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rate means for selectivity estimation. Previous works on selectivity estimation, however, suffer from inherent drawbacks, reflected in efficiency and effective issues. In this paper, we present a general framework for hybrid selectivity estimation. Based on its requirements, we study the applicability of existing approaches. Driven by our findings, we propose a novel estimation approach, TopGuess, exploiting topic models as data synopsis. This enables us to capture correlations between structured and unstructured data in a uniform and scalable manner. We study TopGuess in theorical manner, and show TopGuess to guarantee a linear space | rate means for selectivity estimation. Previous works on selectivity estimation, however, suffer from inherent drawbacks, reflected in efficiency and effective issues. In this paper, we present a general framework for hybrid selectivity estimation. Based on its requirements, we study the applicability of existing approaches. Driven by our findings, we propose a novel estimation approach, TopGuess, exploiting topic models as data synopsis. This enables us to capture correlations between structured and unstructured data in a uniform and scalable manner. We study TopGuess in theorical manner, and show TopGuess to guarantee a linear space | ||
complexity w.r.t. text data size, and a selectivity estimation time complexity independent from its synopsis size. In experiments on real-world data, TopGuess allowed for great improvements in estimation accuracy, without sacrificing runtime performance. | complexity w.r.t. text data size, and a selectivity estimation time complexity independent from its synopsis size. In experiments on real-world data, TopGuess allowed for great improvements in estimation accuracy, without sacrificing runtime performance. | ||
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|Projekt=IZEUS | |Projekt=IZEUS | ||
|Forschungsgruppe=Wissensmanagement | |Forschungsgruppe=Wissensmanagement |
Version vom 24. Juli 2013, 15:51 Uhr
Published: 2013
Mai
Institution: Institute AIFB, KIT
Erscheinungsort / Ort: Karlsruhe
Archivierungsnummer:3039
Kurzfassung
The Resource Description Framework (RDF) has
become an accepted standard for describing entities on the Web. Many such RDF descriptions are text-rich – besides structured data, they also feature large portions of unstructured text. As a result, RDF data is frequently queried using predicates matching structured data, combined with string predicates for textual constraints: hybrid queries. Evaluating hybrid queries requires accu-
rate means for selectivity estimation. Previous works on selectivity estimation, however, suffer from inherent drawbacks, reflected in efficiency and effective issues. In this paper, we present a general framework for hybrid selectivity estimation. Based on its requirements, we study the applicability of existing approaches. Driven by our findings, we propose a novel estimation approach, TopGuess, exploiting topic models as data synopsis. This enables us to capture correlations between structured and unstructured data in a uniform and scalable manner. We study TopGuess in theorical manner, and show TopGuess to guarantee a linear space
complexity w.r.t. text data size, and a selectivity estimation time complexity independent from its synopsis size. In experiments on real-world data, TopGuess allowed for great improvements in estimation accuracy, without sacrificing runtime performance.
Download: Media:Awa-topguess-selectivityestimation-tr.pdf