Workload-Aware Self-tuning Histograms for the Semantic Web

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


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.


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.



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 and about the Semagrow system please see


  1. 1.
    Bruno, N., Chaudhuri, S.: Exploiting statistics on query expressions for optimization. In: Proceedings of the 2002 ACM International Conference on Management of Data (SIGMOD 2002), New York, NY, USA, pp. 263–274. ACM (2002)Google Scholar
  2. 2.
    Stillger, M., Lohman, G.M., Markl, V., Kandil, M.: LEO - DB2’s LEarning optimizer. In: Proceedings of the 27th International Conference on Very Large Data Bases, VLDB 2001, San Francisco, CA, USA, pp. 19–28. Morgan Kaufmann Publishers Inc. (2001)Google Scholar
  3. 3.
    Aboulnaga, A., Chaudhuri, S.: Self-tuning histograms: building histograms without looking at data. In: Proceedings of the 1999 ACM International Conference on Management of Data (SIGMOD 1999), New York, NY, USA, pp. 181–192. ACM (1999)Google Scholar
  4. 4.
    Bruno, N., Chaudhuri, S., Gravano, L.: STHoles: a multidimensional workload-aware histogram. In: Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data (SIGMOD 2001), pp. 211–222 (2001)Google Scholar
  5. 5.
    Srivastava, U., Haas, P.J., Markl, V., Kutsch, M., Tran, T.M.: ISOMER: consistent histogram construction using query feedback. In: Proceedings of the 22nd International Conference on Data Engineering (ICDE 2006), Washington, DC, USA. IEEE Computer Society (2006)Google Scholar
  6. 6.
    Roh, Y.J., Kim, J.H., Chung, Y.D., Son, J.H., Kim, M.H.: Hierarchically organized skew-tolerant histograms for geographic data objects. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, New York, NY, USA, pp. 627–638. ACM (2010)Google Scholar
  7. 7.
    Kaushik, R., Suciu, D.: Consistent histograms in the presence of distinct value counts. Proc. VLDB Endowment 2, 850–861 (2009)CrossRefGoogle Scholar
  8. 8.
    Markl, V., Haas, P.J., Kutsch, M., Megiddo, N., Srivastava, U., Tran, T.M.: Consistent selectivity estimation via maximum entropy. VLDB J. 16, 55–76 (2007)CrossRefGoogle Scholar
  9. 9.
    Bruno, N., Chaudhuri, S., Weikum, G.: Database tuning using online algorithms. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 741–744. Springer, New York (2009)Google Scholar
  10. 10.
    Khachatryan, A., Müller, E., Stier, C., Böhm, K.: Sensitivity of self-tuning histograms: query order affecting accuracy and robustness. In: Ailamaki, A., Bowers, S. (eds.) SSDBM 2012. LNCS, vol. 7338, pp. 334–342. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31235-9_22 Google Scholar
  11. 11.
    Chaudhuri, S., Ganti, V., Gravano, L.: Selectivity estimation for string predicates: overcoming the underestimation problem. In: Proceedings of the 20th International Conference on Data Engineering (ICDE 2004), Washington, DC, USA. IEEE Computer Society (2004)Google Scholar
  12. 12.
    Lim, L., Wang, M., Vitter, J.S.: CXHist: an on-line classification-based histogram for XML string selectivity estimation. In: Proceedings of the 31st International Conference on Very Large Data Bases (VLDB 2005), Trondheim, Norway, 30 August – 2 September 2005, pp. 1187–1198 (2005)Google Scholar
  13. 13.
    Ding, L., Finin, T., Joshi, A., Pan, R., Cost, R.S., Peng, Y., Reddivari, P., Doshi, V., Sachs, J.: Swoogle: a search and metadata engine for the semantic web. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, CIKM 2004, New York, NY, USA, pp. 652–659. ACM (2004)Google Scholar
  14. 14.
    Auer, S., Demter, J., Martin, M., Lehmann, J.: LODStats – an extensible framework for high-performance dataset analytics. In: Teije, A., Völker, J., Handschuh, S., Stuckenschmidt, H., d’Acquin, M., Nikolov, A., Aussenac-Gilles, N., Hernandez, N. (eds.) EKAW 2012. LNCS (LNAI), vol. 7603, pp. 353–362. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33876-2_31 CrossRefGoogle Scholar
  15. 15.
    Langegger, A., Wöss, W.: RDFStats - an extensible RDF statistics generator and library. In: 23rd International Workshop on Database and Expert Systems Applications, Los Alamitos, CA, USA, pp. 79–83. IEEE Computer Society (2009)Google Scholar
  16. 16.
    Harth, A., Hose, K., Karnstedt, M., Polleres, A., Sattler, K.U., Umbrich, J.: Data summaries for on-demand queries over linked data. In: Proceedings of the 19th International World Wide Web Conference (WWW 2010), Raleigh, NC, USA, 26–30 April 2010Google Scholar
  17. 17.
    Zoulis, N., Mavroudi, E., Lykoura, A., Charalambidis, A., Konstantopoulos, S.: Workload-aware self-tuning histograms of string data. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds.) DEXA 2015. LNCS, vol. 9261, pp. 285–299. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-22849-5_20 CrossRefGoogle Scholar
  18. 18.
    Winkler, W.E.: String comparator metrics and enhanced decision rules in the Fellegi-Sunter model of record linkage. In: Proceedings of the Section on Survey Research Methods, Technical report, pp. 354–359. American Statistical Association (1990)Google Scholar
  19. 19.
    Charalambidis, A., Troumpoukis, A., Konstantopoulos, S.: SemaGrow: optimizing federated SPARQL queries. In: Proceedings of the 11th International Conference on Semantic Systems (SEMANTiCS 2015), Vienna, Austria, 15–18 September 2015Google Scholar
  20. 20.
    Charalambidis, A., Konstantopoulos, S., Karkaletsis, V.: Dataset descriptions for optimizing federated querying. In: Companion Proceedings of the 24th International World Wide Web Conference Companion Proceedings (WWW 2015), Poster Session, Florence, Italy, 18–22 May 2015Google Scholar
  21. 21.
    Celli, F., Keizer, J., Jaques, Y., Konstantopoulos, S., Vudragović, D.: Discovering, indexing and interlinking information resources. F1000Research 4 (2015). (Version 2; referees: 3 approved)Google Scholar

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

Personalised recommendations