Repairs and Consistent Answers for Inconsistent Probabilistic Spatio-Temporal Databases

  • Francesco Parisi
  • John Grant
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8720)


We formally introduce the concept of repair and consistent answer for inconsistent probabilistic spatio-temporal databases. We start by defining the syntax and semantics of SPOT databases, a declarative framework that has been explored in recent years for the representation of spatio-temporal data with uncertainty expressed as probability intervals. In this framework we study two types of repairs, that is, minimal modifications that lead to consistent databases: maximal consistent subsets and probability interval expansion. We also extend the concept of consistent answer to this framework and find that this can be done in several different ways. In emphasizing tractable cases we propose polynomial-time algorithms for computing consistent answers and repairs based on probability interval expansion, and experimentally validate our approach.


Integrity Constraint Probability Interval Query Answer Probability Bound Optimistic Answer 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agarwal, D., Chen, D., Lin, L.J., Shanmugasundaram, J., Vee, E.: Forecasting high-dimensional data. In: SIGMOD Conference, pp. 1003–1012 (2010)Google Scholar
  2. 2.
    Agarwal, P.K., Arge, L., Erickson, J.: Indexing moving points. J. Comput. Syst. Sci. 66(1), 207–243 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Akdere, M., Çetintemel, U., Riondato, M., Upfal, E., Zdonik, S.B.: The case for predictive database systems: Opportunities and challenges. In: CIDR, pp. 167–174 (2011)Google Scholar
  4. 4.
    Arenas, M., Bertossi, L.E., Chomicki, J.: Consistent query answers in inconsistent databases. In: Int. Symposium on Principles of Database Systems (PODS), pp. 68–79 (1999)Google Scholar
  5. 5.
    Benjelloun, O., Sarma, A.D., Halevy, A.Y., Widom, J.: Uldbs: Databases with uncertainty and lineage. In: VLDB, pp. 953–964 (2006)Google Scholar
  6. 6.
    Bertossi, L.: Database Repairing and Consistent Query Answering. Morgan & Claypool Publishers (2011)Google Scholar
  7. 7.
    Bertossi, L.E., Bravo, L., Franconi, E., Lopatenko, A.: The complexity and approximation of fixing numerical attributes in databases under integrity constraints. Inf. Syst. 33(4-5), 407–434 (2008)CrossRefGoogle Scholar
  8. 8.
    Cao, H., Wolfson, O., Trajcevski, G.: Spatio-temporal data reduction with deterministic error bounds. VLDB J. 15(3), 211–228 (2006)CrossRefGoogle Scholar
  9. 9.
    Chomicki, J., Marcinkowski, J.: Minimal-change integrity maintenance using tuple deletions. Inf. Comput. 197(1-2), 90–121 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Dai, X., Yiu, M.L., Mamoulis, N., Tao, Y., Vaitis, M.: Probabilistic spatial queries on existentially uncertain data. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 400–417. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Fazzinga, B., Flesca, S., Furfaro, F., Parisi, F.: Cleaning trajectory data of RFID-monitored objects through conditioning under integrity constraints. In: Int. Conf. on Extending Database Technology (EDBT), pp. 379–390 (2014)Google Scholar
  12. 12.
    Fazzinga, B., Flesca, S., Furfaro, F., Parisi, F.: Offline cleaning of RFID trajectory data. In: Int. Conf. on Scientific and Statistical Database Management (SSDBM), p. 5 (2014)Google Scholar
  13. 13.
    Flesca, S., Furfaro, F., Parisi, F.: Preferred database repairs under aggregate constraints. In: Prade, H., Subrahmanian, V.S. (eds.) SUM 2007. LNCS (LNAI), vol. 4772, pp. 215–229. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Flesca, S., Furfaro, F., Parisi, F.: Consistent answers to boolean aggregate queries under aggregate constraints. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010, Part II. LNCS, vol. 6262, pp. 285–299. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Flesca, S., Furfaro, F., Parisi, F.: Querying and repairing inconsistent numerical databases. ACM Trans. Database Syst. 35(2) (2010)Google Scholar
  16. 16.
    Flesca, S., Furfaro, F., Parisi, F.: Range-consistent answers of aggregate queries under aggregate constraints. In: Deshpande, A., Hunter, A. (eds.) SUM 2010. LNCS, vol. 6379, pp. 163–176. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Grant, J., Molinaro, C., Parisi, F.: Aggregate count queries in probabilistic spatio-temporal databases. In: Liu, W., Subrahmanian, V.S., Wijsen, J. (eds.) SUM 2013. LNCS, vol. 8078, pp. 255–268. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    Grant, J., Parisi, F., Parker, A., Subrahmanian, V.S.: An agm-style belief revision mechanism for probabilistic spatio-temporal logics. Artif. Intell. 174(1), 72–104 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Grant, J., Parisi, F., Subrahmanian, V.S.: Research in probabilistic spatiotemporal databases: The SPOT framework. In: Ma, Z., Yan, L. (eds.) Advances in Probabilistic Databases. STUDFUZZ, vol. 304, pp. 1–22. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  20. 20.
    Hadjieleftheriou, M., Kollios, G., Tsotras, V.J., Gunopulos, D.: Efficient indexing of spatiotemporal objects. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 251–268. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  21. 21.
    Hammel, T., Rogers, T.J., Yetso, B.: Fusing live sensor data into situational multimedia views. In: Multimedia Information Systems, pp. 145–156 (2003)Google Scholar
  22. 22.
    Kollios, G., Gunopulos, D., Tsotras, V.J.: On indexing mobile objects. In: Int. Symposium on Principles of Database Systems (PODS), pp. 261–272 (1999)Google Scholar
  23. 23.
    Martinez, M.V., Parisi, F., Pugliese, A., Simari, G.I., Subrahmanian, V.S.: Inconsistency management policies. In: KR, pp. 367–377 (2008)Google Scholar
  24. 24.
    Martinez, M.V., Parisi, F., Pugliese, A., Simari, G.I., Subrahmanian, V.S.: Policy-based inconsistency management in relational databases. Int. J. Approx. Reas. 55(2), 501–526 (2014)CrossRefMathSciNetGoogle Scholar
  25. 25.
    Mittu, R., Ross, R.: Building upon the coalitions agent experiment (CoAx) - integration of multimedia information in gccs-m using impact. In: Multimedia Inf. Syst., pp. 35–44 (2003)Google Scholar
  26. 26.
    Parisi, F., Grant, J.: Integrity constraints for probabilistic spatio-temporal knowledgebases. In: Straccia, U., Cali, A. (eds.) SUM 2014. LNCS, vol. 8720, pp. 251–264. Springer, Heidelberg (2014)Google Scholar
  27. 27.
    Parisi, F., Parker, A., Grant, J., Subrahmanian, V.S.: Scaling cautious selection in spatial probabilistic temporal databases. In: Jeansoulin, R., Papini, O., Prade, H., Schockaert, S. (eds.) Methods for Handling Imperfect Spatial Information. STUDFUZZ, vol. 256, pp. 307–340. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Parisi, F., Sliva, A., Subrahmanian, V.S.: Embedding forecast operators in databases. In: Benferhat, S., Grant, J. (eds.) SUM 2011. LNCS, vol. 6929, pp. 373–386. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  29. 29.
    Parisi, F., Sliva, A., Subrahmanian, V.S.: A temporal database forecasting algebra. Int. J. of Approximate Reasoning 54(7), 827–860 (2013)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Parker, A., Subrahmanian, V.S., Grant, J.: A logical formulation of probabilistic spatial databases. IEEE TKDE, 1541–1556 (2007)Google Scholar
  31. 31.
    Parker, A., Infantes, G., Grant, J., Subrahmanian, V.S.: Spot databases: Efficient consistency checking and optimistic selection in probabilistic spatial databases. IEEE TKDE 21(1), 92–107 (2009)Google Scholar
  32. 32.
    Pelanis, M., Saltenis, S., Jensen, C.S.: Indexing the past, present, and anticipated future positions of moving objects. ACM Trans. Database Syst. 31(1), 255–298 (2006)CrossRefGoogle Scholar
  33. 33.
    Pfoser, D., Jensen, C.S., Theodoridis, Y.: Novel approaches in query processing for moving object trajectories. In: VLDB, pp. 395–406 (2000)Google Scholar
  34. 34.
    Southey, F., Loh, W., Wilkinson, D.F.: Inferring complex agent motions from partial trajectory observations. In: IJCAI, pp. 2631–2637 (2007)Google Scholar
  35. 35.
    Tao, Y., Cheng, R., Xiao, X., Ngai, W.K., Kao, B., Prabhakar, S.: Indexing multi-dimensional uncertain data with arbitrary probability density functions. In: VLDB, pp. 922–933 (2005)Google Scholar
  36. 36.
    Tao, Y., Papadias, D., Sun, J.: The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: VLDB, pp. 790–801 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Francesco Parisi
    • 1
  • John Grant
    • 2
  1. 1.Department of Informatics, Modeling, Electronics and System EngineeringUniversity of CalabriaItaly
  2. 2.Department of Computer Science and UMIACSUniversity of MarylandCollege ParkUSA

Personalised recommendations