Abstract
Business Intelligence solutions, encompassing technologies such as multi-dimensional data modeling and aggregate query processing, are being applied increasingly to non-traditional data. This paper extends multi-dimensional aggregation to apply to data with associated interval values that capture when the data hold. In temporal databases, intervals typically capture the states of reality that the data apply to, or capture when the data are, or were, part of the current database state.
This paper proposes a new aggregation operator that addresses several challenges posed by interval data. First, the intervals to be associated with the result tuples may not be known in advance, but depend on the actual data. Such unknown intervals are accommodated by allowing result groups that are specified only partially. Second, the operator contends with the case where an interval associated with data expresses that the data holds for each point in the interval, as well as the case where the data holds only for the entire interval, but must be adjusted to apply to sub-intervals. The paper reports on an implementation of the new operator and on an empirical study that indicates that the operator scales to large data sets and is competitive with respect to other temporal aggregation algorithms.
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References
Akinde, M.O., Böhlen, M.H.: The efficient computation of subqueries in complex OLAP queries. In: Proc. of the 19th Intl. Conf. on Data Engineering, Bangalore, India, pp. 163–174 (2003)
Akinde, M.O., Böhlen, M.H., Johnson, T., Lakshmanan, L.V.S., Srivastava, D.: Efficient OLAP query processing in distributed data warehouses. In: Proc. of the 8th Intl. Conf. on Extending Database Technology, Prague, Czech Republic, pp. 336–353 (2002)
Chatziantoniou, D., Akinde, M.O., Johnson, T., Kim, S.: MD-join: An operator for complex OLAP. In: Proc. of the 17th Intl. Conf. on Data Engineering, Heidelberg, Germany, pp. 524–533 (2001)
Kline, N., Snodgrass, R.T.: Computing temporal aggregates. In: Proc. of the 11th Intl. Conf. on Data Engineering, Taipei, Taiwan, pp. 222–231 (1995)
Moon, B., Vega Lopez, I.F., Immanuel, V.: Efficient algorithms for large-scale temporal aggregation. IEEE Trans. on Knowledge and Data Engineering 15(3), 744–759 (2003)
Yang, J., Widom, J.: Incremental computation and maintenance of temporal aggregates. The VLDB Journal 12, 262–283 (2003)
Zhang, D., Markowetz, A., Tsotras, V., Gunopulos, D., Seeger, B.: Efficient computation of temporal aggregates with range predicates. In: Proc. of the 20th ACM SIGACT-SIGMODSIGART Symposium on Principles of Database Systems, Santa Barbara, CA, pp. 237–245 (2001)
Tuma, P.A.: Implementing Historical Aggregates in TempIS. PhD thesis, Wayne State University, Detroit, Michigan (1992)
Tao, Y., Papadias, D., Faloutsos, C.: Approximate temporal aggregation. In: Proc. of the 20th Intl. Conf. on Data Engineering, Boston, USA, pp. 190–201 (2004)
Vega Lopez, I.F., Snodgrass, R.T., Moon, B.: Spatiotemporal aggregate computation: A survey. IEEE Trans. on Knowledge and Data Engineering 17(2), 271–286 (2005)
Enderle, J., Hampel, M., Seidl, T.: Joining interval data in relational databases. In: Proc. Of the ACM SIGMOD Intl. Conf. on Knowledge and Data Engineering, Paris, France, pp. 683–694 (2004)
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Böhlen, M., Gamper, J., Jensen, C.S. (2006). Multi-dimensional Aggregation for Temporal Data. In: Ioannidis, Y., et al. Advances in Database Technology - EDBT 2006. EDBT 2006. Lecture Notes in Computer Science, vol 3896. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11687238_18
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DOI: https://doi.org/10.1007/11687238_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32960-2
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