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ACM SIGMOD Record
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Dynamic Taxonomies: A Model for Large Information Bases
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Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
Uniform Access to Multimedia Information Bases through Dynamic Taxonomies
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
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TCGOV'05 Proceedings of the 2005 international conference on E-Government: towards Electronic Democracy
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dg.o '07 Proceedings of the 8th annual international conference on Digital government research: bridging disciplines & domains
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DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
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Access to complex information bases through multidimensional, dynamic taxonomies (also improperly known as faceted classification systems) is rapidly becoming pervasive in industry, especially in e-commerce. In this paper, the major shortcomings of conventional, monodimensional taxonomic approaches, such as the independence of different branches of the taxonomy and insufficient scalability, are discussed. The dynamic taxonomy approach, the first and most complete model for multidimensional taxonomic access to date, is reviewed and compared to conventional taxonomies. We analyze the reducing power of dynamic taxonomies and conventional taxonomies and report experimental results on real data, which confirm that monodimensional taxonomies are not useful for browsing/retrieval on large databases, whereas dynamic taxonomies can effectively manage very large databases and exhibit a very fast convergence.