Abstract
Modern organizations are faced with numerous information management challenges in an increasingly complex and dynamic environment. Vast amounts of data and myriads of models of reality are routinely used to predict key outcomes. Decision support systems (DSS) play a key role in facilitating decision making through management of quantitative models, data, and interactive interfaces (Power, 2000). The basic thrust of such applications is to enable decision-makers to focus on making decisions rather than being heavily involved in gathering data and conceiving and selecting analytical decision models. Accordingly, the number and complexity of decision models and of modeling platforms has dramatically increased, rendering such models a corporate (and national) resource (Muhanna & Pick, 1994). Further, Internet technology has brought many new opportunities to conduct business electronically, leading to increased globalization. Managers and decision makers are increasingly collaborating in distributed environments in order to make efficient and effective use of organizational resources. Thus, the need for distributed decision support in general, and model sharing and reuse in particular, is greater today than ever before. This has attracted significant attention from researchers in information systems-related areas to develop a computing infrastructure to assist such distributed model management (Krishnan & Chari, 2000). In this article, we focus on distributed model management advances, and the discussion is organized as follows. The next section provides a background on model management systems from a life-cycle perspective. This is followed by a critical review of current research status on distributed decision support systems from a model management viewpoint with a particular emphasis on Web services. Future trends in this area are then discussed, followed by concluding remarks.
Key Terms in this Chapter
Decision Support Systems (DSS): A decision support system is a computer-based system for supporting decision makers confront semistructured and ill-structured problems by leveraging data, knowledge, and models.
Service Oriented Architecture (SOA): The focus of SOA is to expose application logic as loosely coupled services. Design principles underlying SOA emphasize reuse, abstraction, autonomy, loose coupling, statelessness, composability, and discoverability.
Distributed Model Management Systems (DMMS): DMMS are systems for managing models in a distributed environment where models, solvers, and client applications may be distributed over multiple heterogeneous platforms
Model Management (MM): Analogous to data management, refers to the ability to create, retrieve, update, delete, and manipulate models. It also includes the need for managing models for integrity, consistency, security, and currency
Model Management Systems (MMS): MMS are computer-based systems for supporting MM functions including but not limited to searching, selecting, retrieving, and composing models. MMS provide a way to access and manage various modeling resources and the modeling life cycle.
Services: Services are independent building blocks that collaborate to deliver application functionality.
Web Services (WS): Web services are services that utilize XML and Web services technologies such as SOAP, WSDL, and UDDI to represent a Web-based implementation of an SOA.