Access Methods for Bi-Temporal Databases

  • Anil Kumar
  • Vassilis J. Tsotras
  • Christos Faloutsos
Part of the Workshops in Computing book series (WORKSHOPS COMP.)


While much work has recently appeared in literature on access methods for transaction-time databases, not much has been done for indexing bitemporal databases, i. e., databases that incorporate both transaction and valid time dimensions. In this paper we first discuss the issues involved in addressing general bitemporal queries and then propose two general approaches in solving such queries. For simplicity we present our findings in relation to the so-called bitemporal pure-timeslice query. However our methodology applies to more complex bitemporal queries. The first approach reduces bitemporal queries to partial persistence problems for which an efficient method is then designed. Using this approach we introduce a new access method for the bitemporal pure-timeslice query, the Bitemporal Interval Tree. The second approach “sees” bitemporal data objects as consisting of two intervals, a valid-time and a transaction- time interval. It then divides bitemporal data objects in two categories according to whether the right endpoint of the transaction time interval is known, and uses a different, R*-tree based organization, for each category. In this paper we present the advantages and disadvantages of these approaches. In addition we compare them through simulation with a straightforward approach that “sees” the intervals associated with a bitemporal object as one rectangle that is stored in a single R*-tree.


Query Time Access Method Valid Time Temporal Database Data Page 
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]
    R. Snodgrass, I. Ahn. Temporal Databases. IEEE Computer,Vol. l9,No. 9, pp 35–42, 1986.CrossRefGoogle Scholar
  2. [2]
    C.S. Jensen, editor et al. A Consensus Glossary of Temporal Database Concepts. ACM SIGMOD Record, Vol. 23, No. 1, pp. 52–64, 1994.CrossRefGoogle Scholar
  3. [3]
    V. Lum, P. Dadam, R. Erbe, J. Guenauer, P. Pistor, G. Walch, H. Werner, J.Woodfill. Designing DBMS Support for the Temporal Database. Proc. ACM SIGMOD, pp 115–130, 1984.Google Scholar
  4. [4]
    I. Ahn, R. Snodgrass. Performance Evaluation of a Temporal Database Management System. Proc. ACM SIGMOD, pp 96–107, 1986.Google Scholar
  5. [5]
    M. Stonebraker. The Design of the Postgres Storage System. Proc. 13th Conference on Very Large Databases, pp 289–300, 1987.Google Scholar
  6. [6]
    D.Lomet, B.Salzberg. Access Methods for Multiversion Data. Proc. ACM SIGMOD,pp 315–324, 1989.Google Scholar
  7. [7]
    D. Lomet, B. Salzberg. The Performance of a Multiversion Access Method. Proc. ACM SIGMOD, pp 353–363, 1990.Google Scholar
  8. [8]
    A. Segev, H. Gunadhi. Event-Join Optimization in Temporal Relational Databases. Proc. 15th Conference on Very Large Databases, pp 205–215, Aug. 1989.Google Scholar
  9. [9]
    Y. Manolopoulos, G. Kapetanakis. Overlapping B+ Trees for Temporal Data. Proc. of 5th JCIT Conf., Jerusalem, Israel Oct. 22–25, pp 491–498, 1990.Google Scholar
  10. [10]
    R. Elmasri, G. Wuu, Y. Kim. The Time Index: An Access Structure for Temporal Data. Proc. 16th Conference on Very Large Databases, Aug. 1990.Google Scholar
  11. [11]
    S. Lanka, E. Mays. Fully Persistent B+ Trees. Proc. ACM SIGMOD, pp 426–435, 1991.Google Scholar
  12. [12]
    C. Kolovson, M. Stonebraker. Segment Indexes: Dynamic Indexing Techniques for Multi-dimensional Interval Data. Proc. ACM SIGMOD, pp 138–147, 1991.Google Scholar
  13. [13]
    C.S. Jensen, L Mark, N. Roussopoulos. Incremental Implementation Model for Relational Databases with Transaction Time. IEEE Trans, on Knowledge and Data Engineering, Vol. 3, No 4, pp 461–473, 1991.CrossRefGoogle Scholar
  14. [14]
    C. Kolovson. Indexing Techniques for Historical Databases. In A.Tansel, J. Clifford, S.K. Gadia, S. Jajodia, A. Segev, and R. Snodgrass (eds.), Temporal Databases: Theory, Design, and Implementation, Benjamin/Cummings, pp 418–432, 1993.Google Scholar
  15. [15]
    T.Y.C. Leung, R.R. Müntz. Stream Processing: Temporal Query Processing and Optimization. In A.Tansel, J.Clifford, S.K.Gadia, S.Jajodia, A.Segev, and R.Snodgrass(eds.), Temporal Databases: Theory, Design and Implementation, Benjamin/Cummings, pp 329–355, 1993.Google Scholar
  16. [16]
    B. Becker, S. Gschwind, T. Ohler, B. Seeger, P. Widmayer. On Optimal Multiversion Access Structures. Proceedings of Symposium on Large Spatial Databases, 1993. Published in Lecture Notes in Computer Science, Vol 692, pp. 123–141, Springer-Verlag (1993).Google Scholar
  17. [17]
    V.J. Tsotras, N. Kangelaris. The Snapshot Index, an I/O-Optimal Access Method for Snapshot Queries. CATT-Tech. Report 93–68, Polytechnic University, Dec. 1993. Also appears at the Information Systems, An International Journal, Vol. 20, No. 3, pp 237–260, 1995.Google Scholar
  18. [18]
    H. Shen, B.C. Ooi, H. Lu. The TP-Index: A Dynamic and Efficient Indexing Mechanism for Temporal Databases. Proc. 10th IEEE Intern. Conf. on Data Engineering, 1994.Google Scholar
  19. [19]
    V.J. Tsötras, B. Gopinath, G.W.Hart. Efficient Management of Time-Evolving Databases. Accepted at the IEEE Trans, on Knowledge and Data Engineering, 1994.Google Scholar
  20. [20]
    G.M. Landau, J.P. Schmidt, V.J. Tsotras. On Historical Queries along Multiple Lines of Time Evolution. To appear at the VLDB Journal, 1995.Google Scholar
  21. [21]
    B. Salzberg, V.J. Tsotras. A Comparison of Access Methods for Time-Evolving Data. Submitted for publication; available as a technical report from Polytechnic University (CATT-TR-94-8 J), or, Northeastern University (NU-CCS-94-21), 1994.Google Scholar
  22. [22]
    Y.J. Chiang, R. Tamassia. Dynamic Algorithms in Computational Geometry. Proceedings of IEEE, Special Issue on Computational Geometry, Vol 80, No 9, pp 362–381, 1992.Google Scholar
  23. [23]
    E.M. McCreight. Priority Search Trees. SIAM Journal of Computing, Vol. 14, No 2, pp 257–276, 1985.CrossRefMATHMathSciNetGoogle Scholar
  24. [24]
    A. Guttman. R-Trees: A Dynamic Index Structure for Spatial Searching. Proc. ACM SIGMOD, 1984.Google Scholar
  25. [25]
    P.C. Kanellakis, S. Ramaswamy, D.E. Vengroff, J.S. Vitter. Indexing for Data Models with Constraints and Classes. Proc. ACM PODS, pp 233–243, 1993.Google Scholar
  26. [26]
    S. Ramaswamy, S. Subramanian. Path Caching: a Technique for Optimal External Searching. Proc. 13th ACM PODS, pp 25–35, 1994.Google Scholar
  27. [27]
    R. Agrawal, C. Faloutsos, A. Swami. Efficient Similarity Search in Sequence Databases. Proc. FODO Conference, 1993.Google Scholar
  28. [28]
    C. Faloutsos, M. Ranganathan, Y. Manolopoulos. Fast Subsequence Matching in Time-Series Databases. Proc. ACM SIGMOD, pp 419–429, 1994.Google Scholar
  29. [29]
    R.Snodgrass et. al. TSQL92 Language Specification.ACM SIGMOD Ree.Vol 23, Nol, pp 65–86, 1994.Google Scholar
  30. [30]
    N. Beckmann, H.P. Kriegel, R. Schneider, B. Seeger. The R*-tree: An efficient and Robust Access Method for Points and Rectangles. Proc. ACM SIGMOD, pp 322–331, 1990.Google Scholar
  31. [31]
    J.R. Driscoll, N. Sarnak, D. Sleator, R.E. Tarjan. Making Data Structures Persistent. J. of Comp, and Syst. Sci., Vol 38, pp 86–124, 1989.CrossRefMATHMathSciNetGoogle Scholar
  32. [32]
    H. Edelsbrunner. A new Approach to Rectangle Intersections, Part I&II. Int. Journal of Computer Mathematics, Vol. 13, pp 209–229, 1983.Google Scholar
  33. [33]
    J.L. Bentley. Algorithms for Klee’s Rectangle Problems. Computer Science Department, Carnegie- Mellon University, Pittsburgh, 1977.Google Scholar
  34. [34]
    H. Samet. The Design and Analysis of Spatial Data Structures. Addison-Wesley, 1989.Google Scholar
  35. [35]
    A.Kumar, V.J. Tsotras, C. Faloutsos. On designing Access Methods for Bitemporal Databases. Polytechnic University Tech. Report (CATT-TR-95-85), 1995.Google Scholar
  36. [36]
    B.Chazelle. Filtering Search: a new approach to query answering. Proc.24th IEEE FOCS, 1983.Google Scholar
  37. [37]
    R. Cole. Searching and Storing Similar lists. J. of Algorithms, Vol 7, pp 202–220. 1986.CrossRefMATHGoogle Scholar

Copyright information

© British Computer Society 1995

Authors and Affiliations

  • Anil Kumar
    • 1
  • Vassilis J. Tsotras
    • 2
  • Christos Faloutsos
    • 3
  1. 1.Dept. of Electr. Eng.Polytechnic UniversityBrooklynUSA
  2. 2.Dept. of Computer SciencePolytechnic UniversityBrooklynUSA
  3. 3.AT&T Bell LabsMurray HillUSA

Personalised recommendations