An Efficient Block Index Scheme with Segmentation for Spatio-Textual Similarity Join
Given two collections of objects that carry both spatial and textual information in the form of tags, a Spatio-Textual-based object Similarity JOIN (ST-SJOIN) retrieves the pairs of objects that are textually similar and spatially close. In this paper, we have proposed a block index-based approach c...
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Veröffentlicht in: | KSII transactions on Internet and information systems 2017, 11(7), , pp.3578-3593 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Given two collections of objects that carry both spatial and textual information in the form of tags, a Spatio-Textual-based object Similarity JOIN (ST-SJOIN) retrieves the pairs of objects that are textually similar and spatially close. In this paper, we have proposed a block index-based approach called BIST-JOIN to facilitate the efficient ST-SJOIN processing. In this approach, a dual-feature distance plane (DFDP) is first partitioned into some blocks based on four segmentation schemes, and the ST-SJOIN is then transformed into searching the object pairs falling in some affected blocks in the DFDP. Extensive experiments on real and synthetic datasets demonstrate that our proposed join method outperforms the state-of- the-art solutions. KCI Citation Count: 0 |
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ISSN: | 1976-7277 1976-7277 |
DOI: | 10.3837/tiis.2017.07.015 |