WDSS: Web-Based Decision Support Systems

2019 ◽  
pp. 238-244
Author(s):  
Abdullah Manal ◽  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Web Based

Web-Based Collaborative Spatial Decision Support Systems

2011 ◽  
pp. 285-315 ◽  
Author(s):  
Songnian Li
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
System Design ◽  
Support Systems ◽  
Spatial Decision Support ◽  
Spatial Decision Support Systems ◽  
Web Based ◽  
Web Technologies ◽  
And Performance ◽  
The Web

The rapidly expanding range of Web technology has made it possible to collaboratively make decisions over the Web. This chapter examines some of these Web technologies important to the development of collaborative spatial decision support systems, and identifies their technology impediments and strengths. The outcomes provide a basis for discussing how the existing collaborative spatial decision support systems may be redesigned to take advantage of new Web technologies, and how new collaborative spatial decision support systems may be designed and developed in this Web-based paradigm. Some discussions on selected design and development issues that are important to the development of collaborative spatial decision support systems including system design, user’s impact, and performance are presented.

Web-based customer decision support systems

1998 ◽  
Vol 41 (3) ◽  
pp. 71-78 ◽  
Author(s):  
Robert M. O'Keefe ◽  
Tim McEachern
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Web Based

Special issue of Decision Support Systems on web-based decision support

2007 ◽  
Vol 43 (4) ◽  
pp. 1081-1082
Author(s):  
Hemant K. Bhargava ◽  
Daniel J. Power ◽  
Daewon Sun
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Special Issue ◽  
Web Based

A framework of Web-based Decision Support Systems for portfolio selection with OLAP and PVM

2004 ◽  
Vol 37 (3) ◽  
pp. 367-376 ◽  
Author(s):  
Jichang Dong ◽  
Helen S. Du ◽  
Shouyang Wang ◽  
Kang Chen ◽  
Xiaotie Deng
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Portfolio Selection ◽  
Support Systems ◽  
Web Based

scholarly journals Critical Success Factors influencing Web-Based Decision Support Systems in Online Shopping Context

2014 ◽  
Vol 8 (3) ◽  
pp. 1364-1371
Author(s):  
Mohammed A. I. Ayoub
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Critical Success Factors ◽  
Success Factors ◽  
Online Shopping ◽  
Support Systems ◽  
Quality Data ◽  
Web Based ◽  
Critical Success

Web-based decision support systems are increasingly used over the past years. However, few studies have been conducted on evaluation of web-based decision support systems especially in the field of online shopping. This paper attempts to explore the critical success factors that influence decision making satisfaction in online shopping context by providing a conceptual model for this purpose. Although there are various factors which contribute in making online shopping decisions but this study focuses on five factors i.e. web site quality, data quality, knowledge management, decision making satisfaction, and perceived net benefit. Also, this research will use existing models that explain and predict information systems success. However, these success models need to be updated to recurrent industry developments since the updating existing IS success models, a better understanding of web-based DSS practitioner success can be achieved.

Development of a Web-Based Intelligent Spatial Decision Support System (WEBISDSS)

2011 ◽  
pp. 184-202 ◽  
Author(s):  
Ramanathan Sugumaran ◽  
Shriram Ilavajhala ◽  
Vijayan Sugumaran
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Support Systems ◽  
Spatial Decision Support System ◽  
Spatial Decision Support ◽  
Web Based ◽  
Extensible Markup ◽  
Intelligent Decision Support Systems

A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.

Intelligent Expert Decision Support Systems

2021 ◽  
pp. 510-531
Author(s):  
Abdel-Badeeh M. Salem ◽  
Tetiana Shmelova
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Decision Support Systems ◽  
Knowledge Engineering ◽  
Support Systems ◽  
Case Based Reasoning ◽  
Web Based ◽  
Engineering Research ◽  
New Generation ◽  
Application Developers

In this chapter, the authors present Intelligent Expert Decision Support Systems (IEDSSs) technology and conceptual models of Expert systems(ES) for Human-Operator (H-O) of different areas and Air Navigation System (ANS) too. The authors demonstrate some interesting applications of IEDSS. Intelligent Expert Decision Support Systems technology is a challenging field that has witnessed great advances in the last few years. Artificial intelligence (AI) theories and approaches receive increasing attention within this emerging technology .Researchers have been used the AI concepts and theories to develop a robust generation of IEDSSs. Moreover, the convergence of AI technologies and web technologies (WT) is enabling the creation of a new generation of web-based IEDSSs for all domains and tasks. This chapter discusses the AI methodologies and techniques for developing the IEDSSs. Two most popular paradigms are discussed namely; case-based reasoning and ontological engineering. Moreover, the chapter addresses the challenges faced by the application developers and knowledge engineers in developing and deploying AI-based expert decision support systems. In addition, the chapter presents some examples of ES by the author and colleagues at National Aviation University, Ukraine and some cases of IEDSSs developed by the author and his colleagues at Artificial intelligence and Knowledge Engineering Research Labs, Ain Shams University, AIKE Labs-ASU, Cairo, Egypt.

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