Table of Content
A selective review of database design approaches
Jarrod B. Henson
The design of a database is concerned with the representation by the database system of some aspect of a real world situation . It can be considered as four different stages: specification of requirements; conceptual design; logical design; and physical design. The information needs of various users must be identified and extracted. Two sub-phases of conceptual design can be considered: view modelling, which turns the user requirement into a number of different user views; and view inclusion, which combines them into a single global scheme . The goal of these two stages is to achieve a high level of representation, often referred to as a conceptual scheme that is independent of the DBMS. The logical design of the database is designed to depart from the requirements imposed by a specific physical implementation  the content of a database. In this phase the conceptual schema is thus translated into the logical DBMS model. The logical data model is transformed into an adequate form of the hardware / DBMS configuration used  by physical database design.
Non-hierarchic document clustering using a genetic algorithm
Kevin H. Gabriel
A multivariate statistical technique to identify groups or classifications in multi-dimensional space is cluster analysis, or automatic classification . In recent years, numerous efforts have been made to use such procedures in document database organization in order to bring together documents with a large number of index terms [2-7]. In this paper we look at using a genetic algorithm, now a GA, for clustering documents. GA is a class of algorithms which are not deterministic, derived from Darwinian evolutionary theories [8-10]. They are good solutions for combinational optimization problems, though not necessarily optimal, when the number of possible solutions is far too high to explore all of the options with a certain deterministic algorithm in an appropriate period of time. The non-hierarchical clustering problem is that when the clustering method tries to divide a set of objects into a set of unoverlapping groups in order to maximize certain external criteria of’ cluster goodness’ usually in order to maximize the inter-object similitudes of the inter-cluster and minimize similarities among the intercluster groups. Duran and Odell  emphasize the combined nature of the partitioning problem, who note that it is approximately. The evaluation of this number of partitions would therefore be extremely complicated in terms of computational resources, while if a deterministic algorithm is used it would be absolutely ineffective to generate and then evaluate partitions resulting from a database of non-trivial scale. The first to suggest Raghavan and Birchard[ 12] that a GA for clustering documents could be used, even though no experimentation has made use of actual documents and queries: we have compared the recovery efficiency of GA-based clusters with those of network-based cluster clusters that only contain the most important inter-documental similarities and that they are.
Information systems strategy formation in Higher Education Institutions
Manuel R. Kemp
This paper describes a research project at the Universities of Sheffield’s Department of Information Studies. The research is focused in UK Higher Education Institutions (HEIs) on the training of Information Systems Strategy (ISS) in order to refer to information strategy. Information strategies are viewed as a subset of information systems strategy for the purpose of this research. This research is interested in two levels: first, the research subject and second, the methodological approach to be tested. The majority of HEIs in the UK are developing information strategies. Both domestic pressures and significantly HEFCE have been the impetus of this development. Regrettably, very little information on HEI’s information systems strategies or information strategies is available. It is hoped that the research will deal in a way with this disequilibrium.
Informatics as social science
Roger M. Aguayo
Introduction. A vast body of research has shown information science to be a social science, but information science’s identity as both a social science and a non-social science has become all the more uncertain, or simply has been left to the discretion of the reader. Method. This paper traces the specifics of information science as a social science. The paper examines the background of the social sciences in the history of academic disciplines. The paper discusses the ways in which positivism and interpretativism, the leading traditions of the social sciences, assert themselves in information science as a social science. Conclusions. It is argued that received ideas about the social sciences impact how information science as a social science is perceived. It is also argued that information science as a social science can and should provide valid scientific explanations. This paper distinguishes social interaction as the defining feature of information science as a social science. To this end, the paper proposes global complexity not as a theory or solution, but as a metaphor for information science as a social science to address the pressing issues of our increasingly interconnected world.
Information and understanding: an evolutionary IT framework
Sophia P. Warren
Background. Many definitions of information, knowledge, and data have been suggested throughout the history of information science. In this article, the objective is to provide definitions that are usable for the physical, biological, and social meanings of the terms, covering the various senses important to our field. Argument. Information 1 is defined as the pattern of organization of matter and energy. Information is defined as some pattern of organization of matter and energy that has been given meaning by a living being. Knowledge is defined as information given meaning and integrated with other contents of understanding. Elaboration. The approach is rooted in an evolutionary framework; that is, modes of information perception, processing, transmission, and storage are seen to have developed as a part of the general evolution of members of the animal kingdom. Brains are expensive for animals to support; consequently, efficient storage, including, particularly, storage at emergent levels-for example, storing the concept of chair, rather than specific memories of all chairs ever seen, is powerful and effective for animals. Conclusion. Thus, rather than being reductionist, the approach taken demonstrates the fundamentally emergent nature of most of what higher animals and human beings, in particular, experience as information.