Search engines are continuously employing advanced techniques that aim to capture user intentions and provide results that go beyond the data that simply satisfy the query conditions. Examples include the personalized results, related searches, similarity search, popular and relaxed queries. In this work we introduce a novel query paradigm that considers a user query as an example of the data in which the user is interested. We call these queries “exemplar queries”. and claim that they can play an important role in dealing with the information deluge. We provide a formal specification of the semantics of such queries and show that they are fundamentally different from notions like queries by example, approximate and related queries.
We provide an implementation of these semantics for graph-based data and present an exact solution with a number of optimizations that improve performance without compromising the quality of the answers. We study two different similarity functions, isomorphism and strong simulation, for retrieving the answers to an exemplar query, and we provide solutions for both. We also provide an approximate solution that prunes the search space and achieves considerably better time-performance with minimal or no impact on effectiveness. We experimentally evaluate the effectiveness and efficiency of these solutions with synthetic and real datasets, and illustrate the usefulness of exemplar queries in practice.
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Source CodeYou may freely use this code for research purposes, provided that you properly acknowledge the authors using the following reference:
Davide Mottin, Matteo Lissandrini, Yannis Velegrakis, Themis Palpanas. Exemplar Queries: A New Way of Searching. International Journal on Very Large Data Bases (VLDBJ), accepted for publication, 2016.