East European Conference on Advances in Databases and Information Systems

ADBIS 2015: Advances in Databases and Information Systems pp 320-333 | Cite as

Efficient Computation of Parsimonious Temporal Aggregation

  • Giovanni Mahlknecht
  • Anton Dignös
  • Johann Gamper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9282)

Abstract

Parsimonious temporal aggregation (PTA) has been introduced to overcome limitations of previous temporal aggregation operators, namely to provide a concise yet data sensitive summary of temporal data. The basic idea of PTA is to first compute instant temporal aggregation (ITA) as an intermediate result and then to merge similar adjacent tuples in order to reduce the final result size. The best known algorithm to compute a correct PTA result is based on dynamic programming (DP) and requires \(\mathcal {O}(n^2)\) space to store a so-called split point matrix, where n is the size of the intermediate data. The matrix stores the split points between which the intermediate tuples are merged.

In this paper, we propose two optimizations of the DP algorithm for PTA queries. The first optimization is termed diagonal pruning and identifies regions of the matrix that need not to be computed. This reduces the runtime complexity. The second optimization addresses the space complexity. We observed that only a subset of the elements in the split point matrix are actually needed. Therefore, we propose to replace the split point matrix by a so-called split point graph, which stores only those split points that are needed to restore the optimal PTA solution. This step reduces the memory consumption. An empirical evaluation shows the effectiveness of the two optimizations both in terms of runtime and memory consumption.

References

  1. 1.
    Böhlen, M.H., Gamper, J., Jensen, C.S.: Multi-dimensional aggregation for temporal data. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 257–275. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  2. 2.
    Dignös, A., Böhlen, M.H., Gamper, J.: Temporal alignment. In: SIGMOD, pp. 433–444 (2012)Google Scholar
  3. 3.
    Gamper, J., Böhlen, M.H., Jensen, C.S.: Temporal aggregation. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 2924–2929. Springer, Heidelberg (2009)Google Scholar
  4. 4.
    Gordevicius, J., Gamper, J., Böhlen, M.H.: Parsimonious temporal aggregation. In: EDBT, pp. 1006–1017 (2009)Google Scholar
  5. 5.
    Gordevicius, J., Gamper, J., Böhlen, M.H.: Parsimonious temporal aggregation. VLDB J. 21(3), 309–332 (2012)CrossRefGoogle Scholar
  6. 6.
    Hirschberg, D.S.: Algorithms for the longest common subsequence problem. J. ACM 24(4), 664–675 (1977)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Kline, N., Snodgrass, R.T.: Computing temporal aggregates. In: ICDE, pp. 222–231 (1995)Google Scholar
  8. 8.
    Lorentzos, N.A.: Period-stamped temporal models. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 2094–2098. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Moon, B., Lopez, I.F.V., Immanuel, V.: Efficient algorithms for large-scale temporal aggregation. IEEE Trans. Knowl. Data Eng. 15(3), 744–759 (2003)CrossRefGoogle Scholar
  10. 10.
    Myers, E.W., Miller, W.: Optimal alignments in linear space. Comput. Appl. Biosci. 4(1), 11–17 (1988)Google Scholar
  11. 11.
    Navathe, S.B., Ahmed, R.: A temporal relational model and a query language. Inf. Sci. 49(1–3), 147–175 (1989)CrossRefMATHGoogle Scholar
  12. 12.
    Snodgrass, R.T. (ed.): The TSQL2 Temporal Query Language. Kluwer, Norwell (1995)MATHGoogle Scholar
  13. 13.
    Snodgrass, R.T.: Developing Time-Oriented Database Applications in SQL. Morgan Kaufmann, San Francisco (1999)Google Scholar
  14. 14.
    Snodgrass, R.T., Gomez, S., McKenzie, L.E.: Aggregates in the temporal query language TQuel. IEEE Trans. Knowl. Data Eng. 5(5), 826–842 (1993)CrossRefGoogle Scholar
  15. 15.
    Tao, Y., Papadias, D., Faloutsos, C.: Approximate temporal aggregation. In: ICDE, pp. 190–201 (2004)Google Scholar
  16. 16.
    Tuma, P.: Implementing Historical Aggregates in TempIS. Ph.D. thesis, Wayne State University, Detroit, Michigan (1992)Google Scholar
  17. 17.
    Lopez, V.I.F., Snodgrass, R.T., Moon, B.: Spatiotemporal aggregate computation: A survey. IEEE Trans. Knowl. Data. Eng. 17(2), 271–286 (2005)CrossRefGoogle Scholar
  18. 18.
    Wang, F.: Employee temporal data set (2009). http://timecenter.cs.aau.dk/
  19. 19.
    Yang, J., Widom, J.: Incremental computation and maintenance of temporal aggregates. VLDB J. 12(3), 262–283 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Giovanni Mahlknecht
    • 1
  • Anton Dignös
    • 1
  • Johann Gamper
    • 1
  1. 1.Free University of Bozen-BolzanoBolzanoItaly

Personalised recommendations