having an interest in the product. Industry reports in 2006 claim that approximately 14 to 20 percent of clicks were in fact fraudulent or invalid.[52]

Software
The Google web search engine is the company's most popular service. As of August 2007, Google is the most used search engine on the web with a 53.6% market share, ahead of Yahoo! (19.9%) and Live Search (12.9%).[53] Google indexes billions of Web pages, so that users can search for the information they desire, through the use of keywords and operators, although at any given time it will only return a maximum of 1,000 results for any specific search query. Google has also employed the Web Search technology into other search services, including Image Search, Google News, the price comparison site Google Product Search, the interactive Usenet archive Google Groups, Google Maps, and more.
In 2004, Google launched its own free web-based e-mail service, known as Gmail (or Google Mail in some jurisdictions).[54] Gmail features spam-filtering technology and the capability to use Google technology to search e-mail. The service generates revenue by displaying advertisements and links from the AdWords service that are tailored to the choice of the user and/or content of the e-mail messages displayed on screen.
In early 2006, the company launched Google Video, which not only allows users to search and view freely available videos but also offers users and media publishers the ability to publish their content, including television shows on CBS, NBA basketball games, and music videos.[55]
Google has also developed several desktop applications, including Google Desktop, Picasa, SketchUp and Google Earth, an interactive mapping program powered by satellite and aerial imagery that covers the vast majority of the planet. Google Earth is generally considered to be remarkably accurate and extremely detailed. Many major cities have such detailed images that one can zoom in close enough to see vehicles and pedestrians clearly. Consequently, there have been some concerns about national security implications; contention is that the software can be used to pinpoint with near-precision accuracy the physical location of critical infrastructure, commercial and residential buildings, bases, government agencies, and so on. However, the satellite images are not necessarily frequently updated, and all of them are available at no charge through other products and even government sources; the software simply makes accessing the information easier. A number of Indian state governments have raised concerns about the security risks posed by geographic details provided by Google Earth's satellite imaging.[56]
Google has promoted their products in various ways. In London, Google Space was set-up in Heathrow Airport, showcasing several products, including Gmail, Google Earth and Picasa.[57][58] Also, a similar page was launched for American college students, under the name College Life, Powered by Google.[59]
In 2007, some reports surfaced that Google was planning the release of its own mobile phone, possibly a competitor to Apple's iPhone.[60][61][62] The project, called Android, an operating system provides a standard development kit that will allow any "Android" phone to run software developed for the Android SDK, no matter the phone manufacturer.
On 1 September 2008, Google pre-announced the upcoming availability of Google Chrome, an open-source web browser[63], which was released on 2 September 2008.

Enterprise Products
Google entered the Enterprise market in February, 2002 with the launch of its Google Search Appliance, targeted toward providing search technology to larger organizations[64]. Providing search for a smaller document repository, Google launched the Mini in 2005.
Late in 2006, Google began to sell Custom Search Business Edition, providing customers with an advertising-free window into Google.com's index[65]. In 2008, Google re-branded its next version of Custom Search Business Edition as Google Site Search[65].
In 2007, Google launched Google Apps Premier Edition, a version of Google Apps targeted primarily at the business user. It includes such extras as more disk space for e-mail, API access, and premium support, for a price of US$50 per user per year. A large implementation of Google Apps with 38,000 users is at Lakehead University in Thunder Bay, Ontario, Canada.[66]
Also in 2007, Google acquired Postini[67] and continued to sell the acquired technology[68] as Google Security Services[69].

Platform



Google runs its services on several server farms, each comprising thousands of low-cost commodity computers running stripped-down versions of Linux. While the company divulges no details of its hardware, a 2006 estimate cites 450,000 servers, "racked up in clusters at data centers around the world."[70]

Corporate affairs and culture

Left to right, Eric E. Schmidt, Sergey Brin and Larry Page
Google is known for its relaxed corporate culture, of which its playful variations on its own corporate logo are an indicator. In 2007 and 2008, Fortune Magazine placed Google at the top of its list of the hundred best places to work.[4] Google's corporate philosophy embodies such casual principles as "you can make money without doing evil," "you can be serious without a suit," and "work should be challenging and the challenge should be fun."[71]
Google has been criticized for having salaries below industry standards.[72] For example, some system administrators earn no more than US$35,000 per year – considered to be quite low for the Bay Area job market.[73] However, Google's stock performance following its IPO has enabled many early employees to be competitively compensated by participation in the corporation's remarkable equity growth.[74]
After the company's IPO in August 2004, it was reported that founders Sergey Brin and Larry Page, and CEO Eric Schmidt, requested that their base salary be cut to US$1.00.[75] Subsequent offers by the company to increase their salaries have been turned down, primarily because, "their primary compensation continues to come from returns on their ownership stakes in Google. As significant stockholders, their personal wealth is tied directly to sustained stock price appreciation and performance, which provides direct alignment with stockholder interests."[75] Prior to 2004, Schmidt was making US$250,000 per year, and Page and Brin each earned a salary of US$150,000.[75]
They have all declined recent offers of bonuses and increases in compensation by Google's board of directors. In a 2007 report of the United States' richest people, Forbes reported that Sergey Brin and Larry Page were tied for #5 with a net worth of US$18.5 billion each.[76]
In 2007 and through early 2008, Google has seen the departure of several top executives. Justin Rosenstein, Google’s product manager, left in June 2007.[77] Shortly thereafter, Gideon Yu, former chief financial officer of YouTube, a Google unit, joined Facebook[78] along with Benjamin Ling, a high-ranking engineer, who left in October 2007.[79] In March 2008, two senior Google leaders announced their desire to pursue other opportunities. Sheryl Sandburg, ex-VP of global online sales and operations began her position as COO of Facebook[80] while Ash ElDifrawi, former head of brand advertising, left to become CMO of Netshops Inc.[81]
Google's persistent cookie and other information collection practices have led to concerns over user privacy. As of 11 December 2007, Google, like the Microsoft search engine, stores "personal information for 18 months" and by comparison, Yahoo! and AOL (Time Warner) "retain search requests for 13 months."[82]
U.S. District Court Judge Louis Stanton, on July 1, 2008 ordered Google to give YouTube user data / log to Viacom to support its case in a billion-dollar copyright lawsuit against Google.[83][84] Google and Viacom, however, on July 14, 2008, agreed in compromise to protect YouTube users' personal data in the $1 billion (£ 497 million) copyright lawsuit. Google agreed it will make user information and internet protocol addresses from its YouTube subsidiary anonymous before handing over the data to Viacom. The privacy deal also applied to other litigants including the FA Premier League, the Rodgers & Hammerstein Organisation and the Scottish Premier League.[85][86] The deal however did not extend the anonymity to employees, since Viacom would prove that Google staff are aware of uploading of illegal material to the site. The parties therefore will further meet on the matter lest the data be made available to the court.[87]

Googleplex

The Googleplex

Google's headquarters in Mountain View, California, is referred to as "the Googleplex" in a play of words; a googolplex being 1010100, or a one followed by a googol of zeros, and the HQ being a complex of buildings (cf. multiplex, cineplex, etc). The lobby is decorated with a piano, lava lamps, old server clusters, and a projection of search queries on the wall. The hallways are full of exercise balls and bicycles. Each employee has access to the corporate recreation center. Recreational amenities are scattered throughout the campus and include a workout room with weights and rowing machines, locker rooms, washers and dryers, a massage room, assorted video games, Foosball, a baby grand piano, a pool table, and ping pong. In addition to the rec room, there are snack rooms stocked with various foods and drinks.[88]

Sign at the Googleplex
In 2006, Google moved into 311,000 square feet (28,900 m2) of office space in New York City, at 111 Eighth Ave. in Manhattan.[89] The office was specially designed and built for Google and houses its largest advertising sales team, which has been instrumental in securing large partnerships, most recently deals with MySpace and AOL.[89] In 2003, they added an engineering staff in New York City, which has been responsible for more than 100 engineering projects, including Google Maps, Google Spreadsheets, and others.[89] It is estimated that the building costs Google US$10 million per year to rent and is similar in design and functionality to its Mountain View headquarters, including foosball, air hockey, and ping-pong tables, as well as a video game area.[89] In November 2006, Google opened offices on Carnegie Mellon's campus in Pittsburgh.[90] By late 2006, Google also established a new headquarters for its AdWords division in Ann Arbor, Michigan.[91]
Google is taking steps to ensure that their operations are environmentally sound. In October 2006, the company announced plans to install thousands of solar panels to provide up to 1.6 megawatts of electricity, enough to satisfy approximately 30% of the campus' energy needs.[92] The system will be the largest solar power system constructed on a U.S. corporate campus and one of the largest on any corporate site in the world.[92] Google has faced accusations in Harper's Magazine[93] of being extremely excessive with their energy usage, and were accused of employing their "Don't be evil" motto as well as their very public energy saving campaigns as means of trying to cover up or make up for the massive amounts of energy their servers actually require.

Innovation time off
As an interesting motivation technique (usually called Innovation Time Off), all Google engineers are encouraged to spend 20% of their work time (one day per week) on projects that interest them. Some of Google's newer services, such as Gmail, Google News, Orkut, and AdSense originated from these independent endeavors.[94] In a talk at Stanford University, Marissa Mayer, Google's Vice President of Search Products and User Experience, stated that her analysis showed that half of the new product launches originated from the 20% time.[95]

Easter eggs and April Fool's Day jokes

Google has a tradition of creating April Fool's Day jokes—such as Google MentalPlex, which allegedly featured the use of mental power to search the web.[96] In 2002, they claimed that pigeons were the secret behind their growing search engine.[97] In 2004, they featured Google Lunar (which claimed to feature jobs on the moon),[98] and in 2005, a fictitious brain-boosting drink, termed Google Gulp was announced.[99] In 2006, they came up with Google Romance, a hypothetical online dating service.[100] In 2007, Google announced two joke products. The first was a free wireless Internet service called TiSP (Toilet Internet Service Provider)[101] in which one obtained a connection by flushing one end of a fiber-optic cable down their toilet and waiting only an hour for a "Plumbing Hardware Dispatcher (PHD)" to connect it to the Internet.[101] Additionally, Google's Gmail page displayed an announcement for Gmail Paper, which allows users of their free email service to have email messages printed and shipped to a snail mail address.[102]
Google's services contain a number of Easter eggs; for instance, the Language Tools page offers the search interface in the Swedish Chef's "Bork bork bork," Pig Latin, "Hacker" (actually leetspeak), Elmer Fudd, and Klingon.[103] In addition, the search engine calculator provides the Answer to Life, the Universe, and Everything from Douglas Adams' The Hitchhiker's Guide to the Galaxy.[104] As Google’s search box can be used as a unit converter (as well as a calculator), some non-standard units are built in, such as the Smoot. Google also routinely modifies its logo in accordance with various holidays or special events throughout the year, such as Christmas, Mother's Day, or the birthdays of various notable individuals.[105]

IPO and culture
Many people speculated that Google's IPO would inevitably lead to changes in the company's culture,[106] because of shareholder pressure for employee benefit reductions and short-term advances, or because a large number of the company's employees would suddenly become millionaires on paper. In a report given to potential investors, co-founders Sergey Brin and Larry Page promised that the IPO would not change the company's culture.[107] Later Mr. Page said, "We think a lot about how to maintain our culture and the fun elements. We spent a lot of time getting our offices right. We think it's important to have a high density of people. People are packed together everywhere. We all share offices. We like this set of buildings because it's more like a densely packed university campus than a typical suburban office park."[108] Google has faced allegations of sexism and ageism from former employees.[109][110]
However, many analysts are finding that as Google grows, the company is becoming more "corporate". In 2005, articles in The New York Times and other sources began suggesting that Google had lost its anti-corporate, no evil philosophy.[111][112][113] In an effort to maintain the company's unique culture, Google has designated a Chief Culture Officer in 2006, who also serves as the Director of Human Resources. The purpose of the Chief Culture Officer is to develop and maintain the culture and work on ways to keep true to the core values that the company was founded on in the beginning—a flat organization with a collaborative environment.[114]

Philanthropy

In 2004, Google formed a for-profit philanthropic wing, Google.org, with a start-up fund of US$1 billion.[115] The express mission of the organization is to create awareness about climate change, global public health, and global poverty. One of its first projects is to develop a viable plug-in hybrid electric vehicle that can attain 100 mpg. The founding and current director is Dr. Larry Brilliant.[116]

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What Is Google Adsense





AdSense is an advertisement serving program run by Google. Website owners can enroll in this program to enable text, image, and more recently, video advertisements on their websites. These advertisements are administered by Google and generate revenue on either a per-click or per-impression basis. Google is also currently beta-testing a cost-per-action based service.


Overview
Google uses its Internet search technology to serve advertisements based on website content, the user's geographical location, and other factors. Those wanting to advertise with Google's targeted advertisement system may enroll through AdWords. AdSense has become a popular method of placing advertising on a website because the advertisements are less intrusive than most banners, and the content of the advertisements is often relevant to the website.

Currently, AdSense uses JavaScript code to incorporate the advertisements into a participating website. If the advertisements are included on a website that has not yet been crawled by the Mediabot, AdSense will temporarily display advertisements for charitable causes, also known as public service announcements (PSAs). (The Mediabot is different from the Googlebot, which maintains Google's search index.)

Many websites use AdSense to monetize their content. AdSense has been particularly important for delivering advertising revenue to small websites that do not have the resources for developing advertising sales programs and salespeople. To fill a website with advertisements that are relevant to the topics discussed, webmasters implement a brief script on the websites' pages. Websites that are content-rich have been very successful with this advertising program, as noted in a number of publisher case studies on the AdSense website.

Some webmasters invest significant effort into maximizing their own AdSense income. They do this in three ways:[citation needed]

They use a wide range of traffic-generating techniques, including but not limited to online advertising.
They build valuable content on their websites that attracts AdSense advertisements, which pay out the most when they are clicked.
They use copy on their websites that encourages visitors to click on advertisements. Note that Google prohibits webmasters from using phrases like "Click on my AdSense ads" to increase click rates. The phrases accepted are "Sponsored Links" and "Advertisements".[citation needed]
The source of all AdSense income is the AdWords program, which in turn has a complex pricing model based on a Vickrey second price auction. AdSense commands an advertiser to submit a sealed bid (i.e., a bid not observable by competitors). Additionally, for any given click received, advertisers only pay one bid increment above the second-highest bid.


History
The underlying technology behind AdSense was derived originally from WordNet, Simpli (a company started by the founder of Wordnet, George A. Miller), and a number of professors and graduate students from Brown University, including James A. Anderson, Jeff Stibel, and Steve Reiss.[1] A variation of this technology utilizing WordNet was developed by Oingo, a small search engine company based in Santa Monica founded in 1998 by Gilad Elbaz and Adam Weissman.[2][3] Oingo changed its name to Applied Semantics in 2001,[4] which was later acquired by Google in April 2003 for US$102 million.[5]


AdSense for Feeds
In May 2005, Google announced a limited-participation beta version of AdSense for Feeds, a version of AdSense that runs on RSS and Atom feeds that have more than 100 active subscribers. According to the Official Google Blog, "advertisers have their ads placed in the most appropriate feed articles; publishers are paid for their original content; readers see relevant advertising—and in the long run, more quality feeds to choose from."[6]

AdSense for Feeds works by inserting images into a feed. When the image is displayed by a RSS reader or Web browser, Google writes the advertising content into the image that it returns. The advertisement content is chosen based on the content of the feed surrounding the image. When the user clicks the image, he or she is redirected to the advertiser's website in the same way as regular AdSense advertisements.

AdSense for Feeds has remained in its beta state until August 15, 2008, when it became available to all AdSense users.


AdSense for search
A companion to the regular AdSense program, AdSense for search, allows website owners to place Google search boxes on their websites. When a user searches the Internet or the website with the search box, Google shares any advertising revenue it makes from those searches with the website owner. However the publisher is paid only if the advertisements on the page are clicked: AdSense does not pay publishers for mere searches.


AdSense for mobile content
AdSense for mobile content allows publishers to generate earnings from their mobile websites using targeted Google advertisements. Just like AdSense for content, Google matches advertisements to the content of a website — in this case, a mobile website.


XHTML compatibility
As of September 2007, the HTML code for the AdSense search box does not validate as XHTML, and does not follow modern principles of website design because of its use of

non-standard end tags, such as and ,
the attribute checked rather than checked="checked",
presentational attributes other than id, class, or style — for example, bgcolor and align,
a table structure for purely presentational (i.e., non-tabular) purposes,1 and
the font tag.2
1: using a table structure for unintended purposes is strongly recommended against by the W3C[citation needed], but nevertheless does not cause a document to fail validation—there is currently no algorithmic method of determining whether a table is "correctly" used.
2: the font tag is deprecated but does not fail validation in any XHTML standard.

Additionally, the AdSense advertisement units use the JavaScript method document.write(), which does not work correctly when rendered with the application/xhtml+xml MIME type. The units also use the iframe HTML tag, which is not validated correctly with the XHTML 1.0 Strict or XHTML 1.0 Transitional DOCTYPEs.

The terms of the AdSense program forbid its affiliates from modifying the code, thus preventing these participants from having valid XHTML websites.

However, a workaround has been found by creating a separate HTML webpage containing only the AdSense advertisement units, and then importing this page into an XHTML webpage with an object tag.[7] This workaround appears to be accepted by Google.[8]


How AdSense works
The webmaster inserts the AdSense JavaScript code into a webpage.
Each time this page is visited, the JavaScript code creates an IFrame with a src attribute set to the page's URL.
For contextual advertisements, Google's servers use a cache of the page to determine a set of high-value keywords. If keywords have been cached already, advertisements are served for those keywords based on the AdWords bidding system. (More details are described in the AdSense patent.)
For site-targeted advertisements, the advertiser chooses the page(s) on which to display advertisements, and pays based on cost per mille (CPM), or the price advertisers choose to pay for every thousand advertisements displayed.[9] [10]
For referrals, Google adds money to the advertiser's account when visitors either download the referred software or subscribe to the referred service.[11] The referral program will be retired in August 2008.[12]
Search advertisements are added to the list of results after the visitor performs a search.
Because the JavaScript is sent to the Web browser when the page is requested, it is possible for other website owners to copy the JavaScript code into their own webpages. To protect against this type of fraud, AdSense customers can specify the pages on which advertisements should be shown. AdSense then ignores clicks from pages other than those specified.

Abuse
Some webmasters create websites tailored to lure searchers from Google and other engines onto their AdSense website to make money from clicks. These "zombie" websites often contain nothing but a large amount of interconnected, automated content (e.g., a directory with content from the Open Directory Project, or scraper websites relying on RSS feeds for content). Possibly the most popular form of such "AdSense farms" are splogs (spam blogs), which are centered around known high-paying keywords. Many of these websites use content from other websites, such as Wikipedia, to attract visitors. These and related approaches are considered to be search engine spam and can be reported to Google.

A Made for AdSense (MFA) website or webpage has little or no content, but is filled with advertisements so that users have no choice but to click on advertisements. Such pages were tolerated in the past, but due to complaints, Google now disables such accounts.

There have also been reports of Trojan horses engineered to produce counterfeit Google advertisements that are formatted to look like legitimate ones. The Trojan downloads itself onto an unsuspecting computer through a webpage and then replaces the original advertisements with its own set of malicious advertisements.[13]


Criticism
Due to concerns about click fraud, 'Google AdSense' has been criticized by some search engine optimization firms as a large source of what Google calls "invalid clicks", in which one company clicks on a rival's search engine advertisements to drive up the other company's costs.[14] Some publishers that have been blocked by Google complain that little justification or transparency was provided. Webmasters who publish AdSense can receive a life-long ban without justification.[citation needed] Google claims they cannot "disclose any specific details" on fraudulent clicks since it may reveal the nature of their proprietary click-fraud monitoring system.[15]

To help prevent click fraud, AdSense publishers can choose from a number of click-tracking programs[citation needed]. These programs display detailed information about the visitors who click on the AdSense advertisements. Publishers can use this to determine whether or not they have been a victim of click fraud. There are a number of commercial tracking scripts available for purchase.

The payment terms for webmasters have also been criticized.[16] Google withholds payment until an account reaches US$100,[17] but many small content providers[citation needed] require a long time—years in many cases—to build up this much AdSense revenue. These pending payments are recorded on Google's balance sheet as "accrued revenue share".[18] At the close of its 2006 fiscal year, the sum of all these small debts amounted to a little over US$370 million, money that Google is able to invest but effectively belongs to webmasters. However, Google will pay all earned revenue greater than US$10 when an AdSense account is closed.

Google recently came under fire when the official Google AdSense Blog showcased the French video website Imineo.com. This website violated Google's AdSense Program Policies by displaying AdSense alongside sexually explicit material.[19] Typically, websites displaying AdSense have been banned from showing such content.




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Google Management

Co-founders Larry Page, president of Products, and Sergey Brin, president of Technology, brought Google to life in September 1998. Since then, the company has grown to more than 10,000 employees worldwide, with a management team that represents some of the most experienced technology professionals in the industry. Eric Schmidt joined Google as chairman and chief executive officer in 2001.


Board of Directors
Eric Schmidt, Google Inc.
Sergey Brin, Google Inc.
Larry Page, Google Inc.
John Doerr, Kleiner Perkins Caufield & Byers
Ram Shriram, Sherpalo
John Hennessy, Stanford University
Arthur Levinson, Genentech
Paul Otellini, Intel
Shirley M. Tilghman, Princeton University
Ann Mather
Executive Management Group
Eric Schmidt, Chairman of the Board and Chief Executive Officer
Larry Page, Co-Founder & President, Products
Sergey Brin, Co-Founder & President, Technology
Laszlo Bock, Vice President, People Operations
Shona Brown, Senior Vice President, Business Operations
W. M. Coughran, Jr., Senior Vice President, Engineering
David C. Drummond, Senior Vice President, Corporate Development and Chief Legal Officer
Alan Eustace, Senior Vice President, Engineering & Research
Urs Hölzle, Senior Vice President, Operations & Google Fellow
Jeff Huber, Senior Vice President, Engineering
Omid Kordestani, Senior Vice President, Global Sales & Business Development
Patrick Pichette, Senior Vice President & Chief Financial Officer
Jonathan Rosenberg, Senior Vice President, Product Management
Rachel Whetstone, Vice President, Global Communications & Public Affairs
Key executives by function:
Engineering
Vinton G. Cerf, Vice President & Chief Internet Evangelist
Stuart Feldman, Vice President, Engineering
Ben Fried, Chief Information Officer
Vic Gundotra, Vice President, Engineering
Udi Manber, Vice President, Engineering
Nelson Mattos, Vice President, Engineering, EMEA
Shiva Shivakumar, Vice President and Distinguished Entrepreneur
Alfred Spector, VP of Research and Special Initiatives
Benjamin Sloss Treynor, Vice President, Engineering
Jeff Dean, Google Fellow
Sanjay Ghemawat, Google Fellow
Amit Singhal, Google Fellow
Products
Doug Garland, Vice President, Product Management
Bradley Horowitz, Vice President, Product Management
Salar Kamangar, Vice President, Product Management
Marissa Mayer, Vice President, Search Products & User Experience
Sundar Pichai, Vice President, Product Management
Mario Queiroz, Vice President, Product Management, EMEA & Latin America
Lorraine Twohill, Vice President, Marketing, EMEA
Susan Wojcicki, Vice President, Product Management
Sales
Daniel Alegre, Vice President, Asia Pacific Sales & Operations
Tim Armstrong, President, Advertising and Commerce, North America, & Vice President, Google Inc.
Nikesh Arora, President, EMEA Operations & Vice-President, Google UK Ltd.
Sukhinder Singh Cassidy, President, Asia-Pacific & Latin America Operations
David Eun, Vice President, Content Partnerships
David Fischer, Vice President, Global Online Sales & Operations
Dave Girouard, President, Enterprise
John Herlihy, Vice President, Online Sales & Operations, EMEA
Kai-Fu Lee, Vice President, Google Inc.; President, Greater China
Dr. John Liu, Vice President, Sales, Greater China
Norio Murakami, President & General Manager, Google Japan & Vice President, Google Inc.
Penry Price, VP, Advertising Sales, North America
Dennis Woodside, Vice President, UK, Benelux and Ireland
Legal
Kent Walker, Vice President & General Counsel
David Lawee, Vice President, Corporate Development
Megan Smith, Vice President, New Business Development
Finance
Brent Callinicos, Vice President & Treasurer
Francois Delepine, Vice President, Financial Planning and Analysis
Mark Fuchs, Vice President of Finance and Chief Accountant
Julio Pekarovic, Vice President, Global Sales Finance
David Radcliffe, Vice President, Real Estate
Business Operations
Francoise Brougher, Vice President, Business Operations
Google.org
Dr. Larry Brilliant, Executive Director, Google.org



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What is IGoogle?















Windows Live Personalized Experience. It was originally launched in May 2005. Its features include the capability to add web feeds and Google Gadgets (similar to those available on Google Desktop). iGoogle (formerly Google Personalized Homepage and Google IG), a service of Google, is a customizable AJAX-based startpage much like Netvibes, Pageflakes, My Yahoo! and



It was renamed and expanded on April 30, 2007 and is currently available in many localized versions of Google (42 languages, over 70 country domain names, as of October 17, 2007).
Contents

Features

Gadgets
iGoogle supports the use of specially developed "gadgets" to display content on a user's page. The gadgets interact with the user and utilize the Google Gadgets API. Some gadgets developed for Google Desktop can also be used within iGoogle. The Google Gadgets API is public and allows anyone to develop a gadget for any need. [4]
Google also allows all users to create a special gadget that does not require the use of the Gadgets API. The gadgets are designed to be shared with friends and family. The special gadgets must be created using an online wizard and must be of one of the following types:
"Framed Photo" - displays a series of photos,
"GoogleGram" - creation of special daily messages,
"Daily Me" - displays user's current mood and feelings,
"Free Form" - allows the user to input text and an image of their choice,
"YouTube Channel" - displays videos from a YouTube channel,
"Personal List" - allows the user to create a list of items,
"Countdown" - countdown timer

Themes

iGoogle with the Winter Scape theme.
With iGoogle, users can select unique themes for their Google homepages. Some of the themes are animated depending on weather conditions, the time in your area (you provide your location when selecting a theme), and so on. There are also Easter eggs for the themes—for example, in the "Sweet Dreams" theme, a Pi sign made of stars appears at 3:14 a.m. In the "Beach" theme, the Loch Ness Monster appears at 3:14 a.m. These last for only one minute. Other features include skies that lighten or darken throughout the day and the ability to include lady bugs or bubbles that float acoss the screen.
There are many other Easter eggs, which can be found here or by following the instructions here:

Artist themes
On Wednesday, April 9 (2008), Google began offering a choice of themes by professional artists.

[edit] Experimental iGoogle
On July 8, 2008, Google announced the beginning of a testing period for a new version of iGoogle which alters some features, including replacing the tabs with left navigation, adding chat functionality, and a canvas-view gadget for RSS. Users were selected for this test and notified when they logged in by a link to a brief description and further links to forums. On the forums, it was explained that there was no opt-out, as a Control for the test. Further, there was no information on how long the test would continue. Unfortunately, many were unhappy with the new version and the inability to opt-out.






On October 16, 2008, Google announced the release of this new version of iGoogle and retired its older format. The release does not include the persistent chat widget, however, it does include the left navigation in place of tabs as well as a change to widget controls.The stated purpose is to prepare for OpenSocial, with the new canvas view stated as playing an important role in that.
This change opened to horrible review for several reasons: the frame at the left was wasteful of screen real estate, especially on laptops; the gadgets were in a crowded, messy, and cluttered format according to most commenters; the gmail gadget was especially bad, allowing no opt-out for at least one message summary. Further, the homepage would change depending on how the user navigated through the gadgets, commonly leaving a completely-foreign-looking homepage on re-opening the browser. Comments were running at least 30 to 40 to 1 against the change. Google provided no warning of the change, and no way to keep the old iGoogle page if desired.
Reference : http://en.wikipedia.org/wiki/IGoogle
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EXPERT SYSTEM

An expert system is software that attempts to reproduce the performance of one or more human experts, most commonly in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence. A wide variety of methods can be used to simulate the performance of the expert however common to most or all are 1) the creation of a so-called "knowledgebase" which uses some knowledge representation formalism to capture the subject matter experts (SME) knowledge and 2) a process of gathering that knowledge from the SME and codifying it according to the formalism, which is called knowledge engineering. Expert systems may or may not have learning components but a third common element is that once the system is developed it is proven by being placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or a supplement to some information system.
As a premiere application of computing and artificial intelligence, the topic of expert systems has many points of contact with general systems theory, operations research, business process reengineering and various topics in applied mathematics and management science


Examples
Two illustrations of actual expert systems can give an idea of how they work. In one real world case at a chemical refinery a senior employee was about to retire and the company was concerned that the loss of his expertise in managing a fractionating tower would severely impact operations of the plant. A knowledge engineer was assigned to produce an expert system reproducing his expertise saving the company the loss of the valued knowledge asset. Similarly a system called Mycin was developed from the expertise of best diagnosticians of bacterial infections whose performance was found to be as good or better than the average clinician. An early commercial success and illustration of another typical application (a task generally considered overly complex for a human) was an expert system fielded by DEC in the 1980s to quality check the configurations of their computers prior to delivery. The eighties were the time of greatest popularity of expert systems and interest lagged after the onset of the AI Winter.

Overview
The most common form of expert system is a computer program, with a set of rules, that analyzes information (usually supplied by the user of the system) about a specific class of problems, and recommends one or more courses of user action. The expert system may also provide mathematical analysis of the problem(s). The expert system utilizes what appears to be reasoning capabilities to reach conclusions. The reasoning sequence may form a simple decision tree, and might incorporate fuzzy logic.
A related term is wizard. A wizard is an interactive computer program that helps a user solve a problem. Originally the term wizard was used for programs that construct a database search query based on criteria supplied by the user. However, some rule-based expert systems are also called wizards. Other "Wizards" are a sequence of online forms that guide users through a series of choices, such as the ones which manage the installation of new software on computers, and these are not expert systems.

Prominent expert systems and languages
ART - An early general-purpose programming language used in the development of expert systems
CADUCEUS (expert system) - Blood-borne infectious bacteria
CLIPS - Programming language used in the development of expert systems
Drools - An open source offering from JBOSS labs
Dendral - Analysis of mass spectra
Dipmeter Advisor - Analysis of data gathered during oil exploration
Jess - Java Expert System Shell. A CLIPS engine implemented in Java used in the development of expert systems
KnowledgeBench – expert system for building new product development applications
Knowledge Engineering Environment - e-mycin derivative that implemented an ATMS
MQL 4 - MetaQuotes Language 4, a customized language for financial strategy programming
Mycin - Diagnose infectious blood diseases and recommend antibiotics (by Stanford University)
NETeXPERT - A mission-critical Operational Support Systems framework with rules, policies, object modeling, and adapters for Network Operations Center automation
NEXPERT Object - An early general-purpose commercial backwards-chaining inference engine used in the development of expert systems
Prolog - Programming language used in the development of expert systems
Forth - Programming language used in the development of expert systems
R1/Xcon - Order processing
SHINE Real-time Expert System - Spacecraft Health INference Engine
STD Wizard - Expert system for recommending medical screening tests
PyKe - Pyke is a knowledge-based inference engine (expert system)

The study of expert systems

Knowledge representation
Main article: Knowledge representation
Knowledge representation is an issue that arises in both cognitive science and artificial intelligence. In cognitive science, it is concerned with how people store and process information. In artificial intelligence (AI) the primary aim is to store knowledge so that programs can process it and achieve the verisimilitude of human intelligence. AI researchers have borrowed representation theories from cognitive science. Thus there are representation techniques such as frames, rules and semantic networks which have originated from theories of human information processing. Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge representation is to represent knowledge in a manner as to facilitate inferencing i.e. drawing conclusions from knowledge.

Knowledge engineer
Main article: Knowledge engineers
Knowledge engineers are concerned with the representation chosen for the expert's knowledge declarations and with the inference engine used to process that knowledge. He / she can use the knowledge acquisition component of the expert system to input the several characteristics known to be appropriate to a good inference technique, including:
A good inference technique is independent of the problem domain.
In order to realize the benefits of explanation, knowledge transparency, and reusability of the programs in a new problem domain, the inference engine must not contain domain specific expertise.
Inference techniques may be specific to a particular task, such as diagnosis of hardware configuration. Other techniques may be committed only to a particular processing technique.
Inference techniques are always specific to the knowledge structures.
Successful examples of rule processing techniques are forward chaining and backward chaining.

Expert systems topics

Chaining
There are two main methods of reasoning when using inference rules: backward chaining and forward chaining.
Forward chaining starts with the data available and uses the inference rules to conclude more data until a desired goal is reached. An inference engine using forward chaining searches the inference rules until it finds one in which the if-clause is known to be true. It then concludes the then-clause and adds this information to its data. It would continue to do this until a goal is reached. Because the data available determines which inference rules are used, this method is also called data driven.
Backward chaining starts with a list of goals and works backwards to see if there is data which will allow it to conclude any of these goals. An inference engine using backward chaining would search the inference rules until it finds one which has a then-clause that matches a desired goal. If the if-clause of that inference rule is not known to be true, then it is added to the list of goals. For example, suppose a rulebase contains two rules:
(1) If Fritz is green then Fritz is a frog.
(2) If Fritz is a frog then Fritz hops.
Suppose a goal is to conclude that Fritz hops.The rulebase would be searched and rule (2) would be selected because its conclusion (the then clause) matches the goal. It is not known that Fritz is a frog, so this "if" statement is added to the goal list. The rulebase is again searched and this time rule (1) is selected because its then clause matches the new goal just added to the list. This time, the if-clause (Fritz is green) is known to be true and the goal that Fritz hops is concluded. Because the list of goals determines which rules are selected and used, this method is called goal driven.

Certainty factors
One advantage of expert systems over traditional methods of programming is that they allow the use of "confidences" (or "certainty factors"). When a human reasons he does not always conclude things with 100% confidence. He might say, "If Fritz is green, then he is probably a frog" (after all, he might be a chameleon). This type of reasoning can be imitated by using numeric values called confidences. For example, if it is known that Fritz is green, it might be concluded with 0.85 confidence that he is a frog; or, if it is known that he is a frog, it might be concluded with 0.95 confidence that he hops. These numbers are similar in nature to probabilities, but they are not the same. They are meant to imitate the confidences humans use in reasoning rather than to follow the mathematical definitions used in calculating probabilities.

Expert system architecture
The following general points about expert systems and their architecture have been illustrated.
1. The sequence of steps taken to reach a conclusion is dynamically synthesized with each new case. It is not explicitly programmed when the system is built.
2. Expert systems can process multiple values for any problem parameter. This permits more than one line of reasoning to be pursued and the results of incomplete (not fully determined) reasoning to be presented.
3. Problem solving is accomplished by applying specific knowledge rather than specific technique. This is a key idea in expert systems technology. It reflects the belief that human experts do not process their knowledge differently from others, but they do possess different knowledge. With this philosophy, when one finds that their expert system does not produce the desired results, work begins to expand the knowledge base, not to re-program the procedures.
There are various expert systems in which a rulebase and an inference engine cooperate to simulate the reasoning process that a human expert pursues in analyzing a problem and arriving at a conclusion. In these systems, in order to simulate the human reasoning process, a vast amount of knowledge needed to be stored in the knowledge base. Generally, the knowledge base of such an expert system consisted of a relatively large number of "if then" type of statements that were interrelated in a manner that, in theory at least, resembled the sequence of mental steps that were involved in the human reasoning process.
Because of the need for large storage capacities and related programs to store the rulebase, most expert systems have, in the past, been run only on large information handling systems. Recently, the storage capacity of personal computers has increased to a point where it is becoming possible to consider running some types of simple expert systems on personal computers.
In some applications of expert systems, the nature of the application and the amount of stored information necessary to simulate the human reasoning process for that application is just too vast to store in the active memory of a computer. In other applications of expert systems, the nature of the application is such that not all of the information is always needed in the reasoning process. An example of this latter type application would be the use of an expert system to diagnose a data processing system comprising many separate components, some of which are optional. When that type of expert system employs a single integrated rulebase to diagnose the minimum system configuration of the data processing system, much of the rulebase is not required since many of the components which are optional units of the system will not be present in the system. Nevertheless, earlier expert systems require the entire rulebase to be stored since all the rules were, in effect, chained or linked together by the structure of the rulebase.
When the rulebase is segmented, preferably into contextual segments or units, it is then possible to eliminate portions of the Rulebase containing data or knowledge that is not needed in a particular application. The segmenting of the rulebase also allows the expert system to be run with systems or on systems having much smaller memory capacities than was possible with earlier arrangements since each segment of the rulebase can be paged into and out of the system as needed. The segmenting of the rulebase into contextual segments requires that the expert system manage various intersegment relationships as segments are paged into and out of memory during execution of the program. Since the system permits a rulebase segment to be called and executed at any time during the processing of the first rulebase, provision must be made to store the data that has been accumulated up to that point so that at some time later in the process, when the system returns to the first segment, it can proceed from the last point or rule node that was processed. Also, provision must be made so that data that has been collected by the system up to that point can be passed to the second segment of the rulebase after it has been paged into the system and data collected during the processing of the second segment can be passed to the first segment when the system returns to complete processing that segment.
The user interface and the procedure interface are two important functions in the information collection process.

End user
The end-user usually sees an expert system through an interactive dialog, an example of which follows:
Q. Do you know which restaurant you want to go to?
A. No
Q. Is there any kind of food you would particularly like?
A. No
Q. Do you like spicy food?
A. No
Q. Do you usually drink wine with meals?
A. Yes
Q. When you drink wine, is it French wine?
A. Why
As can be seen from this dialog, the system is leading the user through a set of questions, the purpose of which is to determine a suitable set of restaurants to recommend. This dialog begins with the system asking if the user already knows the restaurant choice (a common feature of expert systems) and immediately illustrates a characteristic of expert systems; users may choose not to respond to any question. In expert systems, dialogs are not pre-planned. There is no fixed control structure. Dialogs are synthesized from the current information and the contents of the knowledge base. Because of this, not being able to supply the answer to a particular question does not stop the consultation.

Explanation system
Another major distinction between expert systems and traditional systems is illustrated by the following answer given by the system when the user answers a question with another question, "Why", as occurred in the above example. The answer is:
A. I am trying to determine the type of restaurant to suggest. So far Chinese is not a likely choice. It is possible that French is a likely choice. I know that if the diner is a wine drinker, and the preferred wine is French, then there is strong evidence that the restaurant choice should include French.
It is very difficult to implement a general explanation system (answering questions like "Why" and "How") in a traditional computer program. An expert system can generate an explanation by retracing the steps of its reasoning. The response of the expert system to the question WHY is an exposure of the underlying knowledge structure. It is a rule; a set of antecedent conditions which, if true, allow the assertion of a consequent. The rule references values, and tests them against various constraints or asserts constraints onto them. This, in fact, is a significant part of the knowledge structure. There are values, which may be associated with some organizing entity. For example, the individual diner is an entity with various attributes (values) including whether they drink wine and the kind of wine. There are also rules, which associate the currently known values of some attributes with assertions that can be made about other attributes. It is the orderly processing of these rules that dictates the dialog itself.

Expert systems versus problem-solving systems
The principal distinction between expert systems and traditional problem solving programs is the way in which the problem related expertise is coded. In traditional applications, problem expertise is encoded in both program and data structures.
In the expert system approach all of the problem related expertise is encoded in data structures only; none is in programs. This organization has several benefits.
An example may help contrast the traditional problem solving program with the expert system approach. The example is the problem of tax advice. In the traditional approach data structures describe the taxpayer and tax tables, and a program in which there are statements representing an expert tax consultant's knowledge, such as statements which relate information about the taxpayer to tax table choices. It is this representation of the tax expert's knowledge that is difficult for the tax expert to understand or modify.
In the expert system approach, the information about taxpayers and tax computations is again found in data structures, but now the knowledge describing the relationships between them is encoded in data structures as well. The programs of an expert system are independent of the problem domain (taxes) and serve to process the data structures without regard to the nature of the problem area they describe. For example, there are programs to acquire the described data values through user interaction, programs to represent and process special organizations of description, and programs to process the declarations that represent semantic relationships within the problem domain and an algorithm to control the processing sequence and focus.
The general architecture of an expert system involves two principal components: a problem dependent set of data declarations called the knowledge base or rule base, and a problem independent (although highly data structure dependent) program which is called the inference engine.

Individuals involved with expert systems
There are generally three individuals having an interaction with expert systems. Primary among these is the end-user; the individual who uses the system for its problem solving assistance. In the building and maintenance of the system there are two other roles: the problem domain expert who builds and supplies the knowledge base providing the domain expertise, and a knowledge engineer who assists the experts in determining the representation of their knowledge, enters this knowledge into an explanation module and who defines the inference technique required to obtain useful problem solving activity. Usually, the knowledge engineer will represent the problem solving activity in the form of rules which is referred to as a rule-based expert system. When these rules are created from the domain expertise, the knowledge base stores the rules of the expert system.

Inference rule
An understanding of the "inference rule" concept is important to understand expert systems. An inference rule is a statement that has two parts, an if-clause and a then-clause. This rule is what gives expert systems the ability to find solutions to diagnostic and prescriptive problems. An example of an inference rule is:
If the restaurant choice includes French, and the occasion is romantic,
Then the restaurant choice is definitely Paul Bocuse.
An expert system's rulebase is made up of many such inference rules. They are entered as separate rules and it is the inference engine that uses them together to draw conclusions. Because each rule is a unit, rules may be deleted or added without affecting other rules (though it should affect which conclusions are reached). One advantage of inference rules over traditional programming is that inference rules use reasoning which more closely resemble human reasoning.
Thus, when a conclusion is drawn, it is possible to understand how this conclusion was reached. Furthermore, because the expert system uses knowledge in a form similar to the expert, it may be easier to retrieve this information from the expert.

Procedure node interface
The function of the procedure node interface is to receive information from the procedures coordinator and create the appropriate procedure call. The ability to call a procedure and receive information from that procedure can be viewed as simply a generalization of input from the external world. While in some earlier expert systems external information has been obtained, that information was obtained only in a predetermined manner so only certain information could actually be acquired. This expert system, disclosed in the cross-referenced application, through the knowledge base, is permitted to invoke any procedure allowed on its host system. This makes the expert system useful in a much wider class of knowledge domains than if it had no external access or only limited external access.
In the area of machine diagnostics using expert systems, particularly self-diagnostic applications, it is not possible to conclude the current state of "health" of a machine without some information. The best source of information is the machine itself, for it contains much detailed information that could not reasonably be provided by the operator.
The knowledge that is represented in the system appears in the rulebase. In the rulebase described in the cross-referenced applications, there are basically four different types of objects, with associated information present.
Classes--these are questions asked to the user.
Parameters--a parameter is a place holder for a character string which may be a variable that can be inserted into a class question at the point in the question where the parameter is positioned.
Procedures--these are definitions of calls to external procedures.
Rule Nodes--The inferencing in the system is done by a tree structure which indicates the rules or logic which mimics human reasoning. The nodes of these trees are called rule nodes. There are several different types of rule nodes.
The rulebase comprises a forest of many trees. The top node of the tree is called the goal node, in that it contains the conclusion. Each tree in the forest has a different goal node. The leaves of the tree are also referred to as rule nodes, or one of the types of rule nodes. A leaf may be an evidence node, an external node, or a reference node.
An evidence node functions to obtain information from the operator by asking a specific question. In responding to a question presented by an evidence node, the operator is generally instructed to answer "yes" or "no" represented by numeric values 1 and 0 or provide a value of between 0 and 1, represented by a "maybe."
Questions which require a response from the operator other than yes or no or a value between 0 and 1 are handled in a different manner.
A leaf that is an external node indicates that data will be used which was obtained from a procedure call.
A reference node functions to refer to another tree or subtree.
A tree may also contain intermediate or minor nodes between the goal node and the leaf node. An intermediate node can represent logical operations like And or Or.
The inference logic has two functions. It selects a tree to trace and then it traces that tree. Once a tree has been selected, that tree is traced, depth-first, left to right.
The word "tracing" refers to the action the system takes as it traverses the tree, asking classes (questions), calling procedures, and calculating confidences as it proceeds.
As explained in the cross-referenced applications, the selection of a tree depends on the ordering of the trees. The original ordering of the trees is the order in which they appear in the rulebase. This order can be changed, however, by assigning an evidence node an attribute "initial" which is described in detail in these applications. The first action taken is to obtain values for all evidence nodes which have been assigned an "initial" attribute. Using only the answers to these initial evidences, the rules are ordered so that the most likely to succeed is evaluated first. The trees can be further re-ordered since they are constantly being updated as a selected tree is being traced.
It has been found that the type of information that is solicited by the system from the user by means of questions or classes should be tailored to the level of knowledge of the user. In many applications, the group of prospective uses is nicely defined and the knowledge level can be estimated so that the questions can be presented at a level which corresponds generally to the average user. However, in other applications, knowledge of the specific domain of the expert system might vary considerably among the group of prospective users.
One application where this is particularly true involves the use of an expert system, operating in a self-diagnostic mode on a personal computer to assist the operator of the personal computer to diagnose the cause of a fault or error in either the hardware or software. In general, asking the operator for information is the most straightforward way for the expert system to gather information assuming, of course, that the information is or should be within the operator's understanding. For example, in diagnosing a personal computer, the expert system must know the major functional components of the system. It could ask the operator, for instance, if the display is a monochrome or color display. The operator should, in all probability, be able to provide the correct answer 100% of the time. The expert system could, on the other hand, cause a test unit to be run to determine the type of display. The accuracy of the data collected by either approach in this instance probably would not be that different so the knowledge engineer could employ either approach without affecting the accuracy of the diagnosis. However, in many instances, because of the nature of the information being solicited, it is better to obtain the information from the system rather than asking the operator, because the accuracy of the data supplied by the operator is so low that the system could not effectively process it to a meaningful conclusion.
In many situations the information is already in the system, in a form of which permits the correct answer to a question to be obtained through a process of inductive or deductive reasoning. The data previously collected by the system could be answers provided by the user to less complex questions that were asked for a different reason or results returned from test units that were previously run.

User interface
The function of the user interface is to present questions and information to the user and supply the user's responses to the inference engine.
Any values entered by the user must be received and interpreted by the user interface. Some responses are restricted to a set of possible legal answers, others are not. The user interface checks all responses to insure that they are of the correct data type. Any responses that are restricted to a legal set of answers are compared against these legal answers. Whenever the user enters an illegal answer, the user interface informs the user that his answer was invalid and prompts him to correct it.

Application of expert systems
Expert systems are designed and created to facilitate tasks in the fields of accounting, medicine, process control, financial service, production, human resources etc. Indeed, the foundation of a successful expert system depends on a series of technical procedures and development that may be designed by certain technicians and related experts.
A good example of application of expert systems in banking area is expert systems for mortgages. Loan departments are interested in expert systems for mortgages because of the growing cost of labour which makes the handling and acceptance of relatively small loans less profitable. They also see in the application of expert systems a possibility for standardised, efficient handling of mortgage loan, and appreciate that for the acceptance of mortgages there are hard and fast rules which do not always exist with other types of loans.
While expert systems have distinguished themselves in AI research in finding practical application, their application has been limited. Expert systems are notoriously narrow in their domain of knowledge—as an amusing example, a researcher used the "skin disease" expert system to diagnose his rustbucket car as likely to have developed measles—and the systems were thus prone to making errors that humans would easily spot. Additionally, once some of the mystique had worn off, most programmers realized that simple expert systems were essentially just slightly more elaborate versions of the decision logic they had already been using. Therefore, some of the techniques of expert systems can now be found in most complex programs without any fuss about them.
An example, and a good demonstration of the limitations of, an expert system used by many people is the Microsoft Windows operating system troubleshooting software located in the "help" section in the taskbar menu. Obtaining expert / technical operating system support is often difficult for individuals not closely involved with the development of the operating system. Microsoft has designed their expert system to provide solutions, advice, and suggestions to common errors encountered throughout using the operating systems.
Another 1970s and 1980s application of expert systems — which we today would simply call AI — was in computer games. For example, the computer baseball games Earl Weaver Baseball and Tony La Russa Baseball each had highly detailed simulations of the game strategies of those two baseball managers. When a human played the game against the computer, the computer queried the Earl Weaver or Tony La Russa Expert System for a decision on what strategy to follow. Even those choices where some randomness was part of the natural system (such as when to throw a surprise pitch-out to try to trick a runner trying to steal a base) were decided based on probabilities supplied by Weaver or La Russa. Today we would simply say that "the game's AI provided the opposing manager's strategy."

Advantages and disadvantages
Advantages:
Provides consistent answers for repetitive decisions, processes and tasks
Holds and maintains significant levels of information
Encourages organizations to clarify the logic of their decision-making
Never "forgets" to ask a question, as a human might
Disadvantages:
Lacks common sense needed in some decision making
Cannot make creative responses as human expert would in unusual circumstances
Domain experts not always able to explain their logic and reasoning
Errors may occur in the knowledge base, and lead to wrong decisions
Cannot adapt to changing environments, unless knowledge base is changed

Types of problems solved by expert systems
Expert systems are most valuable to organizations that have a high-level of know-how experience and expertise that cannot be easily transferred to other members. They are designed to carry the intelligence and information found in the intellect of experts and provide this knowledge to other members of the organization for problem-solving purposes.
Typically, the problems to be solved are of the sort that would normally be tackled by a medical or other professional. Real experts in the problem domain (which will typically be very narrow, for instance "diagnosing skin conditions in human teenagers") are asked to provide "rules of thumb" on how they evaluate the problems, either explicitly with the aid of experienced systems developers, or sometimes implicitly, by getting such experts to evaluate test cases and using computer programs to examine the test data and (in a strictly limited manner) derive rules from that. Generally, expert systems are used for problems for which there is no single "correct" solution which can be encoded in a conventional algorithm — one would not write an expert system to find shortest paths through graphs, or sort data, as there are simply easier ways to do these tasks.
Simple systems use simple true/false logic to evaluate data. more sophisticated systems are capable of performing at least some evaluation, taking into account real-world uncertainties, using such methods as fuzzy logic. Such sophistication is difficult to develop and still highly imperfect.


Reference : http://en.wikipedia.org/wiki/Expert_system
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