Information retrieval is a wide, often loosely-defined term but in these pages I shall be concerned only with automatic information retrieval systems. Automatic as opposed to manual and information as opposed to data or fact. Unfortunately the word information can be very misleading. In the context of information retrieval (IR), information, in the technical meaning given in Shannon's theory of communication, is not readily measured (Shannon and Weaver1). In fact, in many cases one can adequately describe the kind of retrieval by simply substituting 'document' for 'information'. Nevertheless, 'information retrieval' has become accepted as a description of the kind of work published by Cleverdon, Salton, Sparck Jones, Lancaster and others. A perfectly straightforward definition along these lines is given by Lancaster2: 'Information retrieval is the term conventionally, though somewhat inaccurately, applied to the type of activity discussed in this volume. An information retrieval system does not inform (i.e. change the knowledge of) the user on the subject of his inquiry. It merely informs on the existence (or non-existence) and whereabouts of documents relating to his request.' This specifically excludes Question-Answering systems as typified by Winograd3 and those described by Minsky4. It also excludes data retrieval systems such as used by, say, the stock exchange for on-line quotations.

In the physical sciences and engineering, units of measurement provide a valuable aid to both the exposition and comprehension of physical systems. In addition, they provide an error checking facility comparable to static type checking commonly found with programming languages. It is argued that units of measurement can provide similar benefits in the specification and design of software and computer systems. To demonstrate this, we present an extension of the Z specification notation with support for the incorporation of units in specifications and demonstrate the feasibility of static dimensional analysis of the resulting language. 1 Introduction It is common place for scientists and engineers to use units of measurement in describing models of physical systems because ffl for such descriptions to be complete, the units of quantities need to be specified --- a building plan is useless if the units of the dimensions are not known; ffl having units explicit in a descriptio...

Database and data model evolution is a significant problem in the highly dynamic business environment that we experience these days. To support the rapidly changing data requirements of agile companies, conceptual data models, which constitute the foundation of database design, should be sufficiently flexible to be able to incorporate changes easily and smoothly. In order to understand what factors drive the maintainability of conceptual data models and to improve conceptual modelling processes, we need to be able to assess conceptual data model properties and qualities in an objective and cost-efficient manner. The scarcity of early available and thoroughly validated maintainability measurement instruments motivated us to define a set of metrics for Entity-Relationship (ER) diagrams, which are a relevant graphical formalism of the conceptual data modelling method. In this paper we show that these objectives and easily calculated metrics, measuring internal properties of ER diagrams related to their structural complexity, can be used as indirect measures (hereafter called indicators) of the maintainability

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this paper I offer such a conceptual framework. It provides basic KM-related concepts, a business process decision model, a knowledge life cycle model, a KM framework, and a detailed listing of descriptors and metrical concepts associated with the main categories of the conceptual framework. I begin with the basic concepts.

This paper presents a set of metrics and pseudo-metrics for the measurement of conceptual distances in M.E.R.O.DE. business models. The measures are developed and validated using measure and measurement theory. It is argued that this metrics set constitutes a strong formal basis for the further assessment and prediction of relevant internal and external attributes of object-oriented specifications. Keywords - object type, business model, conceptual distance, measure theory, measurement theory, metric, pseudo-metric, scale type, measure validation I. INTRODUCTION In software engineering the notions of software metric and software measure are used interchangeably and mostly without reference to the mathematical discipline of measure theory and the philosophical discipline of measurement theory. In order to formalise the measurement of software, the distinction between a metric and a measure must be clarified and both terms must be used in a consistent manner. Formalisation of measurem...

This paper presents concepts, methodology and tools for producing improved KM benefit estimates. My objective is to provide a framework for thinking about more comprehensive estimation of KM benefits -- estimation that is tightly coupled to corporate goals, and that distinguishes benefits according to their relative importance. I will not propose a specific methodology for estimation in all situations, because, as we will see, no single methodology is appropriate for every corporate situation. Comprehensive benefit estimation is not practical in many situations. While, in others, varying degrees of comprehensiveness will be appropriate. Instead of a single methodology, I will define an abstract pattern of Comprehensive Benefit Estimation (CBE) that would, if implemented, achieve the goal of tight coupling of benefits, goals, and KM initiatives and competing alternatives. Then I will point out how in different concrete situations one may tailor the pattern to achieve a

Aristotelian, or non-Platonist, realism holds that mathematics is a science of the real world, just as much as biology or sociology are. Where biology studies living things and sociology studies human social relations, mathematics studies the quantitative or structural aspects of things, such as ratios, or patterns, or complexity,

The foundations of measurement theory were invesUgated by modeltheoreUc methods. The purpose was to establish a firm ba~is for <;leneral system theory. One major result was the formulatton of the concepts of scale, scale transformation and the proof of the existence, for an arbitrary scale, of the group of scale transformatiqns which "leave the scale form invariantII. As an illustration of the appUcabiUty of these concepts and because of its intrinsic interest an expo~ition was <;liven of the theory of measurement for extensive quantities. A novel formulation of the usual axioms was developed which made them ~lementary formulae in the first order predicate calculus. Thus it was P9ssible to show that this theory is model-complete 0 Then A. Robinson I f! tlleorems were applied to show that this theory is negation complete. In the formulation of the existence theorem for scale transformation 1 groups no restriction was placed on the empirical 'E-model Qr the numerical 'E-model and in the other illustrative examples it wa $ shown how different choices of these 'E-models lead to different scale transformation groups. An example of a theory of a non-extensive quantity was presented but the methods used before would not Yield model-completeness for this theory and thus an interesting unsolved problem remains.

Much effort and research has gone into solving the problem of evaluation of information retrieval systems. However, it is probably fair to say that most people active in the field of information storage and retrieval still feel that the problem is far from solved. One may get an idea of the extent of the effort by looking at the numerous survey articles that have been