Skip to main content
Log in

Temporal aggregation on user-defined granularities

  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

Time-varying data play a major role in many applications, and, starting from the 80’s, they have been widely studied in temporal databases. In the last two decades, several researchers have shown that, to deal with many application domains, user-defined temporal granularities must be coped with. When data are stored at multiple user-defined temporal granularities, the task of defining proper conversion functions to aggregate data from an origin granularity (e.g., business days) to a task granularity (e.g., months) is of primary importance. However, current temporal database approaches mostly demand such a task to system administrators, or to specific applications, providing no methodology or general guideline to accomplish it. In this paper, we propose a general and application-independent methodology which, on the basis of the temporal relationship between two user-defined granularities, provides users with a set of conversion/aggregation functions between them, consistent with the telic vs. atelic character of the data to be aggregated. The correctness of the approach is also proved.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Aristotle. The categories, on interpretation. Prior analytics. Harvard University Press.

  • Bennet, M., & Partee, B. (1978). Tense and discourse location in situation semantics. Bloomington: Indiana University Linguistics Club.

    Google Scholar 

  • Bettini, C. (2009). Temporal granularities. In L. Liu, & M. T. Özsu (Eds.), Encyclopedia of database systems. Springer.

  • Bettini, C., Dyreson, C. E., Evans, W. S., Snodgrass, R. T., & Wang, X. (1997). A glossary of time granularity concepts (pp. 406–413). Dagstuhl: Temporal Databases.

    Google Scholar 

  • Bettini, C., Jajodia, S., & Wang, X. S. (2000). Time granularities in databases, data mining, and temporal reasoning. Springer.

  • Chamoni, P., & Stock, S. (1999). Temporal structures in data warehousing. In Proc. DaWaK’99. LNCS (Vol. 1676, pp. 353–358).

  • Chandra, R., Segev, A., & Stonebraker, M. (1994). Implementing calendars and temporal rules in next generation databases. In Proc. internat. conf. on data engineering (pp. 246–273).

  • Clifford, J., & Rao, A. (1987). A simple, general structure for temporal domains. In Proc. conf. on temporal aspects in information systems (pp. 23–30).

  • Combi, C., Franceschet, M., & Peron, A. (2004). Representing and reasoning about temporal granularities. Journal of Logic and Computation, 14(1), 1–77.

    Article  MathSciNet  Google Scholar 

  • Dal Lago, U., Montanari, A., & Puppis, G. (2007). Compact and tractable automaton-based representations of time granularities. Theoretical Computer Science, 373(1–2), 115–141.

    Article  MathSciNet  Google Scholar 

  • Dowty, D. (1986). The effects of the aspectual class on the temporal structure of discourse, tense and aspect in discourse. Linguistics and Philosophy, 9(1), 37–61.

    Google Scholar 

  • Dyreson, C. (1994). Valid-time indeterminacy. PhD Thesis. USA: Univ. of Arizona.

  • Egidi, L., & Terenziani, P. (2005). A flexible approach to user-defined symbolic granularities in temporal databases. In Proc. ACM symposium on applied computing (pp. 592–597).

  • Egidi, L., & Terenziani, P. (2006). A mathematical framework for the semantics of symbolic languages representing periodic time. Annals of Mathematics and Artificial Intelligence, 46, 317–347.

    MathSciNet  Google Scholar 

  • Egidi, L., & Terenziani, P. (2008). A modular approach to user-defined symbolic periodicities. Data & Knowledge Engineering, 66(1), 163–198.

    Article  Google Scholar 

  • Gamper, J., Bohlen, M., & Jensen, C. (2009). Temporal aggregation. In L. Liu & M. T. Özsu (Eds.), Encyclopedia of database systems (pp. 2924–2929). Springer.

  • Jensen, C. S., & Dyreson, C. E. (1998). A consensus glossary of temporal database concepts—February, 1998 version (pp. 367–405). Springer: Temporal Databases: Research and Practice.

  • Kathri, V., Snodgrass, R. T., & Terenziani, P. (2009). Telic distinction in temporal databases. In L. Liu & M. T. Özsu (Eds.), Encyclopedia of database systems (pp. 2911–2914). Springer.

  • Kimball, R., & Ross, M. (2002). The data warehouse toolkit. The complete guide to dimensional modeling (2nd ed.). Wiley.

  • Kline, M., & Snodgrass, R. T. (1995). Computing temporal aggregates. In Proc. internat. conf. on data engineering (pp. 222–231).

  • Leban, B., McDonald, D., & Forster, D. (1986). A representation for collections of temporal intervals. In Proc. of the 5th national conference on artificial intelligence (pp. 367–371).

  • Lenz, H.J., & Shoshani, A. (1997). Summarizability in OLAP and statistical data bases. In Proc. 9th internat. conf. on scientific and statistical database management (pp. 132–143).

  • Liu L., & Özsu, M. T. (2009). Encyclopedia of database systems. Springer.

  • Malinowski, E., & Zimanyi, E. (2008). A conceptual model for temporal data warehouses and its transformation to the ER and object-relational models. Data & Knowledge Engineering, 64, 101–133.

    Article  Google Scholar 

  • Malinowski, E., & Zimanyi, E. (2009). Advanced data warehouse design. From conventional to spatial and temporal applications. Springer.

  • Melton, J. (2003). Advanced SQL:1999. Understanding object-relational and other advanced features. The Morgan Kauffman series in data management systems. Morgan Kauffman Publishers.

  • Moon, B., Vega Lopez, I. F., & Immanuel, V. (2003). Efficient algorithms for large-scale temporal aggregation. IEEE TKDE, 15(3), 744–759.

    Google Scholar 

  • Navathe, S. B., & Ahmed, R. (1989). A temporal relational model and a query language. Information Sciences, 49(1–3), 147–175.

    Article  Google Scholar 

  • Ning, P., Wang, X.S., & Jajodia, S. (2002). An algebraic representation of calendars. Annals of Mathematics and Artificial Intelligence, 36(1–2), 5–38.

    Article  MathSciNet  Google Scholar 

  • Shoham, Y. (1987). Temporal logics in Al: Semantic and ontological considerations. Artificial Intelligence, 33(1), 89–104.

    Article  MathSciNet  Google Scholar 

  • Snodgrass, R. T. (1995). The TSQL2 temporal query language. Kluwer.

  • Snodgrass, R. T. (2000). Developing time-oriented database applications in SQL. Morgan Kaufmann.

  • Snodgrass, R. T., Gomez, S., & McKenzie, L. E. (1993). Aggregates in the temporal query language TQuel. IEEE TKDE, 5(5), 826–842.

    Google Scholar 

  • Soo, M., & Snodgrass, R. (1993). Multiple calendar support for conventional database management systems. In Proc. internat. worksh. on infrastructure for temporal databases.

  • Terenziani, P. (2009). Temporal periodicity. In L. Liu & M. T. Özsu (Eds.), Encyclopedia of database systems (pp. 3004–3008). Springer.

  • Terenziani, P., & Snodgrass, R. T. (2004). Reconciling point-based and Interval-based semantics in temporal relational databases: A proper treatment of the telic/atelic distinction. IEEE TKDE, 16(4), 540–551.

    Google Scholar 

  • Terenziani, P., Snodgrass, R. T., Bottrighi, A., Molino, G., & Torchio, M. (2007). Extending temporal databases to deal with Telic/Atelic medical data. Artificial Intelligence in Medicine, 39(2), 113–126.

    Article  Google Scholar 

  • Tuma, P. A. (1992). Implementing hystorical aggregates in TempIS. Ph.D. thesis. USA: Wayne State University.

  • Tuzhilin, A., & Clifford, J. (1995). On periodicity in temporal databases. Information Systems, 20(8), 619–639.

    Article  Google Scholar 

  • Vega Lopez, I. F., Snodgrass, R. T., & Moon, B. (2005). Spatiotemporal aggregate computation: A survey. IEEE TKDE, 17(2), 271–286.

    Google Scholar 

  • Vendler, Z. (1967). Verbs and times. Linguistics in Philosophy (pp. 97–121). Cornell University Press.

  • Wang, X., Bettini, C., Brodsky, A., & Jajodia, S. (1997). Logical design for temporal databases with multiple granularities. ACM Transactions on Database Systems, 22(2), 115–170.

    Article  Google Scholar 

  • Webber, B. (1988). Tense and aspect. Computational Linguistics, 2(14) (special issue)

  • Wu, Y., Jagodia, S., & Sean Wang, X. (1999). Temporal database bibliography update. Temporal databases—research and practice. LNCS (Vol.1399, pp. 338–367). Springer.

  • Yang, J., & Widom, J. (2003). Incremental computation and maintenance of temporal aggregates. VLDB Journal, 12(3), 262–283.

    Article  Google Scholar 

  • Zhang, D., Gunopulos, D., Tsotras, V. J., & Seeger, B. (2003). Temporal and spatio-temporal aggregations over data streams using multiple time granularities. Information Systems, 28, 61–84.

    Article  Google Scholar 

  • Zhang, D., Markowetz, A., Tsotras, V. J., Gunopulos, D., & Seeger, B. (2001). Efficient computation of temporal aggregates with range predicates. In Proc. of PODS.

Download references

Acknowledgements

The author is very grateful to the Reviewers for their in-depth, constructive and inspiring criticism. The author is also very grateful to Rick Snodgrass, for many illuminating suggestions and comments regarding a preliminary version of the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paolo Terenziani.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Terenziani, P. Temporal aggregation on user-defined granularities. J Intell Inf Syst 38, 785–813 (2012). https://doi.org/10.1007/s10844-011-0179-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10844-011-0179-y

Keywords

Navigation