Skip to main content
SpringerLink
Log in
Book cover

International Conference on Scalable Uncertainty Management

SUM 2014: Scalable Uncertainty Management pp 251–264Cite as

Integrity Constraints for Probabilistic Spatio-Temporal Knowledgebases

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8720))

Abstract

We formally introduce integrity constraints for probabilistic spatio-temporal knowledgebases. We start by defining the syntax and semantics of PST knowledgebases. This definition generalizes the SPOT framework which is a declarative framework for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because of uncertainty. We augment the previous definition by adding a type of non-atomic formula that expresses integrity constraints. The result is a highly expressive formalism for knowledge representation dealing with probabilistic spatio-temporal data. Our main results concern the complexity of checking the consistency of PST knowledgebases.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agarwal, P.K., Arge, L., Erickson, J.: Indexing moving points. J. Comput. Syst. Sci. 66(1), 207–243 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  2. 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 

  3. Cohn, A.G., Hazarika, S.M.: Qualitative spatial representation and reasoning: An overview. Fundam. Inform. 46(1-2), 1–29 (2001)

    MATH  MathSciNet  Google Scholar 

  4. Doder, D., Grant, J., Ognjanović, Z.: Probabilistic logics for objects located in space and time. J. of Logic and Computation 23(3), 487–515 (2013)

    Article  MATH  Google Scholar 

  5. 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 

  6. 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 

  7. Flesca, S., Furfaro, F., Parisi, F.: Consistency checking and querying in probabilistic databases under integrity constraints. J. Comput. Syst. Sci. 80(7), 1448–1489 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  8. Gabelaia, D., Kontchakov, R., Kurucz, Á., Wolter, F., Zakharyaschev, M.: Combining spatial and temporal logics: Expressiveness vs. complexity. J. Artif. Intell. Res. 23, 167–243 (2005)

    MATH  MathSciNet  Google Scholar 

  9. 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)

    Chapter  Google Scholar 

  10. 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)

    Article  MATH  MathSciNet  Google Scholar 

  11. 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)

    Chapter  Google Scholar 

  12. 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 

  13. Knapp, A., Merz, S., Wirsing, M., Zappe, J.: Specification and refinement of mobile systems in mtla and mobile uml. Theor. Comput. Sci. 351(2), 184–202 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  14. Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition, 2nd edn. Springer (2011)

    Google Scholar 

  15. Lukasiewicz, T.: Probabilistic logic programming with conditional constraints. ACM Trans. on Computational Logic 2(3), 289–339 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  16. Mittu, R., Ross, R.: Building upon the coalitions agent experiment (CoAx) - integration of multimedia information in gccs-m using impact. In: Multimedia Information Systems, pp. 35–44 (2003)

    Google Scholar 

  17. Muller, P.: A qualitative theory of motion based on spatio-temporal primitives. In: Int. Conf. on Principles of Knowledge Representation and Reasoning (KR), pp. 131–143 (1998)

    Google Scholar 

  18. Papadimitriou, C.H., Steiglitz, K.: Combinatorial optimization: algorithms and complexity. Prentice-Hall, Inc., Upper Saddle River (1982)

    Google Scholar 

  19. Parisi, F., Grant, J.: Repairs and consistent answers for inconsistent probabilistic spatio-temporal databases. In: Straccia, U., Cali, A. (eds.) SUM 2014. LNCS, vol. 8720, pp. 265–279. Springer, Heidelberg (2014)

    Google Scholar 

  20. 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)

    Chapter  Google Scholar 

  21. 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)

    Chapter  Google Scholar 

  22. Parisi, F., Sliva, A., Subrahmanian, V.S.: A temporal database forecasting algebra. Int. J. of Approximate Reasoning 54(7), 827–860 (2013)

    Article  MathSciNet  Google Scholar 

  23. Parker, A., Subrahmanian, V.S., Grant, J.: A logical formulation of probabilistic spatial databases. IEEE TKDE, 1541–1556 (2007)

    Google Scholar 

  24. 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 

  25. Parker, A., Infantes, G., Subrahmanian, V.S., Grant, J.: An agm-based belief revision mechanism for probabilistic spatio-temporal logics. In: AAAI, pp. 511–516 (2008)

    Google Scholar 

  26. Parker, A., Yaman, F., Nau, D.S., Subrahmanian, V.S.: Probabilistic go theories. In: Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 501–506 (2007)

    Google Scholar 

  27. 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)

    Article  Google Scholar 

  28. Southey, F., Loh, W., Wilkinson, D.F.: Inferring complex agent motions from partial trajectory observations. In: IJCAI, pp. 2631–2637 (2007)

    Google Scholar 

  29. 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 

  30. Wolter, F., Zakharyaschev, M.: Spatio-temporal representation and reasoning based on RCC-8. In: KR, pp. 3–14 (2000)

    Google Scholar 

  31. Yaman, F., Nau, D., Subrahmanian, V.: A motion closed world assumption. In: Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 621–626 (2005)

    Google Scholar 

  32. Yaman, F., Nau, D.S., Subrahmanian, V.S.: A logic of motion. In: KR, pp. 85–94 (2004)

    Google Scholar 

  33. Yaman, F., Nau, D.S., Subrahmanian, V.S.: Going far, logically. In: Int. Joint Conf. on Artificial Intelligence (IJCAI), pp. 615–620 (2005)

    Google Scholar 

  34. Zhang, M., Chen, S., Jensen, C.S., Ooi, B.C., Zhang, Z.: Effectively indexing uncertain moving objects for predictive queries. PVLDB 2(1), 1198–1209 (2009)

    Google Scholar 

  35. Zheng, K., Trajcevski, G., Zhou, X., Scheuermann, P.: Probabilistic range queries for uncertain trajectories on road networks. In: EDBT, pp. 283–294 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, Italy

    Francesco Parisi

  2. Department of Computer Science and UMIACS, University of Maryland, College Park, USA

    John Grant

Authors
  1. Francesco Parisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Grant
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Istituto di Scienza e Tecnologie dell’Informazione (ISTI - CNR), Pisa, Italy

    Umberto Straccia

  2. Department of Computer Science and Information Systems, University of London, London, United Kingdom

    Andrea Calì

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Parisi, F., Grant, J. (2014). Integrity Constraints for Probabilistic Spatio-Temporal Knowledgebases. In: Straccia, U., Calì, A. (eds) Scalable Uncertainty Management. SUM 2014. Lecture Notes in Computer Science(), vol 8720. Springer, Cham. https://doi.org/10.1007/978-3-319-11508-5_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11508-5_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11507-8

  • Online ISBN: 978-3-319-11508-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation