Abstract
Traditional data warehouses are built in an off-line periodic fashion which makes them less valuable in applications where the most up-to-date data is required. For these applications, data should be incorporated in the warehouse and made available as soon as possible in “Real Time Data Warehouse”. In this paper we propose an indexing model named TiC-Tree, in order to simultaneously index and store multidimensional detailed and aggregated data. Our contribution exploits the temporal nature of data and focuses on range and/or group-by queries. We evaluate our proposal with the synthetic data set Star Schema Benchmark and advocate it in comparison with other existing solution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Polyzotis, N., Skiadopoulos, S., Alkis Simitsis, P.V., Frantzell, N.E.: Supporting Streaming Updates in an Active Data Warehouse. In: Proc. of the 23rd Int. Conf. on Data Engineering (2007)
Tho, M.N., Tjoa, A.M.: Zero-latency data warehousing for hetrogeneous data sources and continuous data streams. In: Proc. of the Fifth Int. Conf. on Information Integration and Web-based Applications Services (2003)
O’Neil, P.E., O’Neil, E.J., Chen, X., Revilak, S.: The Star Schema Benchmark and Augmented Fact Table Indexing. In: Nambiar, R., Poess, M. (eds.) TPCTC 2009. LNCS, vol. 5895, pp. 237–252. Springer, Heidelberg (2009)
Gupta, H.: Selection of Views to Materialize in a Data Warehouse. In: Afrati, F.N., Kolaitis, P.G. (eds.) ICDT 1997. LNCS, vol. 1186. Springer, Heidelberg (1996)
Roussopoulos, N., Kotidis, Y., Roussopoulos, M.: Cubetree: organization of and bulk incremental updates on the data cube. In: Proc. of ACM SIGMOD Int. Conf. on Management of Data, pp. 89–99. ACM, New York (1997)
Sismanis, Y., Deligiannakis, A., Kotidis, Y., Roussopoulos, N.: Hierarchical dwarfs for the rollup cube. In: Proc. of the 6th ACM Int. Workshop on Datawarehousing and OLAP, NY, USA (2003)
Lakshmanan, L.V.S., Pei, J., Han, J.: Quotient cube: how to summarize the semantics of a data cube. In: Proc. of the 28th Int. Conf. on Very Large Data Bases, VLDB Endowment, pp. 778–789 (2002)
Tao, Y., Papadias, D.: Efficient Historical R-Trees. In: Proc. of the 13th Int. Conf. on Scientific and Statistical Database Management, Washington, DC, USA (2001)
Berchtold, S., Keim, D.A., Kriegel, H.P.: The X-tree: An Index Structure for High-Dimensional Data. In: Proc. of 22th Int. Conf. on Very Large Data Bases, Mumbai (Bombay), India, pp. 28–39 (1996)
Papadias, D., Tao, Y., Kalnis, P., Zhang, J.: Indexing spatio-temporal data warehouses. In: Proc. of the 18th Int. Conf. on Data Engineering (2002)
Ester, M., Kohlhammer, J., Kriegel, H.P.: The DC-tree: A Fully Dynamic Index Structure for Data Warehouses. In: Proc. of the 16th Int. Conf. on Data Engineering, pp. 379–388 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ahmed, U., Tchounikine, A., Miquel, M., Servigne, S. (2010). Real-Time Temporal Data Warehouse Cubing. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds) Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, vol 6262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15251-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-15251-1_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15250-4
Online ISBN: 978-3-642-15251-1
eBook Packages: Computer ScienceComputer Science (R0)