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An Empirical Performance Evaluation of Relational Keyword Search Techniques

An Empirical Performance Evaluation of Relational Keyword Search Techniques

Extending the keyword search paradigm to relational data has been an active area of research within the

database and IR community during the past decade. Many approaches have been proposed, but despite numerous publications, there remains a severe lack of standardization for

the evaluation of proposed search techniques. Lack of standardization has resulted in contradictory results from different evaluations, and the numerous discrepancies muddle what

advantages are
proffered by different approaches. In this paper, we present the most extensive empirical performance evaluation of relational keyword search techniques to appear to date in the

literature. Our results indicate that many existing search techniques do not provide acceptable performance for realistic retrieval tasks. In particular, memory consumption precludes

many
search techniques from scaling beyond small data sets with tens of thousands of vertices. We also explore the relationship between execution time and factors varied in previous

evaluations; our analysis indicates that most of these factors have relatively little impact on performance. In summary, our work confirms previous claims regarding the

unacceptable performance of
these search techniques and underscores the need for standardization in evaluations—standardization exemplified by the IR community.

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