Development and Design Methodologies in DWM

Development and Design Methodologies in DWM

James Yao (Montclair State University, USA), John Wang (Montclair State University, USA), Qiyang Chen (Montclair State University, USA) and June Lu (University of Houston - Victoria, USA)
DOI: 10.4018/978-1-59904-843-7.ch028
OnDemand PDF Download:
$37.50

Abstract

Information systems were developed in early 1960s to process orders, billings, inventory controls, payrolls, and accounts payables. Soon information systems research began. Harry Stern started the “Information Systems in Management Science” column in Management Science journal to provide a forum for discussion beyond just research papers (Banker & Kauffman, 2004). Ackoff (1967) led the earliest research on management information systems for decision-making purposes and published it in Management Science. Gorry and Scott Morton (1971) first used the term decision support systems (DSS) in a paper and constructed a framework for improving management information systems. The topics on information systems and DSS research diversifies. One of the major topics has been on how to get systems design right.

Key Terms in this Chapter

Web-Based DSS: This is “a computerized system that delivers decision support information or decision support tools to a manager or business analyst using a ‘thin-client’ Web browser like Netscape Navigator of Internet Explorer. The computer server that is hosting the DSS application is linked to the user’s computer by a network with the TCP/IP protocol….Web-Based DSS can be communications-driven, data-driven, document-driven, knowledge-driven, model-driven or a hybrid.”

Metric-Drive Design: Metric-drive design is a data warehousing design approach which begins by defining key business processes that need to be measured and tracked over time. Then they are modeled in a dimensional model.

Entity-Relationship Data Model: An entity-relationship data model is a model that represents database schema as a set of entities and the relationships among them.

Star Schema: Star schema is a modeling diagram that contains a large central table (fact table) and a set of smaller attendant tables (dimension tables) each represented by only one table with a set of attributes.

Business Intelligence (BI): Abbreviated BI, business intelligence “is a popularized, umbrella term introduced by Howard Dresner of the Gartner Group in 1989 to describe a set of concepts and methods to improve business decision making by using fact-based support systems. The term is sometimes used interchangeably with briefing books and executive information systems. A Business Intelligence System is a data-driven DSS.” Decision support’s purpose is to provide managers with information or business intelligence.

Dimensional Model: A dimensional model contains a central fact table and a set of surrounding dimension tables, each corresponding to one of the components or dimensions of the fact table.

What-If Analysis: This is “the capability of ‘asking’ the software package what the effect will be of changing some of the input data [decision variables] or independent variables.” In a model-driven DSS, a decision variable is a changing factor in the model that is determined by a decision maker. The presence of this capability helps identify a model-driven DSS.

Fact Table: A fact table is the central table in a star schema, containing the names of the facts, or measures, as well as keys to each of the related dimension tables.

Executive Information Systems (EISs): “An EIS is a computerized system intended to provide current and appropriate information to support decision making for [senior] managers using a networked workstation. The emphasis is on graphical displays and an easy to use interface...”

Online Analytical Processing (OLAP): “OLAP is software for manipulating multidimensional data from a variety of sources that has been stored in a data warehouse. The software can create various views and representations of the data. OLAP software provides fast, consistent, interactive access to shared, multidimensional data.”

Materialized View: Mmaterialized view are copies or replicas of data based on SQL queries created in the same manner as dynamic views (Hoffer et al., 2007, p. 298).

Knowledge Discovery in Databases (KDD): KDD is the process of extrapolating information from a database, from the identification of the initial business aims to the application of the decision rules (Giudici, 2003, p. 2).

Parallel Processing: Parallel processing is the allocation of the operating system’s processing load across several processors (Singh, 1998, p. 209).

Data: Data are “binary (digital) representations of atomic facts,” especially from financial transactions. Data may also be “text, graphics, bit-mapped images, sound, analog or digital live-video segments.” Structured data are the “raw material” for analysis using a data-driven DSS. The data are “supplied by data producers and [are] used by managers and analysts to create information.”

Decision Support Systems (DSSs): DSSs are a specific “class of computerized information system that support decision-making activities.” “A DSS is an interactive computer-based system or subsystem intended to help decision makers use communications technologies, data, documents, knowledge and/or models to identify and solve problems…and make decisions….Five more specific Decision Support System types include: Communications-driven DSS, Data-driven DSS, Document-driven DSS, Knowledge-driven DSS, and Model-driven DSS.”

Dimensions: Dimensions are the perspectives or entities with respect to which an organization wants to keep records (Han & Kamber, 2006, p. 110).

Complete Chapter List

Search this Book:
Reset