Soumitra Dutta

Information technology and organisation within European small enterprises

Small enterprises (SEs) provide a major portion of the employment in most European countries. More than 90 per cent of the total number of European Union (EU) businesses are comprised of SEs, accounting for 25 per cent of EU turnover. This paper investigates the strategic management of information technology (IT) and organisation within European Small Enterprises (SEs). SEs in six different European countries/regions were included in the European Small Enterprise Information Technology (SEIT) study conducted at INSEAD: Benelux, France, Germany, Italy, Spain and the UK. The results show that European SEs have to innovate from both technological and organisational perspectives and build partnerships with other organisations in order to successfully address strategic opportunities and challenges.

Strategies for implementing knowledge-based systems

The effective management of knowledge is important for the competitivity of organizations. Rapid technological progress over the last decade has made knowledge-based systems (KBSs) (including expert systems, organizational memory information systems, and other advanced information technology solutions) an integral part of every organizations effort to manage its knowledge assets effectively, KBSs have an important impact on all levels of organizational knowledge: individual, group, organizational, and knowledge links. This paper outlines four generic knowledge processing strategies to guide the implementation of KBSs within organizations. These generic strategies are related both to the level of knowledge assets under consideration and the locus of responsibility for the development of KBS. The different knowledge processing strategies influence the management of knowledge possible within an organization and consequently influence the development of KBS within the organization. The paper also outlines different facilitators and barriers to the four knowledge processing strategies.

Approximate spatial reasoning: integrating qualitative and quantitative constraints

Abstract Approximate reasoning refers in general to a broad class of solution techniques where either the inference procedure or the environment for inference is imprecise. Algorithms for approximate spatial reasoning are important for coping with the widespread imprecision and uncertainty in the real world. This paper develops an integrated framework for representing induced spatial constraints between a set of landmarks given imprecise, incomplete, and possibly conflicting quantitative and qualitative information about them. Fuzzy logic is used as the computational basis for both representing quantitative information and interpreting linguistically expressed qualitative constraints.

Qualitative spatial reasoning: a semi-quantitative approach using fuzzy logic

Qualitative reasoning is useful as it facilitates reasoning with incomplete and weak information and aids the subsequent application of more detailed quantitative theories. Adoption of qualitative techniques for spatial reasoning can be very useful in situations where it is difficult to obtain precise informationand where there are real constraints of memory, time and hostile threats. This paper formulates a computational model for obtaining all induced spatial constraints on a set of landmarks, given a set of approximate quantitative and qualitative constraints on them, which may be incomplete, and perhaps even conflicting.

An event based fuzzy temporal logic

A temporal logic is developed to deal with events that are uncertain with regard to their occurrence in a given interval of time. Events are represented as fuzzy sets with the membership function giving the possibility of occurrence of the event in a given interval of time. An axiomatization of the fuzzy event calculus is presented, and several of its properties are proved. The logic is simple but powerful; it can determine effectively the various temporal relations between uncertain events or their combinations.<<ETX>>

Case-based reasoning systems: from automation to decision-aiding and stimulation

Over the past decade, case-based reasoning (CBR) has emerged as a major research area within the artificial intelligence research field due to both its widespread usage by humans and its appeal as a methodology for building intelligent systems. Conventional CBR systems have been largely designed as automated problem-solvers for producing a solution to a given problem by adapting the solution to a similar, previously solved problem. Such systems have had limited success in real-world applications. More recently, there has been a search for new paradigms and directions for increasing the utility of CBR systems for decision support. The paper focuses on the synergism between the research areas of CBR and decision support systems (DSSs). A conceptual framework for DSSs is presented and used to develop a taxonomy of three different types of CBR systems: 1) conventional, 2) decision-aiding, and 3) stimulative. The major characteristics of each type of CBR system are explained with a particular focus on decision-aiding and stimulative CBR systems. The research implications of the evolution in the design of CBR systems from automation toward decision-aiding and stimulation are also explored.

Software engineering in Europe: a study of best practices

Software development organizations are increasingly aware of the importance of using best practices to improve their development processes, but how many are actually adopting them? The authors analyzed data on almost 400 companies in 20 European countries to find out.

Decision support in non-conservative domains: generalization with neural networks

Models in conventional decision support systems (DSSs) are best suited for problem solutions in domains with well defined/structured (mathematical) or partially defined/semi-structured (heuristic) domain models. Nonconservative/unstructured domains are those which either lack a known model or have a poorly defined domain model. Neural networks (NNs) represent an alternative modelling technique which can be useful in such domains. NNs autonomously learn the underlying domain model from examples and have the ability to generalize, i.e., use the learnt model to respond correctly to previously unseen inputs. This paper describes three different experiments to explore the use of NNs for providing decision support by generalization in non-conservative/ unstructured domains. Our results indicate that NNs have the potential to provide adequate decision support in non-conservative/unstructured domains.

Fuzzy logic applications: Technological and strategic issues

It is noted that fuzzy logic has not only significantly enhanced knowledge-based and expert system technology, but has fundamentally altered the granularity of intelligence. With the help of fuzzy logic, manufacturers of home appliances are today embedding intelligence inside individual products. The application of fussy logic has also transformed industrial process control and enabled new product development strategies. A review of the evolution of fuzzy logic technology aimed at an audience of business and industrial managers, is presented. The strategic business impact of the technology is analyzed. >

Fuzzy rough granular neural networks, fuzzy granules, and classification

We introduce a fuzzy rough granular neural network (FRGNN) model based on the multilayer perceptron using a back-propagation algorithm for the fuzzy classification of patterns. We provide the development strategy of the network mainly based upon the input vector, initial connection weights determined by fuzzy rough set theoretic concepts, and the target vector. While the input vector is described in terms of fuzzy granules, the target vector is defined in terms of fuzzy class membership values and zeros. Crude domain knowledge about the initial data is represented in the form of a decision table, which is divided into subtables corresponding to different classes. The data in each decision table is converted into granular form. The syntax of these decision tables automatically determines the appropriate number of hidden nodes, while the dependency factors from all the decision tables are used as initial weights. The dependency factor of each attribute and the average degree of the dependency factor of all the attributes with respect to decision classes are considered as initial connection weights between the nodes of the input layer and the hidden layer, and the hidden layer and the output layer, respectively. The effectiveness of the proposed FRGNN is demonstrated on several real-life data sets.