Decision support system

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zh:决策支持系统 Decision support systems are a class of computerized information systems that support decision making activities.

Contents

Definitions

The concept of a decision support system (DSS) is extremely broad and its definitions vary depending upon the author's point of view (Druzdzel and Flynn 1999). A DSS can take many different forms and the term can be used in many different ways (Alter 1980). On the one hand, Finlay (1994) and others define a DSS broadly as "a computer-based system that aids the process of decision making." In a more precise way, Turban (1995) defines it 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 fill the gap between these two extremes. For Keen and Scott Morton (1978), DSS couple 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"). For Sprague and Carlson (1982), DSS are "interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems." On the other hand, Keen (1980) 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 (1997), 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...

For more information, we recommend reading Druzdzel and Flynn (1999), Power (2000), Sprague and Watson (1993), the first chapter of Power (2002), the first chapter of Makaras (1999), the first chapter of Silver (1991), and the first two chapters of Sauter (1997).

A brief history

In the absence of an all-inclusive definition, we focus on the history of DSS (see also Power, 2003). According to Keen and Scott Morton (1978), 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 has 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 all-inclusive taxonomy of DSS either. Different authors propose different classifications. At the user-level, Httenschwiler (1999) 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.

At the conceptual level, Power (2002) 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 DSS users to aid decision makers in analyzing a situation, but they are not necessarily data intensive. Dicodess is an example of an open source, model-driven DSS generator (Gachet 2004). A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove (Stanhope 2002). 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. Finally, a knowledge-driven DSS provides specialized problem solving expertise stored as facts, rules, procedures, or in similar structures. At the system level, Power (1997) differentiates enterprise-wide DSS and desktop DSS. Enterprise-wide DSS are linked to large data warehouses and serve many managers in a company. Desktop, single-user DSS are small systems that reside on an individual manager's PC.

Other authors (Alter, Holsapple and Whinston, Donovan and Madnick, Hackathorn and Keen, Golden, Hevner and Power) propose different taxonomies. It is recommended to read the first chapter of Power (2002).

Architectures

Once again, different authors identify different components in a DSS. Sprague and Carlson (1982) 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).

According to Power (2002), 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. Httenschwiler (1999) 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 (1999) 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).

References

  • Alter, S. L. (1980). Decision support systems : current practice and continuing challenges. Reading, Mass., Addison-Wesley Pub.
  • Druzdzel, M. J. and R. R. Flynn (1999). Decision Support Systems. Encyclopedia of Library and Information Science. A. Kent, Marcel Dekker, Inc.
  • Finlay, P. N. (1994). Introducing decision support systems. Oxford, UK Cambridge, Mass., NCC Blackwell; Blackwell Publishers.
  • Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF.
  • Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsuntersttzung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208.
  • Keen, P. G. W. (1980). Decision support systems: a research perspective. Decision support systems : issues and challenges. G. Fick and R. H. Sprague. Oxford ; New York, Pergamon Press.
  • Keen, P. G. W. and M. S. Scott Morton (1978). Decision support systems : an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co.
  • Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.
  • Power, D. J. (1997). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
  • Power, D. J. (2000). Web-based and model-driven decision support systems: concepts and issues. in proceedings of the Americas Conference on Information Systems, Long Beach, California.
  • Power, D. J. (2002). Decision support systems : concepts and resources for managers. Westport, Conn., Quorum Books.
  • Power, D.J. A Brief History of Decision Support Systems. DSSResources.COM, World Wide Web, http://DSSResources.COM/history/dsshistory.html, version 2.8, May 31, 2003.
  • Sauter, V. L. (1997). Decision support systems : an applied managerial approach. New York, John Wiley.
  • Silver, M. (1991). Systems that support decision makers : description and analysis. Chichester ; New York, Wiley.
  • Sprague, R. H. and E. D. Carlson (1982). Building effective decision support systems. Englewood Cliffs, N.J., Prentice-Hall.
  • Sprague, R. H. and H. J. Watson (1993). Decision support systems : putting theory into practice. Englewood Clifts, N.J., Prentice Hall.
  • Stanhope, P. (2002). Get in the Groove : building tools and peer-to-peer solutions with the Groove platform. New York, Hungry Minds.
  • Turban, E. (1995). Decision support and expert systems : management support systems. Englewood Cliffs, N.J., Prentice Hall.

See also

External links

  • DSSResources.COM (http://www.dssresources.com)
  • Genie (http://www.sis.pitt.edu/~genie/)
  • Dicodess (http://www.dicodess.org)
  • GIDEON (http://www.GIDEONonline.com) - Global Infectious Diseases and Epidemiology Network
  • iisy AG (http://www.iisy.com) - intelligent information systems
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