A Review On Evaluation And Benefits Of Decision Support Systems <s> Document-driven DSS <s> Abstract Organizational decisions of the future may include social, environmental, and economic concerns, and be much more “wicked” [Policy Sciences, 4 (1973) 155], complex and interconnected than those of the past. Organizations and their decision support systems must embrace procedures that can deal with this complexity and go beyond the technical orientation of previous DSS. Singerian inquiring organizations [Australian Journal of Information Systems, 6 (1) (1998) 3; http://www.cba.uh.edu/~parks/fis/fis.htm (1998); Proceedings of 3rd Americas Conference on Information Systems, Indianapolis, August 1997, p. 293; Proceedings of the 1999 Meeting of the America's Conference on Information Systems, Milwaukee, August 1999; Special Issue of Information Systems Frontiers on Philosophical Reasoning in Information Systems Research (in press)], based on Churchman's [The Design of Inquiring Systems: Basic Concepts of Systems and Organization, Basic Books, New York, NY, 1971] inquiring systems and Mitroff and Linstone's [The Unbounded Mind: Breaking the Chains of Traditional Business Thinking, Oxford Univ. Press, New York, 1993] unbounded systems thinking (UST), are designed to deal with wicked decision situations. This paper discusses DSS and knowledge management in Singerian organizations and calls for a new decision-making paradigm for DSS. <s> BIB001 </s> A Review On Evaluation And Benefits Of Decision Support Systems <s> Document-driven DSS <s> (NOTE: Each chapter ends with Lessons Learned.) Preface. Acknowledgments. About the Author. I. THE RUBBER MEETS THE ROAD. 1. Introduction. Knowledge Management: In Search of Alchemy. What This Book Is About. 2. The Knowledge Edge. Making Sense of Nonsense. Intellectual Capital. The Drivers of Knowledge Management. Creating the Knowledge Edge. 3. The Origins of Knowledge. From Data to Information to Knowledge. From Data to Knowledge. Classifying Knowledge. The Three Fundamental Processes. Taming the Tiger's Tail. Business and Knowledge. II. THE ROAD AHEAD: IMPLEMENTING KNOWLEDGE MANAGEMENT. 4. The 10-Step Knowledge Management Road Map. The 10-Step Knowledge Management Road Map. Phase 1. Infrastructual Evaluation. Phase 2. Knowledge Management System Analysis, Design, and Development. Phase 3. Deployment. Phase 4. Metrics for Evaluation. IIA. THE FIRST PHASE: INFRASTRUCTURE EVALUATION AND LEVERAGE. 5. The Leveraged Infrastructure. Leveraging What Exists. Leveraging the Internet. The Knowledge Platform: A 10,000-Fott View. 6. Aligning Knowledge Management and Business Strategy. Strategic Visioning. Knowledge Transfer versus Integration: The Strategic Dichotomy. Real Options Under Uncertainly. The Responsiveness Quadrahedron: Variety and Speed. Business Models and Executability. Codification or Personalization? Knowledge Maps to Link Knowledge to Strategy. Strategic Imperatives for Successful Knowledge Management. Strategic Imperatives for Successful Knowledge Management. Assessing Focus. Detecting Lost Opportunities. IIB. THE SECOND PHASE: KM SYSTEM ANALYSIS, DESIGN, AND DEVELOPMENT. 7. The Knowledge Management Platform. Technology Components of the Knowledge Management. The Seven-Layer Knowledge Management System Architecture. Foundation for the Interface Layer. The Web or Proprietary Platforms? Collaborative Intelligence and Filtering Layer. Knowledge Management Platforms versus Other Enterprise Systems. The Application Layer. The Promise of Peer-to-Peer Knowledge Networks. 8. Knowledge Audit and Analysis. Hindsight, Insight, and Foresight. Measuring Knowledge Growth. The Knowledge Audit Team. Conducting the Knowledge Audit. Choosing Your Company's Knowledge Niches. 9. Designing the Knowledge Management Team. Sources of Expertise. Team Composition and Selection Criteria. Team Life Span and Sizing Issues. The KM Team's Project Space. Chemistry. Highways to Failure. 10. Creating the Knowledge Management System Blueprint. The Knowledge Management Architecture. Components of a Knowledge Management System. Integrative and Interactive Knowledge Applications. Build or Buy? User Interface Design Considerations. A Network View of the KM Architecture. Future-Proofing the Knowledge Management System. 11. Developing the Knowledge Management System. The Building Blocks: Seven Layers. The Interface Layer. A Live Walkthrough: Urban Motors. The Access and Authentication Layer. The Collaborative Filtering and Intelligence Layer. The Application Layer. The Transport Layer. The Middleware and Legacy Integration Layer. The Repository Layer. IIC. THE THIRD PHASE: DEPLOYMENT. 12. Prototyping and Deployment. Moving from Firefighting to Systems Deployment. Legacy Deployment Methods. The Results-Driven Incremental Methodology. 13. Leadership and Reward Structures. From the Chief Information Officer. The Successful Knowledge Leader. Reward Structures to Ensure Knowledge Management Success. IID. THE FINAL PHASE AND BEYOND: REAL OPTIONS ANALYSIS FOR PERFORMANCE. 14. Real-Options Analysis for Knowledge Valuation. The Limitation of Traditional Metrics. Real-Options Analysis. Measuring Inputs for Real-Options Models. III. SIDE ROADS: APPENDICES (ON THE CD-ROM). Digital Appendix A. The Knowledge Management Assessment Kit. Digital Appendix B. Alternative Schemes For Structuring the Knowledge Management Platform Front End. Digital Appendix C. Software Tools. Endnotes. Bibliographic References and Further Reading. Glossary. Index. About the CD. <s> BIB002 </s> A Review On Evaluation And Benefits Of Decision Support Systems <s> Document-driven DSS <s> In an organization, knowledge is the most important resource in the creation of core competitive advantages. It is circulated and accumulated by knowledge flows (KFs) in the organization to support workers' task needs. Because workers accumulate knowledge of different domains, they may cooperate and participate in several task-based groups to satisfy their needs. In this paper, we propose algorithms that integrate information retrieval and data mining techniques to mine and construct group-based KFs (GKFs) for task-based groups. A GKF is expressed as a directed knowledge graph which represents the knowledge referencing behavior, or knowledge flow, of a group of workers with similar task needs. Task-related knowledge topics and their relationships (flows) can be identified from the knowledge graph so as to fulfill workers' task needs and promote knowledge sharing for collaboration of group members. Moreover, the frequent knowledge referencing path can be identified from the knowledge graph to indicate the frequent knowledge flow of the workers. To demonstrate the efficacy of the proposed methods, we implement a prototype of the GKF mining system. Our GKF mining methods can enhance organizational learning and facilitate knowledge management, sharing, and reuse in an environment where collaboration and teamwork are essential. <s> BIB003
A document-driven DSS provides the decision-maker with relevant information coming from a store of documents. This category of DSS has arisen more recently than other categories such as data-driven or modeldriven DSS. Examples of document stores that could usefully drive decision-making processes would include news articles, user reviews or comments on websites, or emails. Also, many knowledge management systems such as BIB002 and BIB001 are in this category. Such systems are often searchable collections of documents embodying "knowledge" submitted by members of a community of practice within an organization. A recent document-driven DSS is described by BIB003 . The benefit of their system is to facilitate "knowledge reuse, cooperation and sharing among workers who perform similar … tasks." Extending their previous work on mining an individual's knowledge flows (KFs), they develop an algorithm for mining and constructing a group KF. Using a dataset containing information about fourteen knowledge workers, the authors develop a prototype system to illustrate the algorithm proposed. The system performs worker cluster and group-based KF mining, omitting only the identification of knowledge-referencing paths phase of their proposed method. The benefits provided by group KF mining include facilitation of knowledge cooperation, sharing and reuse, as well as providing a useful reference for a novice when performing tasks.