Development and Application of a Spreadsheet-Based Spatial Decision Support System (SDSS)

Spatial decision support systems (SDSS) are decision support tools which have been used widely in addressing complicated issues involving a spatial component. The use of SDSS has increased greatly over the last few decades especially in fields such as planning, natural resources management, and environmental science. Traditionally, SDSS have been developed with Geographic Information Systems (GIS) technology as a major component and used in application areas in which the use of GIS technology has been common. GIS software is often expensive and requires significant expertise, which can lead to under-utilization of GIS-based SDSS. In this paper, we describe the development of a freely available SDSS extension developed for Microsoft Excel, a very commonly used spreadsheet application. The purpose of this SDSS is to expand potential SDSS use to a wider potential audience for research, management, and teaching purposes.

Visualization-Based Decision Support Systems

Increasingly, decision makers are incorporating large quantities of interrelated data in their decision making. Decision support systems need to provide visualization tools to help decision makers glean trends and patterns that will help them design and evaluate alternative actions. While visualization software that might be incorporated into decision support systems is available, the literature does not provide sufficient guidelines for selecting among possible visualizations or their attributes. This paper describes a case study of the development of a visualization component to represent regional relationship data. It addresses the specific information goals of the target organization, various constraints that needed to be satisfied, and how the goals were achieved via a suitable choice of visualization technology and visualization algorithms. The development process highlighted the need for specific visualizations to be driven by the specific problem characteristics as much as general rules of visualization. Lessons learned during the process and how these lessons may be generalized to address similar requirements is presented.

Automated Diagnosis through Ontologies and Logical Descriptions

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

DSS-CMM

While DSS development methodologies and implementation research are abound in the DSS literature, a gap still exists in terms of the ability to provide a holistic conceptual structure for improving the management and development of decision support systems, the ability to capture and share understanding of key DSS development processes, and most notably, the ability to provide guidance for DSS development and process improvements. This paper proposes a Decision Support System Capability Maturity Model (DSS-CMM). The model leverages related DSS literature and input from DSS researchers and practitioners to identify pertinent DSS development processes and capability levels. From a theoretical perspective, DSS-CMM provides a meta-model for DSS development processes and represents the first maturity model specifically targeting DSS development. From a practical perspective, the model provides a framework for organizations to assess the capability level of their DSS development processes and devise process improvement initiatives to address any limitations with existing practices.

Discrepancies and Analogies in Artificial Intelligence and Engineering Design Approaches in Addressing Collaborative Decision-Making

One of the trends in the decision-making field in the past 20 years has been the migration from individual decision-making to collective one. Several changes of working conditions influenced this trend: geographical dispersion due to the business internationalisation, concurrent work in order to satisfy time delays, facilitation of the information sharing induced by the development of local area networks (LAN), and internet. This study examines the discrepancies and analogies in addressing the collaborative decision making in two scientific fields: artificial intelligence and engineering design. These two fields have different considerations and approaches in view to the decision-making support. This paper exposes a comparative study concerning two research studies, both decision support oriented: the first one concerns the collaborative decision-making in early design stages in vehicle development projects (Jankovic, Bocquet, Stal Le Cardinal, & Bavoux, 2006) and the second one concerns the development of an architecture of a Cooperative decision Support Systems (CDSS) (Zaraté, 2005).

Understanding Organisational Decision Support Maturity

Forty years after Gorry and Scott Morton’s seminal paper on DSS, supporting decisions in organisation is still a critical objective. Given the elapsed time since DSSs were first introduced, it is important to gauge the scope and quality of decision support provided to managers. Using Executive MBA students as informants about decision making in their organisations, the authors carried out 10 case studies of Irish organisations to assess their maturity in terms of decision support usage. The findings indicate that, in the vast majority of firms, decision support is still not available to help manage in situations involving high levels of abstraction. As was the case at the beginning of the history of DSS, the operational level is still where DSSs are used most consistently across firms. Furthermore, this study illustrates that engaging with managers on the topic of decision making is difficult, given the possibility of bias and misrepresentation inherent in the reality of decision making.

Ontology Engineering The “Whats,” “Whys,” and “Hows” of Data Exchange

In a challenging financial climate, there is a growing impetus for businesses to use existing process data to support more intelligent decision making. For large-scale complex systems such as railways, electricity grids, and gas distribution networks, this often means combining information from numerous different condition monitoring systems; however, given the vast amounts of data produced every day and the frequently incompatible data models used to represent it, is it possible to be sure that the information generated is being used correctly? This paper provides an introduction to the field of Ontology, an emerging technology that allows the exact “meaning” of an item of data to be described in a way that can be interpreted by computers. Through this retention of meaning, it becomes possible for computers to perform simple reasoning operations, inferring new information about a system from the existing facts, and enabling exciting new Semantic Web technologies.

Hierarchical Database Model for Querying Economic Network Independence Distribution

The development of the internet has been triggering numerous mutations in the visualization of actors in economic network independence distribution (ENID) of goods. ENID overcomes the physical barriers of shop-floor space so unprecedented variety of products could be offered to the customers. Avoidance of expensive trade space allows suppliers to reduce price compared to those in the physical world. User friendly and easy contact with the supplier of the goods make shopping very convenient. Despite these advantages of ENID, there is a need to develop better theories about how this system should behave in order to protect participants’ interests. This work employed hierarchical database model using B-tree and pre-order algorithm to insert and traverse participant records for easy processing. N-level models were adopted to calculate each level and sub-level cluster commission. The implementation was carried out using C# and sql. The application of the model permits the participants to query any information about ENID for on line real time decision makings.

Extended TOPSIS with Correlation Coefficient of Triangular Intuitionistic Fuzzy Sets for Multiple Attribute Group Decision Making

This paper extends the technique for order preference by similarity to ideal solution (TOPSIS) for solving multi-attribute group decision making (MAGDM) problems under triangular intuitionistic fuzzy sets by using its correlation coefficient. In situations where the information or the data is of the form of triangular intuitionistic fuzzy numbers (TIFNs), some arithmetic aggregation operators have to be defined, namely the triangular intuitionistic fuzzy ordered weighted averaging (TIFOWA) operator and the triangular intuitionistic fuzzy hybrid aggregation (TIFHA) operator. An extended TOPSIS model is developed to solve the MAGDM problems using a new type of correlation coefficient defined for TIFNs based on the triangular intuitionistic fuzzy weighted arithmetic averaging (TIFWAA) operator and the TIFHA operator. With an illustration this proposed model of MAGDM with the correlation coefficient of TIFNs is compared with the other existing methods.

Strategic Development of a Decision Making Support System in a Public R&D Center

Decision making in new technologies is a crucial activity to raise competitiveness, especially for technology organizations. The decision-making process requires the use of information technology tools, since the information amount is large and requires reliable methods for collecting, accessing, storing, processing, distributing, and evaluating, in order to provide reliable information to decision makers. The strategy of an organization must take into account the integration of this aspect with other organizational functions. This paper presents a proposal to integrate new elements into the IT strategy, considering the interactions with other organizational functions, defining an implementation and transition plan that takes into account the organization dynamics, which has limited resources and, therefore, requires a gradual and long term transition plan. This paper refers as case study to a Mexican Public R&D Center, which has re-engineered its operating model with a systems approach.