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Comparing data summaries for processing live queries over Linked Data

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Abstract

A growing amount of Linked Data—graph-structured data accessible at sources distributed across the Web—enables advanced data integration and decision-making applications. Typical systems operating on Linked Data collect (crawl) and pre-process (index) large amounts of data, and evaluate queries against a centralised repository. Given that crawling and indexing are time-consuming operations, the data in the centralised index may be out of date at query execution time. An ideal query answering system for querying Linked Data live should return current answers in a reasonable amount of time, even on corpora as large as the Web. In such a live query system source selection—determining which sources contribute answers to a query—is a crucial step. In this article we propose to use lightweight data summaries for determining relevant sources during query evaluation. We compare several data structures and hash functions with respect to their suitability for building such summaries, stressing benefits for queries that contain joins and require ranking of results and sources. We elaborate on join variants, join ordering and ranking. We analyse the different approaches theoretically and provide results of an extensive experimental evaluation.

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Authors and Affiliations

  1. Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland

    Jürgen Umbrich, Marcel Karnstedt & Axel Polleres

  2. Max-Planck-Institut für Informatik, Saarbrücken, Germany

    Katja Hose

  3. Institute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Andreas Harth

Authors
  1. Jürgen Umbrich
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  2. Katja Hose
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  3. Marcel Karnstedt
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  4. Andreas Harth
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  5. Axel Polleres
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Correspondence to Andreas Harth.

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Umbrich, J., Hose, K., Karnstedt, M. et al. Comparing data summaries for processing live queries over Linked Data. World Wide Web 14, 495–544 (2011). https://doi.org/10.1007/s11280-010-0107-z

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