On Top-K Queries Over Evidential Data

Fatma Ezzahra Bousnina, Mouna Chebbah, Mohamed Anis Bach Tobji, Allel Hadjali, Boutheina Ben Yaghlane

2017

Abstract

Uncertain data are obvious in a lot of domains such as sensor networks, multimedia, social media, etc. Top-k queries provide ordered results according to a defined score. This kind of queries represents an important tool for exploring uncertain data. Most of works cope with certain data and with probabilistic top-k queries. However, at the best of our knowledge there is no work that exploits the Top-k semantics in the Evidence Theory context. In this paper, we introduce a new score function suitable for Evidential Data. Since the result of the score function is an interval, we adopt a comparison method for ranking intervals. Finally we extend the usual semantics/interpretations of top-k queries to the evidential scenario.

References

  1. Amato, G., Rabitti, F., Savino, P., and Zezula, P. (2003). Region proximity in metric spaces and its use for approximate similarity search. ACM Transactions on Information Systems (TOIS), 21(2):192-227.
  2. Andritsos, P., Fuxman, A., and Miller, R. J. (2006). Clean answers over dirty databases: A probabilistic approach. In 22nd International Conference on Data Engineering (ICDE'06), pages 30-30. IEEE.
  3. Arrow, K. J. (2012). Social choice and individual values, volume 12. Yale university press.
  4. Bell, D. A., Guan, J. W., and Lee, S. K. (1996). Generalized union and project operations for pooling uncertain and imprecise information. Data & Knowledge Engineering (DKE), 18:89-117.
  5. Ben Yaghlane, A., Denoeux, T., and Mellouli, K. (2008). Elicitation of expert opinions for constructing belief functions. Uncertainty and Intelligent Information Systems, pages 75-88.
  6. Borda, J. C. (1781). Mémoire sur les élections au scrutin. Translated in the political theory of condorcet. Sommerlad F, Mclean I. Social studies, Oxford, 1989.
  7. Bousnina, F. E., Bach Tobji, M. A., Chebbah, M., Liétard, L., and Ben Yaghlane, B. (2015). A new formalism for evidential databases. In 22nd International Symposium on Methodologies for Intelligent Systems (ISMIS), Foundations of Intelligent Systems, pages 31- 40. Springer.
  8. Cheng, R., Kalashnikov, D. V., and Prabhakar, S. (2004a). Querying imprecise data in moving object environments. IEEE Transactions on Knowledge and Data Engineering (TKDE), 16(9):1112-1127.
  9. Cheng, R., Xia, Y., Prabhakar, S., Shah, R., and Vitter, J. S. (2004b). Efficient indexing methods for probabilistic threshold queries over uncertain data. In 13th International Conference on Very Large Data Bases (VLDB), pages 876-887. VLDB Endowment.
  10. Chung, B. S., Lee, W.-C., and Chen, A. L. (2009). Processing probabilistic spatio-temporal range queries over moving objects with uncertainty. In 12th International Conference on Extending Database Technology, Advances in Database Technology, pages 60-71. ACM.
  11. Considine, J., Li, F., Kollios, G., and Byers, J. (2004). Approximate aggregation techniques for sensor databases. In 20th International Conference on Data Engineering (ICDE), pages 449-460. IEEE.
  12. Dempster, A. P. (1967). Upper and lower probabilities induced by a multiple valued mapping. The Annals of Mathematical Statistics, 38(2):325-339.
  13. Dempster, A. P. (1968). A generalization of bayesian inference. Journal of the Royal Statistical Society, Series B, 30:205-247.
  14. Ding, X. and Jin, H. (2012). Efficient and progressive algorithms for distributed skyline queries over uncertain data. IEEE Transactions on Knowledge and Data Engineering (TKDE), 24(8):1448-1462.
  15. Elmi, S., Benouaret, K., HadjAli, A., Bach Tobji, M. A., and Ben Yaghlane, B. (2014). Computing skyline from evidential data. In 8th International Conference on Scalable Uncertainty Management (SUM), pages 148-161, Oxford, UK.
  16. Elmi, S., Benouaret, K., HadjAli, A., Bach Tobji, M. A., and Ben Yaghlane, B. (2015). Requeˆtes skyline en présence des données évidentielles. In Extraction et Gestion des Connaissances (EGC), pages 215-220.
  17. Ennaceur, A., Elouedi, Z., and Lefevre, E. (2014). Multicriteria decision making method with belief preference relations. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 22(04):573-590.
  18. Fagin, R. (1996). Combining fuzzy information from multiple systems. In 15th ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, pages 216-226. ACM.
  19. Fagin, R. (1998). Fuzzy queries in multimedia database systems. In 17th ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, pages 1- 10. ACM.
  20. Ilyas, I. F., Beskales, G., and Soliman, M. A. (2008). A survey of top-k query processing techniques in relational database systems. ACM Computing Surveys (CSUR), 40(4):11.
  21. Inada, K.-i. (1964). A note on the simple majority decision rule. Econometrica, Journal of the Econometric Society, pages 525-531.
  22. Inada, K.-i. (1969). The simple majority decision rule. Econometrica, Journal of the Econometric Society, pages 490-506.
  23. Ishibuchi, H. and Tanaka, H. (1990). Multiobjective programming in optimization of the interval objective function. European Journal of Operational Research (EJOR), 48(2):219-225.
  24. Kemeny, J. G. and Snell, L. (1962). Preference ranking: an axiomatic approach. Mathematical models in the social sciences, pages 9-23.
  25. Kendall, M. (1990). Rank correlation methods. Oxford University Press, 5th edition.
  26. Kundu, S. (1997). Min-transitivity of fuzzy leftness relationship and its application to decision making. Fuzzy sets and systems, 86(3):357-367.
  27. Laplace, P. S. d. (1812). Théorie analytique des probabilités. Courcier, Paris.
  28. Lee, S. K. (1992a). An extended relational database model for uncertain and imprecise information. In 18th Conference on Very Large Data Bases (VLDB), pages 211-220, Canada.
  29. Lee, S. K. (1992b). Imprecise and uncertain information in databases : an evidential approach. In 8th International Conference on Data Engineering (ICDE), pages 614-621.
  30. Re, C., Dalvi, N., and Suciu, D. (2007). Efficient topk query evaluation on probabilistic data. In IEEE 23rd International Conference on Data Engineering (ICDE), pages 886-895. IEEE.
  31. Salo, A. and Hämäläinen, R. (1992). Processing interval judgments in the analytic hierarchy process.
  32. Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.
  33. Silberstein, A., Braynard, R., Ellis, C., Munagala, K., and Yang, J. (2006). A sampling-based approach to optimizing top-k queries in sensor networks. In 22nd International Conference on Data Engineering (ICDE), pages 68-68. IEEE.
  34. Soliman, M. A., Ilyas, I. F., and Chang, K. C.-C. (2007). Top-k query processing in uncertain databases. In 23rd International Conference on Data Engineering (ICDE), pages 896-905. IEEE.
  35. Tao, Y., Xiao, X., and Cheng, R. (2007). Range search on multidimensional uncertain data. ACM Transactions on Database Systems (TODS), 32(3):15.
  36. Theobald, M., Schenkel, R., and Weikum, G. (2005). An efficient and versatile query engine for topx search. In 31st International Conference on Very large Data Bases (VLDB), pages 625-636. VLDB Endowment.
  37. Wang, Y.-M., Yang, J.-B., and Xu, D.-L. (2005). A preference aggregation method through the estimation of utility intervals. Computers & Operations Research, 32(8):2027-2049.
  38. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8:338-353.
  39. Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1:3-28.
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Paper Citation


in Harvard Style

Bousnina F., Chebbah M., Bach Tobji M., Hadjali A. and Ben Yaghlane B. (2017). On Top-K Queries Over Evidential Data . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 106-113. DOI: 10.5220/0006317701060113


in Bibtex Style

@conference{iceis17,
author={Fatma Ezzahra Bousnina and Mouna Chebbah and Mohamed Anis Bach Tobji and Allel Hadjali and Boutheina Ben Yaghlane},
title={On Top-K Queries Over Evidential Data},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={106-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006317701060113},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - On Top-K Queries Over Evidential Data
SN - 978-989-758-247-9
AU - Bousnina F.
AU - Chebbah M.
AU - Bach Tobji M.
AU - Hadjali A.
AU - Ben Yaghlane B.
PY - 2017
SP - 106
EP - 113
DO - 10.5220/0006317701060113