Abstract
The datacube is a conceptual data structure to support OnLine Analytical Processing (OLAP). It is essentially a series of tables organized according to attributes (called dimensions). Table rows (or cells) contain aggregated information for collections of records that satisfy value constraints for each dimension. The Statistics Tree (ST) uses a tree structure for storing the datacube in memory in order to optimize cell lookup time and handle a variety of types of cell-based queries. An Augmented ST (AST) is proposed with additional list structures within the ST. The additional lists link together the cells that comprise the tables of the datacube. An algorithm that builds table lists requires only a single traversal of the ST. Thus the AST supports both cell-level and table-level queries. Algorithms to build and update datacubes stored as ASTs are shown. A web-based wireless sensor network OLAP server based on the AST is described.
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.
References
Codd, E.F., Codd, S.B., Salley, C.T.: Providing OLAP (Online Analytical Processing) to User-Analysts: An IT Mandate, http://www.minet.uni-jena.de/dbis/lehre/ss2005/sem_dwh/lit/Cod93.pdf
Moon, S.W., Kim, J.S., Kwon, K.N.: Effectiveness of OLAP-based Cost Data Management in Construction Cost Estimate. Automation in Construction 16(3), 336–344 (2007)
Shen, L., Liu, S., Chen, S., Wang, X.: The Application Research of OLAP in Police Intelligence Decision System. Procedia Engineering 29, 397–402 (2012)
Hsiao, T., Petchulat, S.: Data Visualization on Web-based OLAP. In: ACM 14th International Workshop on Datawarehousing and OLAP, pp. 75–82. ACM, New York (2011)
Ordonez, C., Chen, Z., Garcia-Garcia, J.: Data Visualization on Web-based OLAP. In: ACM 14th International Workshop on Datawarehousing and OLAP, pp. 83–87. ACM, New York (2011)
Han, J., Kamber, M., Pei, J.: Data Mining. Morgan Kaufmann, San Francisco (2012)
Fu, L., Hammer, J.: CubiST: A New Algorithm for Improving the Performance of Ad-hoc OLAP Queries. In: DOLAP 2000 Proceedings of the 3rd ACM International Workshop on Data Warehousing and OLAP, pp. 72–79. ACM, New York (2000)
Hammer, J., Fu, L.: Improving the Performance of OLAP Queries Using Families of Statistics Trees. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, pp. 274–283. Springer, Heidelberg (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dunstan, N. (2013). An OLAP Server for Sensor Networks Using Augmented Statistics Trees. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-40319-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40318-7
Online ISBN: 978-3-642-40319-4
eBook Packages: Computer ScienceComputer Science (R0)