Data Conversion, Aggregation and Deduction

Data Conversion, Aggregation and Deduction in Advanced Retrieval from Heterogeneous Fact Databases.

Kalervo Järvelin# and Timo Niemi+

#Department of Information Studies
+Department of Computer Science
University of Tampere
P.O.Box 607
FIN-33101 TAMPERE, Finland

Bookstein, A. et al (Eds.), The 14th International Conference on Research and Development in Information Retrieval (ACM SIGIR '91), Chicago, IL, Oct. 13-16, 1991. New York, NY: ACM, 1991, pp. 173-182.

Abstract

Modern distributed fact databases are heterogeneous and autonomous. Their heterogeneity is due to many reasons, including varying data models, data structures, attribute naming conventions, units of measurement or naming of data values, composition of data as attributes, technical representation of data, abstraction levels of data, etc. Database autonomity means that the database users have hardly any means for reducing such heterogeneity. Present information retrieval (IR) systems either provide no support for overcoming such heterogeneity or their support is insufficient and difficult to utilize. In this paper we offer integrated and powerful data conversion, aggregation and deductive techniques for advanced IR in such environments. These techniques allow the users to overcome data inconsistency due to units of measurement or naming of data values, composition of data as attributes, abstraction levels of data, and difficulties related to deductive use of hierarchically classified data. In complex situations, all these inconsistencies appears together. Therefore we also show how these techniques are integrated into a powerful query language which has been implemented in Prolog in a workstation environment.

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