Abstract
On-line analytical processing against data warehouse databases is a common form of getting decision making information for almost every business field. Decision support information oftenly concerns periodic values based on regular attributes, such as sales amounts, percentages, most transactioned items, etc. This means that many similar OLAP instructions are periodically repeated, and simultaneously, between the several decision makers. Our Query Cache Tool takes advantage of previously executed queries, storing their results and the current state of the data which was accessed. Future queries only need to execute against the new data, inserted since the queries were last executed, and join these results with the previous ones. This makes query execution much faster, because we only need to process the most recent data. Our tool also minimizes the execution time and resource consumption for similar queries simultaneously executed by different users, putting the most recent ones on hold until the first finish and returns the results for all of them. The stored query results are held until they are considered outdated, then automatically erased. We present an experimental evaluation of our tool using a data warehouse based on a real-world business dataset and use a set of typical decision support queries to discuss the results, showing a very high gain in query execution time.
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References
Agosta, L.: Data Warehousing Lessons Learned: SMP or MPP for Data Warehousing. DM Review Magazine (2002)
Akinde, M.O., Bhlen, M.H., Johnson, T., Lakshmanan, L.V.S., Srivastava, D.: Efficient OLAP query processing in distributed data warehouses. Information Systems 28, 111–135 (2003)
Bernardino, J., Madeira, H.: Experimental Evaluation of a New Distributed Partitioning Technique for Data Warehouses. In: Int. Symposium on Database Engineering and Applications, IDEAS 2001 (2001)
Bernardino, J., Furtado, P., Madeira, H.: Approximate Query Answering Using Data Warehouse Striping. Journal of Intelligent Information Systems – Integrating Artificial Intelligence and Database Technologies 19(2), 145–167 (2002)
Critical Software SA, DWS, www.criticalsoftware.com
Cruanes, T., Dageville, B., Ghosh, B.: Parallel SQL Execution in Oracle 10g. In: ACM SIG International Conference on Management of Data, SIGMOD (2004)
DATAllegro, DATAllegro v3TM, www.datallegro.com
Galindo-Legaria, C.A., Grabs, T., Gukal, S., Herbert, S., Surna, A., Wang, S., Yu, W., Zabback, P., Zhang, S.: Optimizing Star Join Queries for Data Warehousing in Microsoft SQL Server. In: Int. Conf. on Data Engineering (ICDE 2008), pp. 1190–1199 (2008)
Han, W.S., Kwak, W., Lee, J., Lohman, G.M., Markl, V.: Parallelizing Query Optimization. In: International Conference on Very Large Data Bases, VLDB (2008)
Kimball, R., Ross, M.: The Data Warehouse Toolkit, 2nd edn. John Wiley & Sons, Chichester (2002)
Kossman, D.: The state of the art in distributed query processing. ACM Computing Surveys 32(4), 422–469 (2000)
Netezza, The Netezza Performance Server® DW Appliance, www.netezza.com
Oracle Data Warehousing Guide 10g R2, http://downloadwest.oracle.com/docs/cd/B1930601/server.102/b14223.pdf
Pedersen, T.B.: How is BI used in industry?: Report from a knowledge exchange network. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2004. LNCS, vol. 3181, pp. 179–188. Springer, Heidelberg (2004)
RedBrick White Paper, ftp://ftp.software.ibm.com/-software/data/informix/pubs/whitepapers/redbrickwpO40904.pdf
Schewe, K.D., Zhao, J.: Balancing redundancy and query costs in distributed data warehouses – an approach based on abstract state machines. In: Hartmann, S., Stumptner, M. (eds.) 2nd Asia-Pacific Conference on Conceptual Modelling (ER), Austral. CRPIT, vol. 43, pp. 97–105. Computer Society (2005)
Stanoi, I., Agrawal, D.P., El Abbadi, A.: Modeling and Maintaining Multi-view Data Warehouses. In: Akoka, J., Bouzeghoub, M., Comyn-Wattiau, I., Métais, E. (eds.) ER 1999. LNCS, vol. 1728, pp. 161–176. Springer, Heidelberg (1999)
Sun Microsystems, Data Warehousing Performance with SMP and MPP Architectures, White Paper (1998)
Zurek, T., Kreplin, K.: SAP Business Information Warehouse – From Data Warehousing to an E-Business Platform. In: International Conference on Data Engineering, ICDE (2001)
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Santos, R.J., Bernardino, J. (2009). A Query Cache Tool for Optimizing Repeatable and Parallel OLAP Queries. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2009. Lecture Notes in Computer Science, vol 5690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03573-9_11
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DOI: https://doi.org/10.1007/978-3-642-03573-9_11
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