Workload-Aware Self-tuning Histograms for the Semantic Web

  • Katerina Zamani
  • Angelos Charalambidis
  • Stasinos Konstantopoulos
  • Nickolas Zoulis
  • Effrosyni Mavroudi
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9940)

Abstract

Query processing systems typically rely on histograms, data structures that approximate data distribution, in order to optimize query execution. Histograms can be constructed by scanning the database tables and aggregating the values of the attributes in the table, or, more efficiently, progressively refined by analysing query results. Most of the relevant literature focuses on histograms of numerical data, exploiting the natural concept of a numerical range as an estimator of the volume of data that falls within the range. This, however, leaves Semantic Web data outside the scope of the histograms literature, as its most prominent datatype, the URI, does not offer itself to defining such ranges. This article first establishes a framework that formalises histograms over arbitrary data types and provides a formalism for specifying value ranges for different datatypes. This makes explicit the properties that ranges are required to have, so that histogram refinement algorithms are applicable. We demonstrate that our framework subsumes histograms over numerical data as a special case by using to formulate the state-of-the-art in numerical histograms. We then proceed to use the Jaro-Winkler metric to define URI ranges by exploiting the hierarchical nature of URI strings. This greatly extends the state of the art, where strings are treated as categorical data that can only be described by enumeration. We then present the open-source STRHist system that implements these ideas. We finally present empirical evaluation results using STRHist over a real dataset and query workload extracted from AGRIS, the most popular and widely used bibliographic database on agricultural research and technology.

Keywords

Resource Description Framework Query Optimizer Query Execution Query Plan Selectivity Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 318497. For more details about the SemaGrow project please see http://www.semagrow.eu and about the Semagrow system please see http://semagrow.github.io.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Katerina Zamani
    • 1
  • Angelos Charalambidis
    • 1
  • Stasinos Konstantopoulos
    • 1
  • Nickolas Zoulis
    • 1
    • 2
  • Effrosyni Mavroudi
    • 3
  1. 1.Institute of Informatics and TelecommunicationsNCSR ‘Demokritos’AthensGreece
  2. 2.Computer Science DepartmentAthens University of Economics and BusinessAthensGreece
  3. 3.School of Electrical and Computer EngineeringNational Technical University of AthensAthensGreece

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