Redes de Multidifusión (Multicast)

modern business organizations create and store a tremen- dous amount of data in the form of transactions that become database records. Increasingly, however, businesses are relying on their ability to use data that was collected for one purpose (such as sales, customer service, and inventory) for purposes of marketing research, planning, or decision support. For example, transaction data might be revisited with a view to identifying the common characteristics of the firm’s best customers or determining the best way to market a particular type of product. In order to conduct such research or analysis, the data collected in the course of business must be stored in such a way that it is both accu- rate and flexible in terms of the number of different ways in which it can be queried. The idea of the data warehouse is to provide such a repository for data.

When data is used for particular purposes such as sales or inventory control, it is usually structured in records where certain fields (such as stock number or quantity) are routinely processed. It is not so easy to ask a differ- ent question such as “which customers who bought this product from us also bought this other product within six months of their first purchase?” One way to make it easier to query data in new ways is to store the data not in records but in arrays where, for example, one dimension might be product numbers and another categories of customers. This approach, called Online Analytical Processing (OLAP) makes it possible to extract a large variety of relationships without being limited by the original record structure.

i

The key in designing a data warehouse is to provide a way that researchers using analytical tools (such as statistics programs) can access the raw data in the underlying data- base. Software using query languages such as SQL can serve as such a link. Thus, the researcher can define a query

using the many dimensions of the data array, and the OLAP software (also called middleware) translates this query into the appropriate combination of queries against the underly- ing relational database.

The data warehouse is closely related to the concept of data mining. In fact, data mining can be viewed as the exploitation of the collection of views, queries, and other elements that can be generated using the data warehouse as the infrastructure (see datamining).

Further Reading

Data Warehousing Information Center. Available online. URL: http://www.dwinfocenter.org/. Accessed July 8, 2007. Dm Review/dataWarehouse.com Available online. URL: http://

www.datawarehouse.com/. Accessed July 8, 2007.

Inmon, W. H. Building the Data Warehouse. 4th ed. Indianapolis: Wiley, 2005.

Kimball, Ralph, and margy Ross. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. 2nd ed. Indianapo- lis: Wiley, 2002.

DBMS 

decision support system

A decision support system (DSS) is a computer applica- tion that focuses on providing access to or analysis of the key information needed to make decisions, particularly in business. (It can be thought of as a more narrowly focused approach to computer assistance to management—see man-

agementinfoRmationsystem.)

The development of DSS has several roots reaching back to the 1950s. This includes operational analysis and the the- ory of organizations and the development of the first inter- active (rather than batch-processing) computer systems. Indeed, the SAgE automated air defense system developed starting in the 1950s could be described as a military DSS. The system presented real-time information (radar plots) and enabled the operator to select and focus on particular elements using a light pen. By the 1960s more-systematic research on DSS was underway and included the provoca- tive idea of “human-computer symbiosis” for problem solv- ing (see licklideR, j. c. R.).

The “back end” of a DSS is one or more large databases (see dataWaRehouse) that might be compiled from transac- tion records, statistics, online news services, or other sources. The “middle” of the DSS process includes the ability to ana- lyze the data (online analytical processing, or OLAP; see also

datamining). Other elements that might be included in a DSS are rules-based systems (see expeRtsystem) and inter- active models (see simulation). These elements can help the user explore alternatives and “what if” scenarios.

The structure of a DSS is sometimes described as model driven (generally using a small amount of selected data), data driven (based on a large collection of historical data), knowledge driven (perhaps using an expert system), or communications driven (focusing on use of collaborative software—see gRoupWaRe, as well as more recent develop- ments) (see Wikisand Wikipedia).

The general process of warehousing data. The data warehouse adds value to the data by further structuring it so relationships can be explored by analysts.

u

i

—t

“F

e

”

All the data and tools in the world are of little use if the user cannot work with it effectively (see useRinteRface). Information or the results of queries or modeling must be displayed in a way that is easy to grasp and use. (A spread- sheet with nothing highlighted or marked would be a poor choice.) graphical “widgets” such as dials, buttons, sliders, and so on can help the user see the results and decide what to look at next (see digitaldashboaRd).

Another key principle is that decision making in the modern world is as much a social as an individual process. Therefore a DSS should facilitate communication and col- laboration (or interface with software that does so).

A variety of specialized DSSs have been developed for various fields. Examples include PROmIS (for medical deci- sion making) and Carnegie mellon’s ZOg/KmS, which has been used in military and business settings.

Further Reading

greenes, Robert A., ed. Clinical Decision Support: The Road Ahead.

Orlando, Fla.: Academic Press, 2006.

gupta, Jatinder N. D., guisseppi A. Forgionne, and manuel mora T., eds. Intelligent Decision-Making Support Systems. New York: Springer, 2006.

Power, D. J. “A Brief History of Decision Support Systems.” Version 4.0. Available online. URL: http://dssresources.com/history/ dsshistory.html. Accessed September 10, 2007.

Turban, Efraim, et al. Decision Support and Business Intelligence Sys- tems. 8th ed. Upper Saddle River, N.J.: Prentice-Hall, 2006.

Dell, Inc.

Dell Computer (NASDAQ: DELL) is one of the world’s lead- ing manufacturers and sellers of desktop and laptop com- puters (see peRsonalcomputeR). By 2008 Dell had more than 88,000 employees worldwide.

The company was founded by michael Dell, a student at the University of Texas at Austin whose first company was PC’s Limited, founded in 1984. Even at this early stage Dell successfully employed several practices that would come to typify the Dell strategy: Sell directly to customers (not through stores), build each machine to suit the customer’s preferences, and be aggressive in competing on price.

In 1988 the growing company changed its name to Dell Computer Corporation. In the early 1990s Dell tried an alternative business model, selling through warehouse clubs and computer superstores. When that met with little success, Dell returned to the original formula. In 1999 Dell overtook Compaq to become the biggest computer retailer in America.

generally, the Dell product line has aimed at two basic segments: business-oriented (OptiPlex desktops and Lati- tude laptops) and home/consumer (xPS desktops and Inspiron laptops, and in 2007, Inspiron desktops).

c

d

Around 2002, Dell, perhaps facing the growing commod- ity pricing of basic PCs, began to expand into computer peripherals (such as printers) and even home entertainment products (TVs and audio players). In 2003 the company

changed its name to Dell, Inc. (dropping “Computer”). Dell also experienced an increase in international sales in 2005, while achieving a first place ranking in Fortune magazine as “most admired company.” However, the company also made some missteps, losing $300 million because of faulty capacitors on some motherboards. Earnings continued to fall short of analysts’ expectations, and in January 2007 michael Dell returned as CEO after the resignation of Kevin B. Rollins, who had held the post since 2004.

meanwhile, Dell has made further attempts at diversify- ing the product line. In 2006 the company began, for the first time, to introduce AmD (instead of Intel) processors in certain products, and in 2007 Dell responded to cus- tomer suggestions by announcing that some models could be ordered with Linux rather than microsoft Windows installed. Also in 2007, Dell acquired Alienware, maker of high-performance gaming machines.

Dell has struggled to boost its sagging revenue as it lost ground to competitors, notably HP. Known primarily as a mail-order and online company, Dell has announced that it will also sell PCs through “big box” retailers such as Wal-mart.

Dell continues to receive praise and criticism from vari- ous quarters. On the positive side, the company has been praised for its computer-recycling program by the National Recycling Coalition. Dell products also tend to score at or near the top in performance reviews by publications such as PC Magazine.

On the other hand, there have been complaints about Dell’s technical support operation. Technicians apparently follow “scripts” very closely, making customers take sys- tems apart and follow troubleshooting directions regardless of what the customer might already know or have done. The increasing “offshoring” of support has also led to com- plaints about language and communication problems. Further Reading

Dell, Inc. Available online. URL: http://www.dell.com. Accessed September 10, 2007.

Dell, michael, and Catherine Fredman. Direct from Dell: Strategies that Revolutionized an Industry. New York: HarperBusiness, 2006.

Holzner, Steven. How Dell Does It: Using Speed and Innovation to Achieve Extraordinary Results. New York: mcgraw-Hill, 2006.

demon

The unusual computing term demon (sometimes spelled daemon) refers to a process (program) that runs in the background, checking for and responding to certain events. The utility of this concept is that it allows for automation of information processing without requiring that an operator initiate or manage the process.

For example, a print spooler demon looks for jobs that are queued for printing, and deals with the negotiations nec- essary to maintain the flow of data to that device. Another demon (called chron in UNIx systems) reads a file describ- ing processes that are designated to run at particular dates or times. For example, it may launch a backup utility every morning at 1:00 a.m. E-mail also depends on the periodic operation of “mailer demons.”

While the term demon originated in the UNIx culture, similar facilities exist in many operating systems. Even in the relatively primitive mS-DOS for IBm personal comput- ers of the 1980s, the ability to load and retain small utility programs that could share the main memory with the cur- rently running application allowed for a sort of demon that could spool output or await a special keypress. microsoft Windows systems have many demon-like operating system components that can be glimpsed by pressing the Ctrl-Alt- Delete key combination.

The sense of autonomy implied in the term demon is in some ways similar to that found in bots or software agents that can automatically retrieve information on the Internet, or in the Web crawler, which relentlessly pursues, records, and indexes Web links for search engines. (See softWaRe agent and seaRchengine.)

Further Reading

Brock, Dean, and Bob Benites. Mastering Tools, Taming Daemons: UNIX for the Wizard Apprentice. greenwich, Conn.: manning Publications, 1995.

Stevens, W. Richard. Advanced Programming in the UNIX Environ- ment. Upper Saddle River, N.J.: Addison-Wesley, 2005. “UNIx Daemons in Perl.” Available online. URL: http://www.

webreference.com/perl/tutorial/9/. Accessed July 8, 2007.

Dertouzos, Michael L.