[孩子的这几个时刻，需要父母更多的关爱与支持]晚饭后，走进女儿的房间，她立即将一个小本子藏了起来。我问她藏什么呢。她说是日记本，不想让我看。小家伙有秘密了，我必须支持，保证不看，蒙上眼睛，让她随便藏，还告诉她，想告诉妈妈时，妈...+阅读
Decision Support System
Decision support systems are a class of puterized information systems that support decision making activities.
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 puter-based system that aids the process of decision making." In a more precise way, Turban (1995) defines it as "an interactive, flexible, and adaptable puter-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 puter to improve the quality of decisions ("DSS are puter-based support for management decision makers who are dealing with semi-structured problems"). For Sprague and Carlson (1982), DSS are "interactive puter-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 puterized 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.
Additionally, the specifics of it is what makes it less generalized and more detailed. In addition, a DSS also is a specific Software application that helps to analyze data contained with a customer database. This approach to customers is used when deciding on target markets as well as customer habits. As you can see in this specific example, it is obvious that DSS can be used for more than just anization.
Remended 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), 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, 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 anizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the technical work on interactive puter 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 anizational 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-puter interaction, simulation methods, software engineering, and telemunications.
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 he generally been concerned with information overload, certain researchers, notably Douglas Engelbart, he 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, H?ttenschwiler (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, plete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. The system again improves, pletes, 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 munication-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 munication-driven DSS supports more than one person working on a shared task; e
xamples include integrated tools like Microsoft's Meeting or Groove (Stanhope 2002). A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal pany 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.
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 pany. Desktop, single-user DSS are small systems that reside on an individual manager's PC.
When classifying DSS, it can be viewed as very broad or very narrow. Since it is difficult to classify DSS into only one classification, the taxonomy cannot exactly be pinpointed. However, if it is necessary, a DSS is certainly classified into precise, scientific anizational software that not only contributes, but also performs decision making steps in order to ease the pressure for its users. The fact is in a few words, DSS is an anizational decision making software.
Other authors, such as Alter, Holsapple and Whinston, Donovan and Madnick, Hackathorn and Keen, Golden, Hevner and Power, propose different taxonomies. Reading the first chapter of Power (2002) is remended.
Architectures
Once again, different authors identify different ponents in a DSS. Sprague and Carlson (1982) identify three fundamental ponents 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. (2000) describe these three ponents in more detail: the Data Management ponent stores information (which can be further subdivided into that derived from an anization's traditional data repositories, from external sources such as the Inter, or from the personal insights and experiences of individual users); the Model Management ponent 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 ponent is of course the ponent that allows a user to interact with the system.
According to Power (2002), academics and practitioners he discussed building DSS in terms of four major ponents: (a) the user interface, (b) the database, (c) the model and analytical tools, and (d) the DSS architecture and work. H?ttenschwiler (1999) identifies five ponents 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).
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 (1996) classify DSS into the following six frameworks: Text-oriented DSS, Database-oriented DSS, Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS, and pound DSS.
A pound 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 (1996).
The support given by DSS can be separated into three distinct. interrelated categories (Hackathorn and Keen, 1981): Personal Support, Group Support and anizational Support.
Additionally, I classify DSS in a similar way. The build up of a DSS is classified into a few characteristics. 1) inputs: this is used so the DSS can he 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/Feedback: 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.
Applications
As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.
One 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 petitive with their costs.
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 panies were experiencing an increase.
DSS has many applications that he already been spoken about. However, it can be used in any field where anization 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. DSS has endless possibilities that can be used anywhere and anytime, for its decision making needs.
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