InfoWorld: Top News

Saturday, July 21, 2007

iPhone



The iPhone is a multimedia and Internet-enabled quad-band GSM EDGE-supported mobile phone designed and sold by Apple Inc. The iPhone's functions include those of a camera phone and a multimedia player. It also offers Internet services including e-mail, text messaging, web browsing, Visual Voicemail, and local Wi-Fi connectivity. User input is accomplished via a multi-touch screen with virtual keyboard and buttons. Apple has filed more than 200 patents related to the technology behind the iPhone.[1]
The iPhone is available from Apple Retail Stores, the Apple Online Store, and from AT&T Mobility, formerly Cingular Wireless, for a contracted price of US$499 for the 4 GB model and US$599 for the 8 GB model.


Features
Apple has released a video explaining many of iPhone's features through a series of demonstrations.[2]

Touch screen
The 3.5 in liquid crystal display (320×480 px at 160 ppi) HVGA touch screen topped with optical-quality glass[3] is specifically created for use with a finger, or multiple fingers for multi-touch sensing. Because the screen is a capacitive touch screen, no stylus is needed, nor can one be used.[4][5] The requirement for bare skin to be used has caused concerns for users in areas with winter climates; gloves worn would then have to be removed to use the touchpad.[6][7]
For text input, the device implements a virtual keyboard on the touchscreen. It has automatic spell checking, predictive word capabilities, and a dynamic dictionary that learns new words. The predictive word capabilities have been integrated with the dynamic virtual keyboard so that users will not have to be extremely accurate when typing — i.e. touching the edges of the desired letter or nearby letters on the keyboard will be predictively corrected when possible. The keys are somewhat larger and spaced further apart when in landscape mode (currently, only using Safari). Reviewers, writers and analysts have pointed out several areas in which the iPhone falls short. The virtual keyboard has been considered its chief weakness and a risk for Apple.[8] The New York Times' David Pogue and Wall Street Journal's Walt Mossberg, who both tested the iPhone for two weeks, found learning to use it initially difficult, although eventually usable, with Pogue stating use was "frustrating" and "text entry is not the iPhone’s strong suit" but Mossberg considered the keyboard a "nonissue." Both found that the typo-correcting feature of the iPhone was the key to using the virtual keyboard successfully.[9][10]
The iPhone varies from common desktop interfaces by using a direct manipulation model of scrolling. Where a typical desktop GUI achieves scrolling by using a scroll-arrow to push a view-window down and thus the content itself up (or the reverse, clicking up to move content down), the iPhone interface enables the user to move the content itself up or down by a touch-drag-lift motion of the finger, much as one would slide a playing card across a table. Additionally, the speed desired for scrolling is computed based on the speed and acceleration with which the drag motion is performed.
Scrolling through a long list works as if the list is pasted on the surface of a wheel: the wheel can be "spun" by sliding a finger over the display. After the finger is lifted from the display the wheel continues to "spin" for a short moment before coasting down. In this way, the iPhone seems to simulate the physics of a real object, which, it is thought, should give a natural feel to the whole process.
The user interface also features other visual effects, such as horizontally sliding sub-selections and co-selections from right and left, vertically sliding system menus from the bottom (e.g. favorites, keyboard), and menus and widgets that turn around to allow settings to be configured on their back sides.
The photo album and web page magnifications are examples of multi-touch sensing. It is possible to zoom in and out of objects such as web pages and photos by respectively "unpinching" and "pinching" them, that is, placing two fingers (usually thumb and forefinger) on the screen and moving them farther apart or closer together as if stretching or squeezing the image. This scaling is done uniformly and proportionally based on the image in question so there is no distortion of the image itself, as would be the case if the image were actually stretched or squeezed.
One disadvantage of multi-touch with regards to AJAX web sites is that there is no mechanism for 'hovering' over a UI element. That is, there is no separate paradigm for indicating interest or focus on a portion of a web control other than clicking on it.

Other inputs
The display responds to three sensors: a proximity sensor that shuts off the display and touchscreen when the iPhone is brought near the face to save battery power and to prevent spurious inputs from the user's face and ears, an ambient light sensor that adjusts the display brightness which in turn saves battery power, and a 3-axis accelerometer,[11] which senses the orientation of the phone and changes the screen accordingly. Web browsing and music playing support three orientations, while videos play in only one widescreen orientation.
A single "home" hardware button below the display brings up the main menu. Subselections are made via the touchscreen. The iPhone utilizes a full-paged display, with context-specific submenus at the top and/or bottom of each page, sometimes depending on screen orientation. Detail pages display the equivalent of a "Back" button to go up one menu.
The iPhone has three physical switches on its sides: sleep / wake, volume up / down, ringer on / off. All other multimedia and phone operations are done via the touch screen.

Phone
The iPhone allows conferencing, call holding, call merging, caller ID, and integration with other cellular network features and iPhone functions. For example, a playing song fades out when the user receives a call. Once the call is ended the music fades back in.
The iPhone includes a Visual Voicemail feature in conjunction with AT&T which allows users to view a list of current voicemail messages on-screen, without having to call into their voicemail. Unlike most other systems, messages can be listened to in a non-chronological order, by choosing messages from an on-screen list. AT&T modified their voicemail infrastructure to accommodate this new feature designed by Apple.
SMS messages are presented chronologically in a mailbox format similar to Mail, which places all text from recipients together with replies. Text messages are displayed in speech bubbles (similar to iChat) under each recipient's name.

Camera
The iPhone features a built in 2.0 megapixel camera located on the back for still digital photos. It also includes software that allows the user to upload, view, and e-mail photos. The user zooms in and out of photos by "unpinching" and "pinching" them through the multi-touch interface. The software interacts with iPhoto on the Mac.

Multimedia
The layout of the music library differs from previous iPods, with the sections divided more clearly alphabetically, and with a larger font. Similar to previous iPods, the iPhone can sort its media library by songs, artists, albums, videos, playlists, genres, composers, podcasts, audiobooks, and compilations. The Cover Flow, like that on iTunes, shows the different album covers in a scroll-through photo library. Scrolling is achieved by swiping a finger across the screen.
Like the fifth generation iPods introduced in 2005, the iPhone can play video, allowing users to watch TV shows and films. Unlike other image-related content, video on the iPhone plays only in the landscape orientation, when the phone is turned sideways. A two-fingered tap is used to switch between the video's true wide-screen aspect ratio (with black bars on the top and bottom of the screen) and a zoomed mode (to fill the iPhone's screen).

Web connectivity

Wikipedia on the iPhone's Safari web browser.
The iPhone has built-in Wi-Fi, with which it is able to access the World Wide Web (through a wireless network) via a modified version of the Safari web browser. The iPhone is also able to connect to the web through AT&T's EDGE network, but is not able to utilize AT&T's 3G/HSDPA network; Steve Jobs mentioned at the Keynote presentation that 3G support would be a future feature of a new version.[5] The use of the EDGE network instead of 3G has been criticized by analysts. When the user is not in a Wi-Fi hot spot, the iPhone's network connection will use the older EDGE network, which, before the launch, reviewers found that the EDGE network was "excruciatingly slow," with the iPhone taking as long as 100 seconds to download the Yahoo! home page for the first time.[9] Immediately before the launch the observed speed of the network increased to almost 200 kbit/s.[12] This is probably due to the new "Fine EDGE" upgrades AT&T has been making to their network prior to the launch.[13]
The web browser displays full web pages as opposed to simplified pages as on most non-smartphones. The iPhone does not support Flash or Java technology.[8][14] Web pages may be viewed in portrait or landscape mode and support automatic zooming by "pinching" or double-tapping images or text. The iPhone also has Bluetooth 2.x+EDR built in. It works with wireless earpieces that use Bluetooth 2.0 technology.
An agreement between Apple and Google provides for access to a specially modified version of Google Maps — in map, local list, or satellite form, optimized for the iPhone, which also provides optional real-time traffic information. During the product's announcement, Jobs demonstrated this feature by searching for nearby Starbucks locations and then placing a prank call to one with a single tap.[15][16]

E-mail
The iPhone also features an HTML e-mail program, which enables the user to embed photos in an e-mail message. PDF, Microsoft Word, and Microsoft Excel attachments to mail messages can be viewed on the phone.[17] Yahoo! is currently the e-mail provider that is offering a free Push-IMAP e-mail service similar to that on a BlackBerry; IMAP and POP3 mail standards are also supported, including Microsoft Exchange.[18] will sync e-mail account settings over from Apple's own Mail application, Microsoft Outlook and Microsoft Entourage, or can be manually configured using the device's Settings tool. With the correct settings, the e-mail program can check many IMAP or POP3-enabled web based accounts such as Gmail, .Mac mail, and AOL.[19]

OS X
Apple has confirmed that an optimized version of the Mac OS X operating system (without unnecessary components) runs on the iPhone, although differences between the operating system (OS X) running on Macs and the iPhone have not been officially explained. As iPhone's CPU is an ARM processor, the version of OS X that runs on iPhone differs from the desktop version in that it is written for the ARM instruction set architecture (ISA) instead of the x86 and PowerPC ISAs that the Mac version of OS X is written for.
The operating system takes up about 700 MB of the device's total 4 or 8 GB storage.[9] It will be capable of supporting bundled and future applications from Apple.
Apple intends to offer a smooth method for updating the iPhone's operating system, in a similar fashion to the way that Mac OS X and iPods are updated, and touts this as an advantage compared to other cell phones.[20]
Widgets, similar to the ones available in Mac OS X v10.4's Dashboard, are included on the iPhone. They include Stocks and Weather widgets.
The iPhone's version of OS X includes the software component "Core Animation" which is responsible for the smooth animations used in its user interface. Core Animation has not yet been released for Macs, but will be part of Mac OS X v10.5.
The build of OS X on at least one iPhone is "OS X 1.0 (1A543a)", as seen in a crash log for the MobileMail application.[21][22] The application apparently runs as the superuser.

Applications
The phone has several applications located at iPhone's "Home" Screen, including YouTube. It will stream the videos over Wi-Fi and/or EDGE after encoding them using QuickTime's H.264 codec, to which YouTube has converted about 10,000 videos. They are expected to convert the entire catalog by Fall 2007. As a result, the YouTube application on iPhone can currently only view a certain selection of videos from the site.[citation needed]
At WWDC 2007 on June 11, 2007 Apple Inc. announced that the iPhone will support third party "applications" via the Safari web browser, that share the look and feel of the iPhone interface. The applications must be created in Ajax or JavaScript to maintain device security.[23] The iPhone cannot install full programs from anyone but Apple.[24] Steve Jobs has referenced full programs being created by parties other than Apple[25][26]
Analysts also claim that iPhone lacks any type of firewall, which some experts claim is posing a data security risk.[27] It is not confirmed by Apple or by independent analysts that used the actual device for tests that it doesn't have a firewall. Daniel Eran writes: "Dulaney doesn't know if the iPhone has a firewall, has no reason to suggest that its installation of OS X wouldn't offer a firewall, and offers no reasons why a mobile device would need a firewall anyway."[28]

Battery
The iPhone features a built-in rechargeable battery that is not intended to be user-replaceable, similar to existing iPods. Once the battery reaches the end of its life time the phone will need to be returned to Apple and replaced for a fee.[9] The cost of replacing the battery is US$79 and US$6.95 for shipping.[29] The battery is stated to be capable of providing up to seven hours of video, six hours of web browsing, or eight hours of talk time (depending on configuration). The battery life for music playing is stated to be 24 hours.[3] The battery also allows for up to 250 hours of standby time. Apple's site [1] says that the battery life "is designed to retain up to 80% of its original capacity after 400 full charge and discharge cycles.
The Foundation for Taxpayer and Consumer Rights, a consumer advocate group, has sent a complaint to Apple and AT&T over the fee that consumers have to pay to get the battery replaced. In addition, the scheme and pricing was not made known to buyers until after the product was launched.[30]

Other
There are new headphones which are similar to those of current iPods, but which incorporate a microphone. Calls can be answered and ended by squeezing a bud, toggling the microphone. The 3.5 mm TRS connector for the headphones is located on the top left corner (as seen from front upright). Wireless earpieces that use Bluetooth technology to communicate with the iPhone are sold separately. The headphone jack is recessed and as a result many standard stereo headphone jacks require an adapter to work correctly, although some users have successfully modified existing jacks by cutting away several millimeters of rubber at the base of the jack to allow them to fit.
The loudspeaker is used both for handsfree operations and media playback.
The SIM card is located in a slot at the top of the device,[2] and the device is activated through iTunes.[31]
iPhone lacks a number of other handheld features that have not already been mentioned, including voice dialing, voice recording, instant messaging, memory card slot, MMS, A2DP (allowing for stereo sound to be sent to an audio device by Bluetooth), common Bluetooth file transfer, GPS capability, text copy and paste, native games, and support for MP3 files as ringtones.[32][33][34]

Platform support
The iPhone is compatible with Mac OS X version 10.4.10 or later, and Windows XP or Vista. For each, the user must download the latest version of iTunes, iTunes 7.3. The iPhone is not compatible with any 64 bit version of Windows such as Windows XP x64 or any 64 bit edition of Windows Vista.[35]

Pricing and availability
The initial U.S. release is offered in two configurations with two different prices: a 4 GB model for US$499 and an 8 GB model for US$599. In a deal concluded through secretive discussions which began in February 2005,[36] AT&T Mobility is the exclusive carrier of the iPhone in the United States and will remain so until 2009 or later.[37][38] The iPhone may be purchased with a two-year service plan with AT&T[39] with plans ranging from US$59.99 to US$219.99 per month,[40] or pre-paid month to month at a slightly higher rate.[41]
Apple received FCC approval for the iPhone on May 17, 2007.[42] Jobs announced that the iPhone will first be available in late June 2007 in the U.S.,[43] during the fourth quarter 2007 in Europe (O2 have reportedly been awarded the contract for the UK), September 2007 in South Africa[44], and in 2008 for Asia, Mexico, and probably the rest of the Americas.[45] Also, Mac OS X v10.5, which was originally planned for release on June 11 at the Worldwide Developers Conference, is now delayed until October 2007, because engineers from the Mac OS X team were diverted to work on the iPhone.[46] New commercials for the iPhone began airing on television starting on June 3, confirming a release date of June 29, 2007.
Apple also announced that its goal is to capture 1% of the global mobile phone market, which would be approximately 10 million units being sold in the first full calendar year of iPhone availability. For comparison, Jobs announced that the Apple iPod commands 62% of the U.S. market share for MP3 players.[47]


Specifications
The specifications as listed on Apple's website are:[48]
Screen size: 8.9 cm (3.5 in)
Screen resolution: 320×480 pixels at 160 ppi
Input method: Multi-touch screen interface (the "Home" button is the iPhone's only physical front panel button)
Operating System: Darwin OS X
Storage: 4 or 8 GB Flash memory
Quad band GSM (GSM 850, GSM 900, GSM 1800, GSM 1900)
Wi-Fi (802.11b/802.11g), EDGE and Bluetooth 2.0 with EDR
2 megapixel camera
Built-in rechargeable, non-removable battery with up to 8 hours of talk, 6 hours of internet use, 7 hours of video playback, and up to 24 hours of audio playback, lasting over 250 hours on standby.[3]
Size: 115×61×11.6 mm (4.5×2.4×0.46 in)
Weight: 135 g (4.8 oz)
Digital SAR (Specific Absorption Rate) of 0.974 W/kg[49][50]
An analysis of the iPhone's firmware has revealed that the main Samsung chip (designated S5L8900) contains an ARM1176jzf processor, together with a PowerVR MBX 3D graphics co-processor.[51]
Package contents
iPhone
Stereo earphones with in-line microphone
Dock
Dock connector to USB cable
USB power adapter
Documentation (includes 2 white Apple stickers)
Cleaning/polishing cloth
(A separate dock is also available which charges both the iPhone and an Apple Bluetooth headset.)




Wednesday, July 18, 2007

Decision support system

Definitions
Because there are many approaches to decision-making and because of the wide range of domains in which decisions are made, the concept of decision support system (DSS) is very broad. A DSS can take many different forms. In general, we can say that a DSS is a computerized system for helping make decisions. A decision is a choice between alternatives based on estimates of the values of those alternatives. Supporting a decision means helping people working alone or in a group gather intelligence, generate alternatives and make choices. Supporting the choice making process involves supporting the estimation, the evaluation and/or the comparison of alternatives. In practice, references to DSS are usually references to computer applications that perform such a supporting role.[1]
The term decision support system has been used in many different ways (Alter 1980, Power, 2002) and has been defined in various ways depending upon the author's point of view [2]. Finlay [3] and others define a DSS rather broadly as "a computer-based system that aids the process of decision making." Turban [4] defines it more specifically as "an interactive, flexible, and adaptable computer-based information system, especially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker's own insights."
Other definitions fall between these two extremes. For Little [5], a DSS is a "model-based set of procedures for processing data and judgments to assist a manager in his decision-making." For Keen and Scott Morton [6], a DSS couples the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions ("DSS are computer-based support for management decision makers who are dealing with semi-structured problems"). Moore and Chang [7] define DSS as extendible systems capable of supporting ad hoc data analysis and decision modeling, oriented toward future planning, and used at irregular, unplanned intervals. For Sprague and Carlson [8], DSS are "interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems." In contrast, Keen [9] claims that it is impossible to give a precise definition including all the facets of the DSS ("there can be no definition of decision support systems, only of decision support"). Nevertheless, according to Power [10], the term decision support system remains a useful and inclusive term for many types of information systems that support decision making. He humorously adds that every time a computerized system is not an on-line transaction processing system (OLTP), someone will be tempted to call it a DSS. As you can see, there is no universally accepted definition of DSS. [11]
Recommended reading: Druzdzel and Flynn (1999), Power (2000), Sprague and Watson (1993), the first chapter of Power (2002), the first chapter of Marakas (1999), the first chapter of Silver (1991), the first two chapters of Sauter (1997), and Holsaple and Whinston (1996).

A brief history
In the absence of an all-inclusive definition, we focus on the history of DSS (see also Power[11]). According to Keen and Scott Morton [6], the concept of decision support has evolved from two main areas of research: the theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s. It is considered that the concept of DSS became an area of research of its own in the middle of the 1970s, before gaining in intensity during the 1980s. In the middle and late 1980s, executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS. Beginning in about 1990, data warehousing and on-line analytical processing (OLAP) began broadening the realm of DSS. As the turn of the millennium approached, new Web-based analytical applications were introduced.
It is clear that DSS belong to an environment with multidisciplinary foundations, including (but not exclusively) database research, artificial intelligence, human-computer interaction, simulation methods, software engineering, and telecommunications.
DSS also have a weak connection to the user interface paradigm of hypertext. Both the University of Vermont PROMIS system (for medical decision making) and the Carnegie Mellon ZOG/KMS system (for military and business decision making) were decision support systems which also were major breakthroughs in user interface research. Furthermore, although hypertext researchers have generally been concerned with information overload, certain researchers, notably Douglas Engelbart, have been focused on helping decision makers in particular.

Taxonomies
As with the definition, there is no universally accepted taxonomy of DSS either. Different authors propose different classifications. Using the relationship with the user as the criterion, Haettenschwiler [12] differentiates passive, active, and cooperative DSS. A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative DSS allows the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. The system again improves, completes, and refines the suggestions of the decision maker and sends them back to her for validation. The whole process then starts again, until a consolidated solution is generated.
Using the mode of assistance as the criterion, Power [13] differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.
A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data intensive. Dicodess is an example of an open source model-driven DSS generator [14].
A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove [15].
A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.
A document-driven DSS manages, retrieves and manipulates unstructured information in a variety of electronic formats.
A knowledge-driven DSS provides specialized problem solving expertise stored as facts, rules, procedures, or in similar structures.[13]
Using scope as the criterion, Power [10] differentiates enterprise-wide DSS and desktop DSS. An enterprise-wide DSS is linked to large data warehouses and serves many managers in the company. A desktop, single-user DSS is a small systems that runs on an individual manager's PC.

Architectures
Once again, different authors identify different components in a DSS. Sprague and Carlson [8] identify three fundamental components of DSS: (a) the database management system (DBMS), (b) the model-base management system (MBMS), and (c) the dialog generation and management system (DGMS).
Haag et al. [16] describe these three components in more detail: The Data Management Component stores information (which can be further subdivided into that derived from an organization's traditional data repositories, from external sources such as the Internet, or from the personal insights and experiences of individual users); the Model Management Component handles representations of events, facts, or situations (using various kinds of models, two examples being optimization models and goal-seeking models); and the User Interface Management Component is of course the component that allows a user to interact with the system.
According to Power [13], academics and practitioners have discussed building DSS in terms of four major components: (a) the user interface, (b) the database, (c) the model and analytical tools, and (d) the DSS architecture and network.
Hättenschwiler [12] identifies five components of DSS: (a) users with different roles or functions in the decision making process (decision maker, advisors, domain experts, system experts, data collectors), (b) a specific and definable decision context, (c) a target system describing the majority of the preferences, (d) a knowledge base made of external data sources, knowledge databases, working databases, data warehouses and meta-databases, mathematical models and methods, procedures, inference and search engines, administrative programs, and reporting systems, and (e) a working environment for the preparation, analysis, and documentation of decision alternatives.
Marakas [17] proposes a generalized architecture made of five distinct parts: (a) the data management system, (b) the model management system, (c) the knowledge engine, (d) the user interface, and (e) the user(s).
There are several ways to classify DSS applications. Not every DSS fits neatly into one category, but a mix of two or more architecture in one.
Holsapple and Whinston [18] classify DSS into the following six frameworks: Text-oriented DSS, Database-oriented DSS, Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS, and Compound DSS.
A compound DSS is the most popular classification for a DSS. It is a hybrid system that includes two or more of the five basic structures described by Holsapple and Whinston [18].
The support given by DSS can be separated into three distinct, interrelated categories [19]: Personal Support, Group Support, and Organizational Support.
Additionally, the build up of a DSS is also classified into a few characteristics. 1) inputs: this is used so the DSS can have factors, numbers, and characteristics to analyze. 2) user knowledge and expertise: This allows the system to decide how much it is relied on, and exactly what inputs must be analyzed with or without the user. 3) outputs: This is used so the user of the system can analyze the decisions that may be made and then potentially 4) make a decision: This decision making is made by the DSS, however, it is ultimately made by the user in order to decide on which criteria it should use.
DSSs which perform selected cognitive decision-making functions and are based on artificial intelligence or intelligent agents technologies are called Intelligent Decision Support Systems (IDSS).

Applications
As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.
Some of the examples is Clinical decision support system for medical diagnosis. Other examples include a bank loan officer verifying the credit of a loan applicant or an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs.
DSS is extensively used in business and management. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources.
A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package[20][21], developed through financial support of USAID during the 80's and 90's, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels.
A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.
DSS has many applications that have already been spoken about. However, it can be used in any field where organization is necessary. Additionally, a DSS can be designed to help make decisions on the stock market, or deciding which area or segment to market a product toward.

Characteristics and Capabilities of DSS
Because there is no exact definition of DSS, there is obviously no agreement on the standard characteristics and capabilities of DSS. Turban, E.,Aronson, J.E., and Liang, T.P. [22] constitute an ideal set of characteristics and capabilities of DSS. The key DSS characteristics and capabilities are as follows:
Support for decision makers in semistructured and unstructured problems.
Support managers at all levels.
Support individuals and groups.
Support for interdependent or sequential decisions.
Support intelligence, design, choice, and implementation.
Support variety of decision processes and styles.
DSS should be adaptable and flexible.
DSS should be interactive and provide ease of use.
Effectiveness balanced with efficiency (benefit must exceed cost).
Complete control by decision-makers.
Ease of development by (modification to suit needs and changing environment) end users.
Support modeling and analysis.
Data access.
Standalone, integration and Web-based.

Decision support system

Definitions
Because there are many approaches to decision-making and because of the wide range of domains in which decisions are made, the concept of decision support system (DSS) is very broad. A DSS can take many different forms. In general, we can say that a DSS is a computerized system for helping make decisions. A decision is a choice between alternatives based on estimates of the values of those alternatives. Supporting a decision means helping people working alone or in a group gather intelligence, generate alternatives and make choices. Supporting the choice making process involves supporting the estimation, the evaluation and/or the comparison of alternatives. In practice, references to DSS are usually references to computer applications that perform such a supporting role.[1]
The term decision support system has been used in many different ways (Alter 1980, Power, 2002) and has been defined in various ways depending upon the author's point of view [2]. Finlay [3] and others define a DSS rather broadly as "a computer-based system that aids the process of decision making." Turban [4] defines it more specifically as "an interactive, flexible, and adaptable computer-based information system, especially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker's own insights."
Other definitions fall between these two extremes. For Little [5], a DSS is a "model-based set of procedures for processing data and judgments to assist a manager in his decision-making." For Keen and Scott Morton [6], a DSS couples the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions ("DSS are computer-based support for management decision makers who are dealing with semi-structured problems"). Moore and Chang [7] define DSS as extendible systems capable of supporting ad hoc data analysis and decision modeling, oriented toward future planning, and used at irregular, unplanned intervals. For Sprague and Carlson [8], DSS are "interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems." In contrast, Keen [9] claims that it is impossible to give a precise definition including all the facets of the DSS ("there can be no definition of decision support systems, only of decision support"). Nevertheless, according to Power [10], the term decision support system remains a useful and inclusive term for many types of information systems that support decision making. He humorously adds that every time a computerized system is not an on-line transaction processing system (OLTP), someone will be tempted to call it a DSS. As you can see, there is no universally accepted definition of DSS. [11]
Recommended reading: Druzdzel and Flynn (1999), Power (2000), Sprague and Watson (1993), the first chapter of Power (2002), the first chapter of Marakas (1999), the first chapter of Silver (1991), the first two chapters of Sauter (1997), and Holsaple and Whinston (1996).

A brief history
In the absence of an all-inclusive definition, we focus on the history of DSS (see also Power[11]). According to Keen and Scott Morton [6], the concept of decision support has evolved from two main areas of research: the theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s. It is considered that the concept of DSS became an area of research of its own in the middle of the 1970s, before gaining in intensity during the 1980s. In the middle and late 1980s, executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS. Beginning in about 1990, data warehousing and on-line analytical processing (OLAP) began broadening the realm of DSS. As the turn of the millennium approached, new Web-based analytical applications were introduced.
It is clear that DSS belong to an environment with multidisciplinary foundations, including (but not exclusively) database research, artificial intelligence, human-computer interaction, simulation methods, software engineering, and telecommunications.
DSS also have a weak connection to the user interface paradigm of hypertext. Both the University of Vermont PROMIS system (for medical decision making) and the Carnegie Mellon ZOG/KMS system (for military and business decision making) were decision support systems which also were major breakthroughs in user interface research. Furthermore, although hypertext researchers have generally been concerned with information overload, certain researchers, notably Douglas Engelbart, have been focused on helping decision makers in particular.

Taxonomies
As with the definition, there is no universally accepted taxonomy of DSS either. Different authors propose different classifications. Using the relationship with the user as the criterion, Haettenschwiler [12] differentiates passive, active, and cooperative DSS. A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative DSS allows the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. The system again improves, completes, and refines the suggestions of the decision maker and sends them back to her for validation. The whole process then starts again, until a consolidated solution is generated.
Using the mode of assistance as the criterion, Power [13] differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.
A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data intensive. Dicodess is an example of an open source model-driven DSS generator [14].
A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove [15].
A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.
A document-driven DSS manages, retrieves and manipulates unstructured information in a variety of electronic formats.
A knowledge-driven DSS provides specialized problem solving expertise stored as facts, rules, procedures, or in similar structures.[13]
Using scope as the criterion, Power [10] differentiates enterprise-wide DSS and desktop DSS. An enterprise-wide DSS is linked to large data warehouses and serves many managers in the company. A desktop, single-user DSS is a small systems that runs on an individual manager's PC.

Architectures
Once again, different authors identify different components in a DSS. Sprague and Carlson [8] identify three fundamental components of DSS: (a) the database management system (DBMS), (b) the model-base management system (MBMS), and (c) the dialog generation and management system (DGMS).
Haag et al. [16] describe these three components in more detail: The Data Management Component stores information (which can be further subdivided into that derived from an organization's traditional data repositories, from external sources such as the Internet, or from the personal insights and experiences of individual users); the Model Management Component handles representations of events, facts, or situations (using various kinds of models, two examples being optimization models and goal-seeking models); and the User Interface Management Component is of course the component that allows a user to interact with the system.
According to Power [13], academics and practitioners have discussed building DSS in terms of four major components: (a) the user interface, (b) the database, (c) the model and analytical tools, and (d) the DSS architecture and network.
Hättenschwiler [12] identifies five components of DSS: (a) users with different roles or functions in the decision making process (decision maker, advisors, domain experts, system experts, data collectors), (b) a specific and definable decision context, (c) a target system describing the majority of the preferences, (d) a knowledge base made of external data sources, knowledge databases, working databases, data warehouses and meta-databases, mathematical models and methods, procedures, inference and search engines, administrative programs, and reporting systems, and (e) a working environment for the preparation, analysis, and documentation of decision alternatives.
Marakas [17] proposes a generalized architecture made of five distinct parts: (a) the data management system, (b) the model management system, (c) the knowledge engine, (d) the user interface, and (e) the user(s).
There are several ways to classify DSS applications. Not every DSS fits neatly into one category, but a mix of two or more architecture in one.
Holsapple and Whinston [18] classify DSS into the following six frameworks: Text-oriented DSS, Database-oriented DSS, Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS, and Compound DSS.
A compound DSS is the most popular classification for a DSS. It is a hybrid system that includes two or more of the five basic structures described by Holsapple and Whinston [18].
The support given by DSS can be separated into three distinct, interrelated categories [19]: Personal Support, Group Support, and Organizational Support.
Additionally, the build up of a DSS is also classified into a few characteristics. 1) inputs: this is used so the DSS can have factors, numbers, and characteristics to analyze. 2) user knowledge and expertise: This allows the system to decide how much it is relied on, and exactly what inputs must be analyzed with or without the user. 3) outputs: This is used so the user of the system can analyze the decisions that may be made and then potentially 4) make a decision: This decision making is made by the DSS, however, it is ultimately made by the user in order to decide on which criteria it should use.
DSSs which perform selected cognitive decision-making functions and are based on artificial intelligence or intelligent agents technologies are called Intelligent Decision Support Systems (IDSS).

Applications
As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.
Some of the examples is Clinical decision support system for medical diagnosis. Other examples include a bank loan officer verifying the credit of a loan applicant or an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs.
DSS is extensively used in business and management. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources.
A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package[20][21], developed through financial support of USAID during the 80's and 90's, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels.
A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.
DSS has many applications that have already been spoken about. However, it can be used in any field where organization is necessary. Additionally, a DSS can be designed to help make decisions on the stock market, or deciding which area or segment to market a product toward.

Characteristics and Capabilities of DSS
Because there is no exact definition of DSS, there is obviously no agreement on the standard characteristics and capabilities of DSS. Turban, E.,Aronson, J.E., and Liang, T.P. [22] constitute an ideal set of characteristics and capabilities of DSS. The key DSS characteristics and capabilities are as follows:
Support for decision makers in semistructured and unstructured problems.
Support managers at all levels.
Support individuals and groups.
Support for interdependent or sequential decisions.
Support intelligence, design, choice, and implementation.
Support variety of decision processes and styles.
DSS should be adaptable and flexible.
DSS should be interactive and provide ease of use.
Effectiveness balanced with efficiency (benefit must exceed cost).
Complete control by decision-makers.
Ease of development by (modification to suit needs and changing environment) end users.
Support modeling and analysis.
Data access.
Standalone, integration and Web-based.

Sunday, July 15, 2007

Artificial intelligence:ปัญญาประดิษฐ์

AI” redirects here. For other uses of "AI" and "Artificial Intelligence", see AI (disambiguation).

Garry Kasparov playing against Deep Blue, the first machine to win a chess game against a reigning world champion.

Artificial intelligence Portal
The term Artificial Intelligence (AI) was first used by John McCarthy who used it to mean "the science and engineering of making intelligent machines".[1] It can also refer to intelligence as exhibited by an artificial (man-made, non-natural, manufactured) entity. While AI is the generally accepted term, others, including both Computational intelligence and Synthetic intelligence, have been proposed as potentially being "more accurate".[2] The terms strong and weak AI can be used to narrow the definition for classifying such systems. AI is studied in overlapping fields of computer science, psychology, philosophy, neuroscience, and engineering, dealing with intelligent behavior, learning, and adaptation and usually developed using customized machines or computers.
Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, natural language, speech, and facial recognition. As such, the study of AI has also become an engineering discipline, focused on providing solutions to real life problems, knowledge mining, software applications, and strategy games like computer chess and other video games. One of the biggest difficulties with AI is that of comprehension. Many devices have been created that can do amazing things, but critics of AI claim that no actual comprehension by the AI machine has taken place.

Mechanisms
Generally speaking AI systems are built around automated inference engines including forward reasoning and backwards reasoning. Based on certain conditions ("if") the system infers certain consequences ("then"). AI applications are generally divided into two types, in terms of consequences: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do however also classify conditions before inferring actions, and therefore classification forms a central part of most AI systems.
Classifiers make use of pattern recognition for condition matching. In many cases this does not imply absolute, but rather the closest match. Techniques to achieve this divide roughly into two schools of thought: Conventional AI and Computational intelligence (CI).
Conventional AI research focuses on attempts to mimic human intelligence through symbol manipulation and symbolically structured knowledge bases. This approach limits the situations to which conventional AI can be applied. Lotfi Zadeh stated that "we are also in possession of computational tools which are far more effective in the conception and design of intelligent systems than the predicate-logic-based methods which form the core of traditional AI," techniques which have become known as soft computing. These often biologically inspired methods stand in contrast to conventional AI and compensate for the shortcomings of symbolicism.[3] These two methodologies have also been labeled as neats vs. scruffies, with neats emphasizing the use of logic and formal representation of knowledge while scruffies take an application-oriented heuristic bottom-up approach.[4]

Classifiers
Classifiers are functions that can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision that has to be made. All the observations combined with their class labels are known as a data set.
When a new observation is received, that observation is classified based on previous experience. A classifier can be trained in various ways; there are mainly statistical and machine learning approaches.
A wide range of classifiers are available, each with its strengths and weaknesses. Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance. Determining a suitable classifier for a given problem is however still more an art than science.
The most widely used classifiers are the neural network, support vector machine, k-nearest neighbor algorithm, Gaussian mixture model, naive Bayes classifier, and decision tree.

Conventional AI
Conventional AI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI). (Also see semantics.) Methods include:
Expert systems: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them.
Case based reasoning: stores a set of problems and answers in an organized data structure called cases. A case based reasoning system upon being presented with a problem finds a case in its knowledge base that is most closely related to the new problem and presents its solutions as an output with suitable modifications.[5]
Bayesian networks
Behavior based AI: a modular method of building AI systems by hand



ปัญญาประดิษฐ์ (Artificial Intelligence) หรือ เอไอ (AI) หมายถึงความฉลาดเทียมที่สร้างขึ้นให้กับสิ่งที่ไม่มีชีวิต ปัญญาประดิษฐ์เป็นสาขาหนึ่งในด้านวิทยาการคอมพิวเตอร์ และวิศวกรรมเป็นหลัก แต่ยังรวมถึงศาตร์ในด้านอื่นๆอย่างจิตวิทยา ปรัชญา หรือชีววิทยา ซึ่งสาขาปัญญาประดิษฐ์เป็นการเรียนรู้เกี่ยวกับกระบวนการการคิด การกระทำ การให้เหตุผล การปรับตัว หรือการอนุมาน และการทำงานของสมอง แม้ว่าดังเดิมนั้นเป็นสาขาหลักในวิทยาการคอมพิวเตอร์ แต่แนวคิดหลายๆ อย่างในศาสตร์นี้ได้มาจากการปรับปรุงเพิ่มเติมจากศาสตร์อื่นๆ เช่น
การเรียนรู้ของเครื่อง นั้นมีเทคนิคการเรียนรู้ที่เรียกว่า การเรียนรู้ต้นไม้ตัดสินใจ ซึ่งประยุกต์เอาเทคนิคการอุปนัยของ จอห์น สจวร์ต มิลล์ นักปรัชญาชื่อดังของอังกฤษ มาใช้
เครือข่ายประสาทเทียมก็นำเอาแนวคิดของการทำงานของสมองของมนุษย์ มาใช้ในการแก้ปัญหาการแบ่งประเภทของข้อมูล และแก้ปัญหาอื่นๆ ทางสถิติ เช่น การวิเคราะห์ความถดถอยหรือ การปรับเส้นโค้ง
อย่างไรก็ตาม เนื่องจากปัจจุบันวงการปัญญาประดิษฐ์ มีการพัฒนาส่วนใหญ่โดยนักวิทยาศาสตร์คอมพิวเตอร์ อีกทั้งวิชาปัญญาประดิษฐ์ ก็ต้องเรียนที่ภาควิชาคอมพิวเตอร์ของคณะวิทยาศาสตร์หรือคณะวิศวกรรมศาสตร์ เราจึงถือเอาง่าย ๆ ว่า ศาสตร์นี้เป็นสาขาของวิทยาการคอมพิวเตอร์นั่นเอง
นิยามของปัญญาประดิษฐ์

หุ่นยนต์ของฮอนด้า ที่รู้จักดีในด้านปัญญาประดิษฐ์
มีคำนิยามของปัญญาประดิษฐ์มากมาย ซึ่งสามารถจัดแบ่งออกเป็น 4 ประเภทโดยมองใน 2 มิติ ได้แก่
ระหว่าง นิยามที่เน้นระบบที่เลียนแบบมนุษย์ กับ นิยามที่เน้นระบบที่ระบบที่มีเหตุผล (แต่ไม่จำเป็นต้องเหมือนมนุษย์)
ระหว่าง นิยามที่เน้นความคิดเป็นหลัก กับ นิยามที่เน้นการกระทำเป็นหลัก
ปัจจุบันงานวิจัยหลักๆ ของ AI จะมีแนวคิดในรูปที่เน้นเหตุผลเป็นหลัก เนื่องจากการนำ AI ไปประยุกต์ใช้แก้ปัญหา ไม่จำเป็นต้องอาศัยอารมณ์หรือความรู้สึกของมนุษย์ อย่างไรก็ตามนิยามทั้ง 4 ไม่ได้ต่างกันโดยสมบูรณ์ นิยามทั้ง 4 ต่างก็มีส่วนร่วมที่คาบเกี่ยวกันอยู่
นิยามดังกล่าวคือ
ระบบที่คิดเหมือนมนุษย์ (Systems that think like humans)
[AI คือ] ความพยายามใหม่อันน่าตื่นเต้นที่จะทำให้คอมพิวเตอร์คิดได้ ... เครื่องจักรที่มีสติปัญญาอย่างครบถ้วนและแท้จริง ("The exciting new effort to make computers think ... machines with minds, in the full and literal sense." [Haugeland, 1985])
[AI คือ กลไกของ]กิจกรรมที่เกี่ยวข้องกับความคิดมนุษย์ เช่น การตัดสินใจ การแก้ปัญหา การเรียนรู้ ("[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning." [Bellman, 1978])
หมายเหตุ ก่อนที่จะทำให้เครื่องคิดอย่างมนุษย์ได้ ต้องรู้ก่อนว่ามนุษย์มีกระบวนการคิดอย่างไร ซึ่งการวิเคราะห์ลักษณะการคิดของมนุษย์ เป็นศาสตร์ด้าน cognitive science เช่น ศึกษาการเรียงตัวของเซลล์สมองในสามมิติ ศึกษาการถ่ายเทประจุไฟฟ้า และวิเคราะห์การเปลี่ยนแปลงทางเคมีไฟฟ้าในร่างกาย ระหว่างการคิด ซึ่งจนถึงปัจจุบัน (พ.ศ. 2548) เราก็ยังไม่รู้แน่ชัดว่า มนุษย์เรา คิดได้อย่างไร
ระบบที่กระทำเหมือนมนุษย์ (Systems that act like humans)
[AI คือ] วิชาของการสร้างเครื่องจักรที่ทำงานในสิ่งซึ่งอาศัยปัญญาเมื่อกระทำโดยมนุษย์ ("The art of creating machines that perform functions that requires intelligence when performed by people." [Kurzweil, 1990])
[AI คือ] การศึกษาวิธีทำให้คอมพิวเตอร์กระทำในสิ่งที่มนุษย์ทำได้ดีกว่าในขณะนั้น ("The study of how to make computers do things at which, at the moment, people are better." [Rich and Knight, 1991])
หมายเหตุ การกระทำเหมือนมนุษย์ เช่น
สื่อสารได้ด้วยภาษาที่มนุษย์ใช้ เช่น ภาษาไทย ภาษาอังกฤษ ตัวอย่างคือ การแปลงข้อความเป็นคำพูด และ การแปลงคำพูดเป็นข้อความ
มีประสาทรับสัมผัสคล้ายมนุษย์ เช่น คอมพิวเตอร์รับภาพได้โดยอุปกรณ์รับสัมผัส แล้วนำภาพไปประมวลผล
เคลื่อนไหวได้คล้ายมนุษย์ เช่น หุ่นยนต์ช่วยงานต่าง ๆ อย่างการ ดูดฝุ่น เคลื่อนย้ายสิ่งของ
เรียนรู้ได้ โดยสามาถตรวจจับรูปแบบการเกิดของเหตุการณ์ใด ๆ แล้วปรับตัวสู่สิ่งแวดล้อมที่เปลี่ยนไปได้
ระบบที่คิดอย่างมีเหตุผล (Systems that think rationally)
[AI คือ] การศึกษาความสามารถในด้านสติปัญญาโดยการใช้โมเดลการคำนวณ ("The study of mental faculties through the use of computational model." [Charniak and McDermott, 1985])
[AI คือ] การศึกษาวิธีการคำนวณที่สามารถรับรู้ ใช้เหตุผล และกระทำ ("The study of the computations that make it possible to perceive, reason, and act" [Winston, 1992])
หมายเหตุ คิดอย่างมีเหตุผล หรือคิดถูกต้อง เช่น ใช้หลักตรรกศาสตร์ในการคิดหาคำตอบอย่างมีเหตุผล เช่น ระบบผู้เชี่ยวชาญ
ระบบที่กระทำอย่างมีเหตุผล (Systems that act rationally)
ปัญญาประดิษฐ์คือการศึกษาเพื่อออกแบบเอเจนต์ที่มีปัญญา ("Computational Intelligence is the study of the design of intelligent agents" [Poole et al., 1998])
AI เกี่ยวข้องกับพฤติกรรมที่แสดงปัญญาในสิ่งที่มนุษย์สร้างขึ้น ("AI ... is concerned with intelligent behavior in artifacts" [Nilsson, 1998])
หมายเหตุ กระทำอย่างมีเหตุผล เช่น เอเจนต์ (โปรแกรมที่มีความสามารถในการกระทำ หรือเป็นตัวแทนในระบบอัตโนมัติต่าง ๆ) สามารถกระทำอย่างมีเหตุผลเพื่อบรรลุเป้าหมายที่ได้ตั้งไว้ เช่น เอเจนต์ในระบบขับรถอัตโนมัติ ที่มีเป้าหมายว่าต้องไปถึงเป้าหมายในระยะทางที่สั้นที่สุด ต้องเลือกเส้นทางที่ไปยังเป้าหมายที่สั้นที่สุดที่เป็นไปได้ จึงจะเรียกได้ว่า เอเจนต์กระทำอย่างมีเหตุผล อีกตัวอย่างเช่น เอเจนต์ในเกมหมากรุก ที่มีเป้าหมายว่าต้องเอาชนะคู่ต่อสู้ ก็ต้องเลือกเดินหมากที่จะทำให้คู่ต่อสู้แพ้ให้ได้ เป็นต้น