Web-Based Decision Support for Knowledge Sharing and Management in Intelligent Buildings Assessment

This article presents the knowledge-oriented information visualization (KIV) approach to facilitating the utilization of building rating systems at post-assessment stage. The KIV approach is introduced by using a Web-based decision support model. The model consists of several toolkits, including a case base of intelligent buildings to support the application of sustainable technologies, a Web-oriented information visualization toolkit for intelligent buildings assessment, and a geographical information system (GIS) toolkit for knowledge reuse in buildings variations. A case study is used to demonstrate how the KIV approach can be applied to support decision making at the post-assessment stage of intelligent buildings.

Software Agents

The origins of the software agent concept are often traced back to the pioneers of artificial intelligence—John Mc Carthy, the creator of LISP programming language, and Carl Hewitt, the father of distributed artificial intelligence (DAI). Kay (1984, p. 84) states that: …the idea of an agent originated with John McCarthy in the mid-1950s, and the term was coined by Oliver G. Selfridge a few years later, when they were both at the Massachusetts Institute of Technology. They had in view a system that, when given a goal, could carry out the details of the appropriate computer operations and could ask for and receive advice, offered in human terms, when it was stuck. An agent would be a ‘soft robot’ living and doing its business within the computer’s world. Nwana (1996, p. 205), on the other hand, claims that: …software agents have evolved from multi-agent systems (MAS), which in turn form one of three broad areas which fall under DAI, the other two being Distributed Problem Solving (DPS) and Parallel Artificial Intelligence (PAI). (…) The concept of an agent (…) can be traced back to the early days of research into DAI in the 1970s – indeed, to Carl Hewitt’s concurrent Actor model. In this model, Hewitt proposed the concept of a self-contained, interactive and concurrently-executing object which he termed ‘Actor’. This object had some encapsulated internal state and could respond to messages from other similar objects1. The software agent concept meant, in the first place, replacing the idea of an expert, which was at the core of earlier support systems, with the metaphor of an assistant. Until 1990s, decision support systems (DSS) were typically built around databases, models, expert systems, rules, simulators, and so forth. Although they could offer considerable support to the rational manager, whose decision making style would rely on quantitative terms, they had little to offer to managers who were guided by intuition. Software agents promised a new paradigm in which DSS designers would aim to augment the capabilities of individuals and organizations by deploying intelligent tools and autonomous assistants. The concept thus heralded a pivotal change in the way computer support is devised. For one thing, it called for a certain degree of intelligence on the part of the computerized tool; for another, it shifted emphasis from the delivery of expert advice toward providing support for the user’s creativity (King, 1993).

Rough Set Theory

Rough set theory (RST), since its introduction in Pawlak (1982), continues to develop as an effective tool in classification problems and decision support. In the majority of applications using RST based methodologies, there is the construction of ‘if .. then ..’ decision rules that are used to describe the results from an analysis. The variation of applications in management and decision making, using RST, recently includes discovering the operating rules of a Sicilian irrigation purpose reservoir (Barbagallo, Consoli, Pappalardo, Greco, & Zimbone, 2006), feature selection in customer relationship management (Tseng & Huang, 2007) and decisions that insurance companies make to satisfy customers’ needs (Shyng, Wang, Tzeng, & Wu, 2007). As a nascent symbolic machine learning technique, the popularity of RST is a direct consequence of its set theoretical operational processes, mitigating inhibiting issues associated with traditional techniques, such as within-group probability distribution assumptions (Beynon & Peel, 2001). Instead, the rudiments of the original RST are based on an indiscernibility relation, whereby objects are grouped into certain equivalence classes and inference taken from these groups. Characteristics like this mean that decision support will be built upon the underlying RST philosophy of “Let the data speak for itself” (Dunstch & Gediga, 1997). Recently, RST was viewed as being of fundamental importance in artificial intelligence and cognitive sciences, including decision analysis and decision support systems (Tseng & Huang, 2007). One of the first developments on RST was through the variable precision rough sets model (VPRSß), which allows a level of mis-classification to exist in the classification of objects, resulting in probabilistic rules (see Ziarko, 1993; Beynon, 2001; Li and Wang, 2004). VPRSß has specifically been applied as a potential decision support system with the UK Monopolies and Mergers Commission (Beynon & Driffield, 2005), predicting bank credit ratings (Griffiths & Beynon, 2005) and diffusion of medicaid home care programs (Kitchener, Beynon, & Harrington, 2004). Further developments of RST include extended variable precision rough sets (VPRSl,u), which infers asymmetric bounds on the possible classification and mis-classification of objects (Katzberg & Ziarko, 1996), dominance-based rough sets, which bases their approach around a dominance relation (Greco, Matarazzo, & Slowinski, 2004), fuzzy rough sets, which allows the grade of membership of objects to constructed sets (Greco, Inuiguchi, & Slowinski, 2006), and probabilistic bayesian rough sets model that considers an appropriate certainty gain function (Ziarko, 2005). A literal presentation of the diversity of work on RST can be viewed in the annual volumes of the Transactions on Rough Sets (most recent year 2006), also the annual conferences dedicated to RST and its developments (see for example, RSCTC, 2004). In this article, the theory underlying VPRSl,u is described, with its special case of VPRSß used in an example analysis. The utilisation of VPRSl,u, and VPRSß, is without loss of generality to other developments such as those referenced, its relative simplicity allows the non-proficient reader the opportunity to fully follow the details presented.

The Method of Appraising Needs and Possibilities of Activity-Based Costing Implementation

Changes that occur in the environment of a given company and a constantly growing competition make the managers enhance the management system. The managers consider which management-support methods and tools could appear effective in fighting competition and ensure the company’s survival or development. Introducing a new management method or tool is in general a complex project, incorporating many aspects and frequently calling for considerable changes in the company’s IT system. Taking up the right decision needs thorough consideration of all the factors supporting or rejecting the project. The decision-making process concerning the implementation of a specific method or tool should address the following questions: • Does, in a given company, there exist objective reasons for which the implementation of a given method would be recommended? • What are the possibilities existing in the company which would make the implementation success realistic? • What tangible benefits will be available due to the new method implementation, in other words, what is the effectiveness of the project? To cut a long story short, one shall determine whether the change is needed, whether it is possible, and whether it will be beneficial to the business. Replying to the third question, different kinds of investment project effectiveness evaluation methods are used, including the internal return rate (IRR) or the net present value (NPV). Their application for the management system improvement project, however, faces many difficulties and can be very time consuming. Yet, one shall remember that before an attempt is made at replying to the third question, positive answers to the first two questions must be given.

How Groupware Systems Can Change How an Organisation Makes Decisions

In this article, a newspaper company which has implemented a computerised editorial system is studied in an attempt to understand the impact that groupware systems can have on the decision making processes of an organisation. First, the case study protocol is presented, and the findings of the case are described in detail. Conclusions are then presented which pertain both to this case and to the implementation of decision support systems that have a groupware dimension.

Exploring the Risks That Affect Community College Decision Makers

For leaders, decision making is a charge that cannot be escaped. For those who prefer to avoid this responsibility, the startling truth is that not making a decision is a decision. Executives, including those who lead community colleges, have critical accountability to build a support network with easy access to pertinent information that carries out decisions as intended. Decision making’s impending risks—particularly in this age of “I need it yesterday”—are amplified by the likelihood of misunderstanding and miscommunication. The man-hours of gathering, analyzing, and prioritizing information behind a good decision can be thwarted without a clear-cut strategy for how to make a decision with that information. This chapter provides insights as to why a United States community college organization’s leadership faltered as a result of decision making. For this domain, this long-neglected dynamic of identifying operational risks was explored using a tailored risk management methodology developed by the Software Engineering Institute (SEI). Community colleges, federal agencies, and small businesses have similar concerns about institutionalizing effective decision making; this chapter addresses those complexities specifically within community colleges and provides an understanding of managerial decision making at the executive level.

The Evaluation of Decision-Making Support Systems' Functionality

Contemporary decision-making support systems (DMSSs) are large systems that vary in nature, combining functionality from two or more classically defined support systems, often blurring the lines of their definitions. For example, in practical implementations, it is rare to find a decision support system (DSS) without executive information system (EIS) capabilities or an expert system (ES) without a recommender system capability. Decision-making support system has become an umbrella term spanning a broad range of systems and functional support capabilities (Alter, 2004). Various information systems have been proposed to support the decision-making process. Among others, there are DSSs, ESs, and management support systems (MSSs). Studies have been conducted to evaluate the decision effectiveness of each proposed system (Brown, 2005; Jean-Charles & Frédéric, 2003; Kanungo, Sharma, & Jain, 2001; Rajiv & Sarv, 2004). Case studies, field studies, and laboratory experiments have been the evaluation vehicles of choice (Fjermestad & Hiltz, 2001; James, Ramakrishnan, & Kustim, 2002; Kaplan, 2000). While for the most part each study has examined the decision effectiveness of an individual system, it has done so by examining the system as a whole using outcome- or user-related measures to quantify success and effectiveness (Etezadi-Amoli & Farhoomand, 1996; Holsapple & Sena, 2005; Jain, Ramamurthy, & Sundaram, 2006). When a study has included two or more systems, individual system effects typically have not been isolated. For example, Nemati, Steiger, Lyer, and Herschel (2002) presented an integrated system with both DSS and AI (artificial intelligence) functionality, but they did not explicitly test for the independent effects of the DSS and AI capabilities on the decision-making outcome and process. This article extends the previous work by examining the separate impacts of different DMSSs on decision effectiveness.

Evaluation of Decision-Making Support Systems

Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.

Ethical Decision Making

Studies have shown that organizations are putting more effort in enforcing the ethical practices in their decision making activities (Janet, Armen, & Ted, 2001). An increasing number of models have also been proposed that have attempted to explore and explain various philosophical approaches to ethical decision making behaviour. In addition, many empirical studies have been presented in various scholarly journals focusing on this subject with the aim of putting theory into practice (O’Fallon & Butterfield, 2005). Nevertheless, unethical practices including fraud, corruption, and bribery continue to be reported (Trevino & Victor, 1992). Bartlett (2003) claims that there is a large gap between theory and practice in ethical decision making research, as existing models are trapped either in undersocialized view (focus on individual factors only) or oversocialized view (focus on situational factor only). Development of a theoretical framework in the ethical decision making area has proven to be very challenging due to the multitude of complex and varied factors that contribute to ethical behaviour. This article attempts to contribute in this challenging area by reviewing and examining the major existing models and presenting an integrated model of ethical decision making model.

DSS and Multiple Perspectives of Complex Problems

Decision support systems have always had a goal of supporting decision-makers. Over time, DSS have taken many forms, or many forms of computer-based support have been considered in the context of DSS, depending on one’s particular perspective. Regardless, there have been decision support systems (DSS), expert systems, executive information systems, group DSS (GDSS), group support systems (GSS), collaborative systems (or computer-supported collaborative work (CSCW) environments), knowledge-based systems, and inquiring systems, all of which are described elsewhere in this encyclopedia. The progression of decision support system types that have emerged follows to some degree the increasing complexity of the problems being addressed. Some of the early DSS involved single decision-makers utilizing spreadsheet models to solve problems. Such an approach would be inadequate in addressing complex problems because one aspect of problem complexity is that multiple stakeholders typically exist. Baldwin (1993) examined the need for supporting multiple views and provides the only attempt found in the information systems literature to operationalize the concept of a perspective. In his work, a view is defined as a set of beliefs that partially describe a general subject of discourse. He identified three major components of a view: the belief or notion to convey, a language to represent the notion, and a subject of discourse. He further described notions as comprising aspects and a vantage point. Aspects are the characteristics or attributes of a subject or situation that a particular notion emphasizes. A vantage point is described by the level of detail (i.e., overview or detailed analysis). Assuming the subject of discourse can be identified with the notion, Baldwin described how differences in views may occur via differences in the notion, the language, or both.