The unclear distinction between data, information, and knowledge has impaired their combination and utilization for the development of integrated systems. There is need for a unified definitional model of data, information, and knowledge based on their roles in computational and cognitive information processing. An attempt to clarify these basic notions is made, and a conceptual framework for integration is suggested by focusing on their different roles and frames of reference within a decision-making process. On this basis, ways of integrating the functionalities of databases, information systems and knowledge-based systems are discussed by taking a knowledge level perspective to the analysis and modeling of systems behaviour. Motivated by recent work in the area of case-based reasoning related to decision support systems, it is further shown that a specific problem solving episode, or case, may be viewed as data, information, or knowledge, depending on its role in decision making and...

... database system. Nail is a purely declarative query language; Glue is a procedural language used for non-query activities. The two languages combined are sufficient to write a complete application. Nail and Glue code are both compiled into the target language IGlue. The Nail compiler uses variants of the magic sets algorithm and supports well-founded models. The Glue compiler's static optimizer uses peephole techniques and data flow analysis to improve code. The IGlue interpreter features a run-time adaptive optimizer that reoptimizes queries and automatically selects indexes. We also describe the Glue-Nail benchmark suite, a set of applications developed to evaluate the Glue-Nail language and to measure the performance of the system.

In this paper we would like to present some preliminary ideas on the possibility of using principled Knowledge Representation techniques -- specifically Description Logics -- in order to support conceptual modeling and flexible access to Information Systems.

Modeling of semantics is one of the most difficult tasks in database design. Constraints are used to express database semantics. They are used differently in database models. They express domain restrictions, specify relationships between components and state database behavior. The utilization depends on the richness of the type system used in the model. The relational model is using a simple type system and has a very large set of integrity constraints. Semantical models are using richer type systems which express also different types of integrity constraints. At the same time, the theory of integrity constraints is more complex. Object-oriented models use either a simple type system or type systems like the semantical models. The theory of integrity constraints is still under development. This overview tries to give a unifying framework on integrity constraints. 1 1 Introduction The goal of this overview is to provide a systematic and unifying introduction to the theory of constrai...

At the 2001 IEEE International Conference on Data Mining in San Jose, California on November 29 - December 2, 2001, there was a panel discussion on how data mining research meets practical development. One of the motivations for organizing the panel discussion was to provide useful advice for industrial people to explore their directions in data mining development.

This document explains a knowledge based model intended to help in Software Requirements Validation. We first construct automatically a specification model from a set of correct specifications using machine learning techniques; and then this model is used to validate new specifications. The machine learning techniques that are used, combine ideas from terminological reasoning, analogical reasoning and probabilistic representation. Contents 1 Introduction 2 2 Theoretic Study of Specification Quality Factors 4 2.1 Completeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.1 Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.2 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.3 Hints to Necessary Information . . . . . . . . . . . . . . . . . . . . . . . . . 5 3 A Metaknowledge Based Validation Approach 8 3.1 Metarequirements . . . . . . . . . . . . . . . . . . . . . . ...

this document preserves this structure and enhances it. The first 42 months of the NCGIA have demonstrated that a multi-institutional, multi-disciplinary center for the advancement of fundamental geographic and GIS research can function effectively. The new research plan builds on the previous one, but also reflects the advancement in our thinking. The five research areas listed in the original solicitation from the National Science Foundation, often referred to as "the five bullets", are herein replaced by three general research areas: spatial representation; spatial analysis; and spatial informatics. This new framework serves as the common base from which individual research efforts can be selected and planned, and into which they are integrated. The Research Initiative continues to be the NCGIA's principal vehicle for research, but is augmented by additional vehicles designed to provide greater flexibility and responsiveness to the Center's ability to conduct its mission and achieve its goals. If NCGIA is to play a truly national role, it must coordinate its research activities with those of other individuals and groups, and act as a focal point and catalyst. The Specialist Meetings held at the start of each NCGIA Research Initiative have performed a useful function in bringing together researchers interested in each topic, and in establishing a common research agenda for the community as a whole. Our published research plan (NCGIA, 1989) has strong similarities to those GIS and GIA research agendas developed and published by other groups (Craig, 1989; Maguire, 1990; Masser, 1990). At its June 1991 meeting the NCGIA Board of Directors gave strong encouragement to undertake the development of a national GIS and GIA research agenda as an NCGIA activity. Thus we see the...

this document preserves this structure and enhances it. The first 42 months of the NCGIA have demonstrated that a multi-institutional, multi-disciplinary center for the advancement of fundamental geographic and GIS research can function effectively. The new research plan builds on the previous one, but also reflects the advancement in our thinking. The five research areas listed in the original solicitation from the National Science Foundation, often referred to as "the five bullets", are herein replaced by three general research areas: spatial representation; spatial analysis; and spatial informatics. This new framework serves as the common base from which individual research efforts can be selected and planned, and into which they are integrated. The Research Initiative continues to be the NCGIA's principal vehicle for research, but is augmented by additional vehicles designed to provide greater flexibility and responsiveness to the Center's ability to conduct its mission and achieve its goals.

Introduction Extant tools currently available for formal modeling, reasoning, decision making and computation in computer engineering are unequivocal, that is, they contains no uncertainties. Certainty indicates that the structures and parameters of the model they are dealing with is assumed to be precisely and totally known, and thus there are no doubts about their values, occurrence and their completeness. However, unfortunately, in our real life application domains, there is an unavoidable and uncontrollable loss of information and a discrepancy-our real word is full of uncertainties [Deyi90]. ' The term 'uncertainty' has a broad semantic content. According to Webste's New Twentieth Century Dictionary, six different interpretations of this term are possible. 1. Not certainly known; questionable; problematical. 2. Vague; not definite or determined. 3. Doubtful; not having certain knowledge; not sure. 4. Ambiguous. 5. Not steady or constant; varying. 6. Liable to change or

Abstract. The Knowledge Base Management Systems (KBMS) Project at the University of Toronto (1985-1995) was inspired by a need for advanced knowledge representation applications that require knowledge bases containing hundreds of thousands or even millions of knowledge units. The knowledge representation language Telos provided a framework for the project. The key results included conceptual modeling innovations in the use of semantic abstractions, representations of time and space, and implementation techniques for storage management, query processing, rule management, and concurrency control. In this paper, we review the key ideas introduced in the KBMS project, and connect them to some of the work since the conclusion of the project that is either closely related to or directly inspired by it.