Informetrics through advanced data management

Informetrics through advanced data management: Complex object restructuring, data aggregation and transitive computation

Järvelin, Kalervo
Department of Information Studies
University of Tampere
P.O.Box 607
FIN-33101 TAMPERE, Finland

Ingwersen, Peter
Royal School of Library and Information Science
Birketinget 6
DK-2300 COPENHAGEN S, Denmark

Niemi, Timo
Department of Computer Science
University of Tampere
P.O.Box 607
FIN-33101 TAMPERE, Finland

Järvelin, K. & Ingwersen, P. & Niemi, T. (1999). Informetrics through advanced data management: Complex object restructuring, data aggregation and transitive computation. Tampere, Finland: University of Tampere, Department of Information Studies, Report R-1999-1. 44 p.

Abstract

This article considers how informetric calculations can easily and declaratively be specified through advanced data management techniques. In particular, bibliographic data and its modeling as complex objects (non-first normal form relations) as well as terminological and citation networks involving transitive relationships are considered. A very high-level declarative query interface, based on this data model, is introduced. The article demonstrates that such data modeling and query interface enable end-users to perform basic informetric ad hoc calculations, such as bibliographic coupling, author co-citation analysis, generalized impact factors, international visibility and international impact, productivity calculations in a given area, etc., easily and often with much less effort than in the contemporary online retrieval systems. Several fruitful generalizations of typical informetric measurements are also proposed. These are based on substituting traditional foci of analysis, e.g., journals, by other object types, such as authors, organizations, countries or classes of a classification scheme. It is shown that the proposed data modeling and query interface make it trivial to switch focus between various object types for informetric calculations. Moreover, it is demonstrated that all informetric data can easily be broken down by criteria that foster advanced analysis, e.g., by years or content-bearing attributes. Such modeling allows flexible data aggregation along many dimensions and the utilization of transitive relationships. These salient features emanate from the query interfaceÕs general data restructuring and aggregation capabilities combined with transitive processing capabilities. The features are illustrated by means of sample queries and results.

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