Linked Open Government Data Analytics

  • Evangelos Kalampokis
  • Efthimios Tambouris
  • Konstantinos Tarabanis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8074)

Abstract

Although the recently launched Open Government Data (OGD) movement promised to provide a number of benefits, recent studies have shown that its full potential has not yet realized. The difficulty in exploiting open data seems surprising if we consider the huge importance data have in modern societies. In this paper we claim that the real value of OGD will unveil from performing data analytics on top of combined statistical datasets that were previously closed in disparate sources and can now be linked to provide unexpected and unexplored insights. To support this claim, we describe the linked OGD analytics concept along with its technical requirements and demonstrate its end-user value employing a use case related to UK general elections. The use case revealed that there is a significant relationship between the probability one of the two main political parties (i.e. Labour Party and Conservative Party) to win in a UK constituency and the unemployment rate in the same constituency.

Keywords

Open government data linked data statistics data analytics 

References

  1. 1.
    Open Knowledge Foundation: The Open Data Handbook (2012), http://opendatahandbook.org
  2. 2.
    Kalampokis, E., Tambouris, E., Tarabanis, K.: A Classification Scheme for Open Government Data: Towards Linking Decentralized Data. International Journal of Web Engineering and Technology 6(3), 266–285 (2011)CrossRefGoogle Scholar
  3. 3.
    Janssen, M., Charalabidis, Y., Zuiderwijk, A.: Benefits, Adoption Barriers and Myths of Open Data and Open Government. Information Systems Management 29(4), 258–268 (2012)CrossRefGoogle Scholar
  4. 4.
    Tammisto, Y., Lindman, J.: Definition of Open Data Services in Software Business. In: Cusumano, M.A., Iyer, B., Venkatraman, N. (eds.) ICSOB 2012. LNBIP, vol. 114, pp. 297–303. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Böhm, C., Naumann, F., Freitag, M., George, S., Höfler, N., Köppelmann, M., Lehmann, C., Mascher, A., Schmidt, T.: Linking Open Government Data: What Journalists Wish They Had Known. In: 6th International Conference on Semantic Systems, vol. 34, ACM, New York (2010)Google Scholar
  6. 6.
    Shadbolt, N., O’Hara, K., Berners-Lee, T., Gibbins, N., Glaser, H., Hall, W., Schraefel, M.C.: Linked Open Government Data: Lessons from Data.gov.uk. IEEE Intelligent Systems 27(3), 16–24 (2012)CrossRefGoogle Scholar
  7. 7.
    Jourdan, Z., Rainer, R.K., Marshall, T.E.: Business Intelligence: An Analysis of the Literature. Information Systems Management 25(2), 121–131 (2008)CrossRefGoogle Scholar
  8. 8.
    Davies, H.T.O., Nutley, S.M., Smith, P.C. (eds.): What works? Evidence-based Policy and Practice in Public Services, UK (2000)Google Scholar
  9. 9.
    Scott, P., Kollman, K., Miller, J. (eds.): Computational Models of Political Economy. MIT Press (2002)Google Scholar
  10. 10.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. International Journal on Semantic Web and Information Systems 5(3), 1–22 (2009)CrossRefGoogle Scholar
  11. 11.
    Hausenblas, M.: Exploiting linked data to build web applications. IEEE Internet Computing 13(4), 68–73 (2009)CrossRefGoogle Scholar
  12. 12.
    W3C, The RDF Data Cube Vocabulary. W3C Working Draft (2013), http://www.w3.org/TR/vocab-data-cube/
  13. 13.
    Etcheverry, L., Vaisman, A.A.: Enhancing OLAP Analysis with Web Cubes. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 469–483. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Sheridan, J., Tennison, J.: Linking UK government data. In: WWW Workshop on Linked Data on the Web (2010)Google Scholar
  15. 15.
    Cyganiak, R., Hausenblas, M., McCuirc, E.: Official Statistics and the Practice of Data Fidelity. In: Wood, D. (ed.) Linking Government Data, pp. 135–151. Springer (2011)Google Scholar
  16. 16.
    Kämpgen, B.: DC Proposal: Online Analytical Processing of Statistical Linked Data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part II. LNCS, vol. 7032, pp. 301–308. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    van Huge, W.R., van Erp, M.: Linked Open Piracy: A Story about e-Science, Linked Data and Statistics. Journal on Data Semantics 1(3), 187–201 (2012)CrossRefGoogle Scholar
  18. 18.
    Paulheim, H.: Generating Possible Interpretations for Statistics from Linked Open Data. In: Simperl, E., Cimiano, P., Polleres, A., Corcho, O., Presutti, V. (eds.) ESWC 2012. LNCS, vol. 7295, pp. 560–574. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  19. 19.
    Kalampokis, E., Tambouris, E., Tarabanis, K.: Understanding the Predictive Power of Social Media. Internet Research (in press, 2013)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Evangelos Kalampokis
    • 1
    • 2
  • Efthimios Tambouris
    • 1
    • 2
  • Konstantinos Tarabanis
    • 1
    • 2
  1. 1.Centre for Research & TechnologyInformation Technologies InstituteHellasGreece
  2. 2.University of MacedoniaThessalonikiGreece

Personalised recommendations