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Compact representations of temporal databases

  • Antoon BronselaerEmail author
  • Christophe Billiet
  • Robin De Mol
  • Joachim Nielandt
  • Guy De Tré
Regular Paper
  • 35 Downloads

Abstract

This paper investigates the storage compactness of temporal merges. A temporal merge is a joint representation of a set of snapshots of the same database at different points in time. An axiomatic requirement for temporal merges is that each individual snapshot must be derivable from it. We first show that the usual temporal extension of a database is a special kind of temporal merge called a direct merge. For direct merges, finding the most compact representation is easy for separate relations. However, if snapshots feature foreign key dependencies, the representation may contain more tuples than strictly needed. We therefore study inclusion dependencies in temporal databases and show how to use them while maintaining minimality in our representation. Next, we argue that direct merges imply redundancy when they contain non-contiguous, value-equivalent tuples. We therefore introduce a more general kind of temporal merge known as a coherent merge and study its properties in depth. Deriving a minimal coherent merge from a direct one is shown to be NP-hard. However, several greedy algorithms are demonstrated to provide decent results in quadratic time. Next, an incremental algorithm for construction of coherent merges is presented. We provide experimental results on real-life data sets that support the usefulness of the contributions of this paper.

Keywords

Temporal database Inclusion dependency Coherent merge Compactness 

Notes

References

  1. 1.
    Jensen, C., Snodgrass, R.: Temporal data management. IEEE Trans. Knowl. Data Eng. 11(1), 36–44 (1999)CrossRefGoogle Scholar
  2. 2.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd edn. Wiley, New York (2013)Google Scholar
  3. 3.
    Castelltort, A., Laurent, A.: Representing history in graph-oriented NoSQL databases: a versioning system. In: 8th International Conference on Digital Information Management, ICDIM 2013, vol. 1, pp. 228–234 (2013).  https://doi.org/10.1109/ICDIM.2013.6694022
  4. 4.
    Böhlen, M.: Temporal database system implementations. SIGMOD Rec. 24(4), 53–60 (1995)CrossRefGoogle Scholar
  5. 5.
    Snodgrass, R., Ahn, I.: A taxonomy of time in databases. ACM SIGMOD Rec. 14(4), 236–246 (1985)CrossRefGoogle Scholar
  6. 6.
    Jensen, C., Snodgrass, R.: Semantics of time-varying information. Inf. Syst. 21(4), 311–352 (1996)CrossRefGoogle Scholar
  7. 7.
    Özsoyoglu, G., Snodgrass, R.: Temporal and real-time databases: a survey. IEEE Trans. Knowl. Data Eng. 7(4), 513–532 (1995)CrossRefGoogle Scholar
  8. 8.
    Jensen, C., et al.: The Consensus Glossary of Temporal Database Concepts February 1998 Version, pp. 367–405. Springer, Berlin (1998)Google Scholar
  9. 9.
    Lorentzos, N., Johnson, R.: Extending relational algebra to manipulate temporal data. Inf. Syst. 13(3), 289–296 (1988)CrossRefGoogle Scholar
  10. 10.
    Navathe, S., Ahmed, R.: A temporal relational model and a query language. Inf. Sci. 49, 147–175 (1989)CrossRefGoogle Scholar
  11. 11.
    Ahn, I., Snodgrass, R.: Partitioned storage for temporal databases. Inf. Syst. 13(4), 369–391 (1988)CrossRefGoogle Scholar
  12. 12.
    Lum, V., Dadam, P., Erbe, R., Guenauer, J., Pistor, P., Walch, G., Werner, H., Woodfill, J.: Design of an Integrated DBMS to Support Advanced Applications, pp. 31–49. Springer, Berlin (1987)Google Scholar
  13. 13.
    Gunadhi, H., Segev, A.: A framework for query optimization in temporal databases. In: Proceedings of the 5th International Conference on Statistical and Scientific Database Management, pp. 131–147 (1988)Google Scholar
  14. 14.
    Gao, D., Jensen, C., Snodgrass, R., Soo, M.: Join operations in temporal databases. VLDB J. 14, 2–29 (2005).  https://doi.org/10.1007/s00778-003-0111-3 CrossRefGoogle Scholar
  15. 15.
    Salzberg, B., Tsotras, V.: Comparison of access methods for time-evolving data. ACM Comput. Surv. 31(2), 185–221 (1999)CrossRefGoogle Scholar
  16. 16.
    Dyreson, C.E.: Temporal coalescing with now, granularity, and incomplete information. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 169–180 (2003).  https://doi.org/10.1145/872757.872779
  17. 17.
    Bebel, B., Krolikowski, Z., Wrembel, R.: Formal approach to modelling a multiversion data warehouse. Bull. Pol. Acad. Sci. Tech. Sci. 54(1), 51–62 (2006)zbMATHGoogle Scholar
  18. 18.
    Amagasa, T., Yoshikawa, M., Uemura, S.: A data model for temporal xml documents. In: Ibrahim, M., Küng, J., Revell, N. (eds.) Proceedings of the 11th International Conference, DEXA 2000, pp. 334–344. Springer, Berlin (2000).  https://doi.org/10.1007/3-540-44469-6_31 CrossRefGoogle Scholar
  19. 19.
    Amagasa, T., Yoshikawa, M., Uemura, S.: Realizing temporal xml repositories using temporal relational databases. In: Proceedings of the 3rd International Symposium on Cooperative Database Systems for Advanced Applications. IEEE (2001).  https://doi.org/10.1109/CODAS.2001.945150
  20. 20.
    Grandi, F., Mandreoli, F.: The valid web: an XML/XSL infrastructure for temporal management of web documents. In: Yakhno, T. (ed.) Lecture Notes in Computer Science 1909—Proceedings of the 1st International Conference on Advances in Information Systems, pp. 294–303. Springer, Berlin (2000)Google Scholar
  21. 21.
    Grandi, F., Mandreoli, F., Tiberio, P.: Temporal modelling and management of normative documents in XML format. Data Knowl. Eng. 54, 327–354 (2005)CrossRefGoogle Scholar
  22. 22.
    Grandi, F., Mandreoli, F., Tiberio, P., Bergonzini, M.: A temporal data model and management system for normative texts in xml format. In: Proceedings of the 5th ACM International Workshop on Web Information and Data Management, pp. 29–36. ACM, New York (2003)Google Scholar
  23. 23.
    Chien, S.Y., Tsotras, V.J., Zaniolo, C., Zhang, D.: Efficient complex query support for multiversion xml documents. In: Proceedings of the 8th International Conference on Extending Database Technology, pp. 161–178. Springer, Berlin (2002).  https://doi.org/10.1007/3-540-45876-X_12
  24. 24.
    Wang, F.: XML-based support for database histories and document versions. Ph.D. Thesis, University of California (2004)Google Scholar
  25. 25.
    Wang, F., Zaniolo, C.: Temporal queries in xml document archives and web warehouses. In: Proceedings of the 10th International Symposium on Temporal Representation and Reasoning and the Fourth International Conference on Temporal Logic. IEEE (2003).  https://doi.org/10.1109/TIME.2003.1214879
  26. 26.
    Ali, K.A., Pokorny, J.: A Comparison of XML-Based Temporal Models, pp. 339–350. Springer, Berlin (2009)Google Scholar
  27. 27.
    Gergatsoulis, M., Stavrakas, Y.: Representing Changes in XML Documents Using Dimensions, pp. 208–222. IEEE, New York (2003).  https://doi.org/10.1007/978-3-540-39429-7_14 CrossRefGoogle Scholar
  28. 28.
    Bellini, P., Bruno, I., Nesi, P., Rauch, N.: Graph databases methodology and tool supporting index/store versioning. J. Vis. Lang. Comput. 31, 222–229 (2015).  https://doi.org/10.1016/j.jvlc.2015.10.018 CrossRefGoogle Scholar
  29. 29.
    Gutierrez, C., Hurtado, C., Vaisman, A.: Introducing time into RDF. IEEE Trans. Knowl. Data Eng. 19(2), 207–218 (2007)CrossRefGoogle Scholar
  30. 30.
    Graube, M., Hensel, S., Urbas, L.: R43ples: revisions for triples—an approach for version control in the semantic web. In: LDQ@SEMANTICS, pp. 1–8. CEUR-WS.org (2014)Google Scholar
  31. 31.
    Codd, E.F.: A relational model of data for large shared data banks. Commun. ACM 13(6), 377–387 (1970)CrossRefGoogle Scholar
  32. 32.
    Wijsen, J.: Temporal FDs on complex objects. ACM Trans. Database Syst. 24(1), 127–176 (1999)CrossRefGoogle Scholar
  33. 33.
    Wijsen, J.: Temporal Dependencies, pp. 2960–2966. Springer, Berlin (2009)Google Scholar
  34. 34.
    Casanova, M., Fagin, R., Papadimitriou, C.: Inclusion dependencies and their interaction with functional dependencies. J. Comput. Syst. Sci. 28, 29–59 (1984)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Rescher, N., Manor, R.: On inference from inconsistent premises. Theory Decis. 1, 179–217 (1970)CrossRefGoogle Scholar
  36. 36.
    Destercke, S., Dubois, D., Chojnacki, E.: Possibilistic information fusion using maximal coherent subsets. IEEE Trans. Fuzzy Syst. 17(1), 79–92 (2009).  https://doi.org/10.1109/TFUZZ.2008.2005731 CrossRefGoogle Scholar
  37. 37.
    Robson, J.: Algorithms for maximum independent sets. J. Algorithms 7(3), 425–440 (1986)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Sakai, S., Togasaki, M., Yamazaki, K.: A note on greedy algorithms for the maximum weighted independent set problem. Discrete Appl. Math. 126(2), 313–322 (2003)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Chen, P.P.S.: The entity-relationship model-toward a unified view of data. ACM Trans. Database Syst. 1(1), 9–36 (1976).  https://doi.org/10.1145/320434.320440 CrossRefGoogle Scholar
  40. 40.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press, New York (1996)Google Scholar
  41. 41.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Telecommunications and Information ProcessingGhent UniversityGhentBelgium

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