Decision support systems have experienced a marked increase in attention and importance over the past 25 years. The aim of this book is to survey the decision support system (DSS) field – covering both developed territory and emergent frontiers. It will give the reader a clear understanding of fundamental DSS concepts, methods, technologies, trends, and issues. It will serve as a basic reference work for DSS research, practice, and instruction. To achieve these goals, the book has been designed according to a ten-part structure, divided in two volumes with chapters authored by well-known, well-versed scholars and practitioners from the DSS community.
Packed with essential information, this valuable volume helps future business management professionals learn to make and support managerial decisions, providing a thorough understanding of the support aspect of DSS. Written from a cognitive processes and decision-making perspective, it concentrates on issues that emphasize managerial applications and the implication of decision support technology on those issues. The volume examines data warehouses, intelligent software agents and DSS system development, as well as an introduction to decision support systems, decision in the organization, modeling decision processes, group decision support and groupware technologies, executive information systems, expert systems and artificial intelligence, knowledge engineering and acquisition, and data mining and data visualization. For Data Warehouse Administrators, CIO and Directors of Information Systems.
Decision Support Systems: Frequently Asked Questions is the authoritative reference guide to computerized Decision Support Systems. Author Dan Power has spent almost 30 years building, studying and teaching others about computerized Decision Support Systems. Dr. Power is first and foremost a Decision Support evangelist and generalist. From his vantage point as editor of DSSResources.COM, he tracks a broad range of contemporary DSS topics. In this DSS FAQ, Dr. Power answers 83 frequently asked questions about computerized decision support systems. The FAQ covers a broad range of contemporary topics and the questions are organized into 8 chapters. DSS FAQ helps readers understand questions like: What is a DSS? What kind of DSS does Mr. X need? Does data modeling differ for a Data-Driven DSS? Is a Data Warehouse a DSS? Is tax preparation software an example of a DSS? What do I need to know about Data Warehousing/OLAP? What is a cost estimation DSS? What is a Spreadsheet-based DSS? Decision Support Systems: Frequently Asked Questions is a useful resource for IT specialists, students, professors and managers. It organizes important Ask Dan! questions (with answers) published in DSS News from 2000 through 2004.
Annotation Researchers, business people and policy makers have recognized the importance of addressing technological, economic and social impacts in conjunction. For example, the rise and fall of the dot-com hype depended on the strength of the business model, on the technological capabilities avalable to firms and on the readiness of the society and economy, at large, to sustain a new breed of business activity. Social and Economic Transformation in the Digital Era addresses this challenge by assembling the latest thinking of leading researchers and policy makers in key subject areas of the information society and presents innovative business models, case studies, normative theories and social explanations.
This book disseminates the current trends among innovative and high-quality research regarding the implementation of conceptual frameworks, strategies, techniques, methodologies, informatics platforms and models for developing advanced industrial tools and techniques and their application in different fields. It presents a collection of theoretical, real-world and original research works in the field of applied industrial tools and techniques. The text goes beyond the state-of-the-art in the field of industrial and software engineering, listing successful applications and use cases of studies of new approaches, applications, methods, techniques for developing advanced industrial tools, methodologies and techniques and their application in different fields. The topics covered in this book are of interest to academics, researchers, students, stakeholders and consultants.
The rapid development of information communication technologies (ICTs) is having a profound impact across numerous aspects of social, economic, and cultural activity worldwide, and keeping pace with the associated effects, implications, opportunities, and pitfalls has been challenging to researchers in diverse realms ranging from education to competitive intelligence.
This book presents recent advances on hybrid intelligent systems using soft computing techniques for intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain groups of papers around a similar subject. The first part consists of papers with the main theme of hybrid intelligent systems for control and robotics, which are basically state of the art papers that propose new models and concepts, which can be the basis for achieving intelligent control and mobile robotics. The second part contains papers with the main theme of hybrid intelligent systems for pattern recognition and time series prediction, which are basically papers using nature-inspired techniques, like evolutionary algorithms, fuzzy logic and neural networks, for achieving efficient pattern recognition or time series prediction. The third part contains papers with the theme of bio-inspired and genetic optimization methods, which basically consider the proposal of new methods and applications of bio-inspired optimization to solve complex optimization of real problems. The fourth part contains papers that deal with the application of intelligent optimization techniques in real world problems in scheduling, planning and manufacturing. The fifth part contains papers with the theme of evolutionary methods and intelligent computing, which are papers considering soft computing methods for applications related to diverse areas, such as natural language processing, recommending systems and optimization.
With the new federal law, No Child Left Behind, there is ever increasing pressure on schools to be accountable for improving student achievement. That pressure is taking the form of focused efforts around data-driven decision making. However, very little is known about what data-driven decision making can really tell one about improving achievement nor is there a full explanation available about what it really takes to do this work. The few examples that do exist, while proposing to get at some of these issues, make huge assumptions about educators' knowledge base and available resources necessary for success. In this book, Philip Streifer fills the gaps by laying out how this work can be done and then explains what is knowable when one actually conducts these analyses and what follow-up steps are needed to make true improvements. He provides readers with a comprehensive understanding of what data-driven decision making can and cannot tell educators about student achievement and addresses the related issues for leadership, policy development, and accountability. Senior level district administration for policy development, school level administrators who have to put policy into practice, and graduate college professors teaching data-driven decision making will find this book most useful.
