Progress in Web-based decision support technologies

1. Progress in Web-based decision support technologies Decision Support Systems 43 (2007) 1083– 1095 Hemant K. Bhargava , Daniel J. Power , Daewon Sun Available online 8 August 2005 授課老師：林娟娟 教授 報告學生：簡子晴、李建翰 1

2. Content • Introduction • Historical perspective • The typologies of DSS • Web-based decision support • Web-enabled decision support • Recent research in Web-based decision support • Conclusions 2

3. Introduction DSS(Decision Support System) • These systems support complex non-routine decisions. • Primary purpose to process data into information • DSS systems are typically employed by tactical level management whose decisions and what-if analysis are less structured. • This information system not only presents the results but also expands the information with alternatives. • Some DSS methodologies • Mathematical Modeling • Simulation • Queries • What-If (OLAP-Cubes) • Datamining 3

4. Introduction • Current DSS facilitate a wide variety of decision tasks: • information gathering and analysis • model building • sensitivity analysis • collaboration • alternative evaluation • decision implementation. • The growing Web, the on-going Web-based DSS such as health care, private companies, government, and education 4

5. Introduction • A Web-based DSS • delivers decision support to a manager or business analyst • using a “thin-client” Web browser interface (Java applets or JavaScript) • since 1995 ( the discussion of the idea at the 3rd ISDSS in HK. ) • This article expands and updates Bhargava and Power’s [5] status report on DSS and Web technologies in 2001. • Section2- a brief historical overview • Section3- the extent to which Web technologies impact • Section4- State of Practice • Section5- State of Research 5

6. Historical perspective 6

7. Historical perspective 7

8. The typologies of DSS • Implemented approaches • data-driven DSShelp managers organize, retrieve, and analyze large volumes of relevant data using database queries and OLAP techniques • model-driven DSSuse formal representations of decision models and provide analytical support using the tools of decision analysis, optimization, stochastic modeling, simulation, statistics, and logic modeling • Web technologies • Communication-driven DSS • Knowledge-driven DSS • document-driven DSS 8

9. Web-based decision support • Decision technologies as services • Web services • Messaging protocols such as SOAP • several XML-related languages • applications for data interchange • The prior articles • Web-enablement of model-driven DSS (Bhargava and Krishnan [4]) • Web-enabling DSS (cf., [15,16,3 8,43]) • Web-enabled data-intensive applications (Fraternali [22]) • geographic information systems tools (Coddington et al. [14]) • optimization and alternative ways (Fourer and Goux [21]) • using a company intranet (Sridhar [51]) 9

10. Web-based decision support • Relevant technologies (Bhargava and Krishnan [4]) • server-side implementation • CGI, Java applications, server-side scripting languages, Active Server pages, PHP, and Java server pages • client-side implementation • scripting languages, Java applets, ActiveX controls, and browser plugins • distributed implementation • CORBA, COM+, Java RMI, and Enterprise Java Beans

11. Web technologies and decision support tasks Application-specific DSS vs. DSS generators Web-based decision support

12. Web-enabled decision support • Web as media • DSS Resources、The OLAP Report、Data Warehousing Online (Decision support portals) • Teradata University Network (teaching and learning resource related to data warehousing) • IBM OSS COIN (similar portals) • Web as computer • Digital product demonstrations (QuickTime movies or Shockwave animation) • Previewing a decision support product using online interactive (Lumina、TreeAge、HDS) • On-line, Web-based Decision Support Systems (OptAmaze.com、Grazing Systems)

13. Web-enabled decision support

14. Recent research in Web-based decision support • Architectures and technologies • Gregg et al. developed a DSS metadata model for distributing decision support systems on the Web • Bharati and Chaudhury conducted an empirical study to investigate customers’ satisfaction with a Web-based decision support system (system quality, information quality, and information presentation) • Iyer et al. studied model management for decision support in a computing environment where enterprise data and models are distributed(VBE) • Gu¨ ntzer et al. proposed Structured Service Models that use a variant of structured modeling • Zhang and Goddard applied Software Architectures to the design of Web-based DSS (3CoFramework->NADSS) • Mitra and Valente provided an overview of Web-based optimization for model-driven decision support (ASP and e-Services)

15. Recent research in Web-based decision support • Architectures and technologies • Research indicates • Web users need detailed information about DSS to organize and understand the available content • Systems should be designed to include constructs and artifacts that support delivery of high-quality information • New approaches for model management are needed that facilitate storage, search, retrieval, matching, and composition from a library of decision models

16. Recent research in Web-based decision support • Applications and implementations • Kohli et al. reported a case study of a Web-based DSS for hospital management called Physician Profiling System (PPS) • Ngai and Wat developed and implemented a Web-based DSS that used a model based on fuzzy set theory to perform risk analysis for e-commerce development • Dong et al. developed a Web-based DSS framework for portfolio selection (OLAP&PVM->WPSS) • Sundarraj identified key issues in managing service contracts and developed a prototype that can support a manager’s planning process • Ray reported a case study that demonstrates the implementation of Web-based decision support technologies (DelDOT) • Liou et al. discussed the development and implementation of a Web-based Group DSS, Team-Spirit(GDSS’s) • Delen et al. developed a Web-based DSS, called Movie Forecast Guru, to help decision makers in the movie industry • Others, Barton(SDM)、Walton(9/11)

17. Conclusions • Web technologies provide • Platform-independent • Remote and distributed computation • The exchange of complex multimedia information • System maintenance is • simplified and centralized • letting end users focus on problem analysis and decision making • Challenges • Technological challenges • Economic challenges • Social and behavioral challenges 17

18. Conclusions • Web Technological challenges • architectural model • random jumps in hyperspace • limitations – cookies, embedding models or data within a Web page, using Java applet • limitations of the Web browser HTTP, round-trip network delays, “pull” nature • Further research • determine guidelines for the use of alternative technologies • understand what technologies may be most effective 18

19. Conclusions • Economic challenges • Few ways to sell decision support services • A few firms have well-defined revenue models • optAmaze.com • subscription-based model – trim optimization service • charging differential prices based - the number of machines optimized • salesforce.com • Web-based CRM tools 19

20. Conclusions • Social and behavioral challenges • user interfaces ( traditional vs. Web browser ) • DSS researchers • focus on adoption, utilization and performance • learn for increasing the satisfaction of customers and suppiers • Rethink our assumptions and determine if correct • Integration from stand-alone to web 20

21. The End Thanks for your listening