Abstract
In modern applications, spatial objects are often annotated with textual descriptions, and users are offered the opportunity to formulate spatio-textual queries. The result set of such a query consists of spatio-textual objects ranked according to their distance from a desired location and to their textual relevance to the query. In this context, a challenging problem is how to select a set of at most b keywords to enhance the description of the facilities of a spatial object, in order to make the object appear in the top-k results of as many users as possible. In this paper, we formulate this problem, called Best-terms and we show that it is NP-hard. Hence, we present a baseline algorithm that provides an approximate solution to the problem. Then, we introduce a novel algorithm for keyword selection that greatly improves the efficiency of query processing. By means of a thorough experimental evaluation, we demonstrate the performance gains attained by our approach.
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Based on the adopted similarity function, the addition of a term does not have a negative effect on the influence score. In the general case, an exact algorithm should examine \(2^{|A|}\) term combinations.
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Acknowledgments
A. Vlachou was supported by the Action “Supporting Postdoctoral Researchers” of the Operational Program “Education and Lifelong Learning” (Action’s Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State. C. Doulkeridis has been co-financed by ESF and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: Aristeia II, Project: ROADRUNNER.
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Gkorgkas, O., Vlachou, A., Doulkeridis, C., Nørvåg, K. (2015). Maximizing Influence of Spatio-Textual Objects Based on Keyword Selection. In: Claramunt, C., et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_22
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DOI: https://doi.org/10.1007/978-3-319-22363-6_22
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