Minimal Spatio-Temporal Database Repairs

  • Markus Mauder
  • Markus Reisinger
  • Tobias Emrich
  • Andreas ZüfleEmail author
  • Matthias Renz
  • Goce Trajcevski
  • Roberto Tamassia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)


This work addresses the problem of efficient detection and fixing of inconsistencies in spatio-temporal databases. In contrast to traditional database settings, where integrity constraints pertain to explicitly stored values and values defined via views and aggregates, spatio-temporal data may exhibit other types of constraint violations that cannot be tied to stored or aggregated values. The main reason is that spatio-temporal phenomena are continuous but their database representations are discrete. Thus, the constraints are semantic in nature, as opposed to being dependent on the actual stored data. We give a general definition of semantic constraints of a trajectory database and define rules to repair violations of these constraints. In order to minimize the distortion of the state of the database, we aim at minimizing the changes needed for repairing violations of such semantic constraints. Towards this goal, we define a measure of dissimilarity between the initial database and its repaired state. Also, to minimize dissimilarity, we propose several simple rules of space- and time-distortion that shift inconsistent observations in space and time to remove inconsistencies. Our evaluation shows that these rules often run into local minima, and thus may not be able to repair a database. To remedy this problem, we propose a hybrid approach that chooses between several possible space and time distortions. We show that a greedy approach which always chooses the locally best repair may still run into local minima and propose a simulated-annealing approach that combines greedy and random repairs to avoid these local minima.


Simulated Annealing Linear Temporal Logic Dead Reckoning Greedy Approach Semantic Constraint 
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.


  1. 1.
    Mobile subscribers 2014: ITU World Telecommunication/ICT Indicatorsdatabase.
  2. 2.
    Achtert, E., Kriegel, H.-P., Schubert, E., Zimek, A.: Interactive data mining with 3d-parallel-coordinate-trees. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 1009–1012. ACM (2013)Google Scholar
  3. 3.
    Arenas, M., Bertossi, L., Chomicki, J.: Consistent query answers in inconsistent databases. In: Proceedings of the Eighteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 1999, pp. 68–79 (1999)Google Scholar
  4. 4.
    Bohannon, P., Fan, W., Flaster, M., Rastogi, R.: A cost-based model and effective heuristic for repairing constraints by value modification. In: Proceedings of SIGMOD, pp. 143–154 (2005)Google Scholar
  5. 5.
    Brinkhoff, T., Kriegel, T., Seeger, B.: Efficient processing of spatial joins using R-trees. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., pp. 237–246, 26–28 May 1993Google Scholar
  6. 6.
    Cheng, R., Emrich, R., Kriegel, H., Mamoulis, N., Renz, M., Trajcevski, G., Züfle, A.: Managing uncertainty in spatial and spatio-temporal data. In: IEEE 30th International Conference on Data Engineering, Chicago, ICDE 2014, IL, USA, March 31 - April 4, 2014, pp. 1302–1305 (2014)Google Scholar
  7. 7.
    Emerson, E.: Temporal and modal logic. In: Handbook of Theoretical Computer Science, Volume B: Formal Models and Sematics (B) (1990)Google Scholar
  8. 8.
    Emrich, T., Kriegel, H.-P., Mamoulis, N., Renz, M., Züfle, A.: Querying uncertain spatio-temporal data. In: Kementsietsidis, A., Salles, M.A.V. (eds) ICDE, pp. 354–365. IEEE Computer Society (2012)Google Scholar
  9. 9.
    Gindele, T., Brechtel, S., Dillmann, R.: Learning driver behavior models from traffic observations for decision making and planning. IEEE Intell. Transport. Syst. Mag. 7(1), 69–79 (2015)CrossRefGoogle Scholar
  10. 10.
    Güting, R.H., Schneider, M.: Moving Objects Databases. Morgan Kaufmann, Amsterdam (2005)Google Scholar
  11. 11.
    Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big data: The next frontier for innovation, competition, and productivity. McKinsey and Company Report, May 2009Google Scholar
  12. 12.
    Parisi, F., Grant, J.: Repairs and consistent answers for inconsistent probabilistic spatio-temporal databases. In: Straccia, U., Calì, A. (eds.) SUM 2014. LNCS, vol. 8720, pp. 265–279. Springer, Heidelberg (2014) Google Scholar
  13. 13.
    Parker, A., Subrahmanian, V., Grant, J.: A logical formulation of probabilistic spatial databases. IEEE Trans. Knowl. Data Eng. 19(11), 1541–1556 (2007)CrossRefGoogle Scholar
  14. 14.
    Pitoura, E., Samaras, G.: Locating objects in mobile computing. IEEE Trans. Knowl. Data Eng. (TKDE) 13(4), 571–592 (2001)CrossRefGoogle Scholar
  15. 15.
    Rozier, K., Vardi, M.: LTL satisfiability checking. In: Automated Technology for Verification and Analysis (2011)Google Scholar
  16. 16.
    Schiller, J., Voisard, A.: Location-Based Services. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2004) Google Scholar
  17. 17.
    Wijsen, J.: Database repairing using updates. ACM Trans. Database Syst. 30(3), 722–768 (2005)CrossRefGoogle Scholar
  18. 18.
    Zhang, B., Trajcevski, G.: The tale of (fusing) two uncertainties. In: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Dallas/Fort Worth, TX, USA, pp. 521–524, 4–7 November 2014Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Markus Mauder
    • 1
  • Markus Reisinger
    • 1
  • Tobias Emrich
    • 1
  • Andreas Züfle
    • 1
    Email author
  • Matthias Renz
    • 1
  • Goce Trajcevski
    • 2
  • Roberto Tamassia
    • 3
  1. 1.Ludwig-Maximilians-Universitüt MünchenMunichGermany
  2. 2.Northwestern UniversityEvanstonUSA
  3. 3.Brown UniversityProvidenceUSA

Personalised recommendations