Abstract
Plots are among the most important and widely used tools for scientific data analysis and visualization. With a plot (a.k.a. range group-by query) data are divided into a number of groups, and at each group, they are summarized over one or more attributes for a given arbitrary range. Wavelets, on the other hand, allow efficient computation of (individual) exact and approximate aggregations. With the current practice, to generate a plot over a wavelet-transformed dataset, one aggregate query is executed per each plot point; hence, for large plots (containing numerous points) a large number of aggregate queries are submitted to the database. On the contrary, we redefine a plot as a range group-by query and propose a wavelet-based technique that exploits I/O sharing across plot points to evaluate the plot efficiently and progressively. The intuition behind our approach comes from the fact that we can decompose a plot query into two sets of 1) aggregate queries, and 2) reconstruction queries. Subsequently, we exploit and extend our earlier related studies to effectively compute both quires in the wavelet domain. We also show that our technique is not only efficient as an exact algorithm but also very effective as an approximation method where either the query time or the storage space is limited.
This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC) and IIS-0238560 (PECASE), unrestricted cash gifts from Google and Microsoft, and partly funded by JPL SURP program and the Center of Excellence for Research and Academic Training on Interactive Smart Oilfield Technologies (CiSoft); CiSoft is a joint University of Southern California - Chevron initiative.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chakrabarti, K., Garofalakis, M.N., Rastogi, R., Shim, K.: Approximate query processing using wavelets. In: Proc. of VLDB, pp. 111–122 (2000)
Chen, Z., Narasayya, V.: Efficient computation of multiple group by queries. In: SIGMOD 2005, pp. 263–274. ACM Press, New York (2005)
Garofalakis, M., Gibbons, P.B.: Wavelet synopses with error guarantees. In: Proc. of ACM SIGMOD (2002)
Geffner, S., Agrawal, D., Abbadi, A.E., Smith, T.: Relative prefix sums: An efficient approach for querying dynamic OLAP data cubes. In: Proc. of ICDE (1999)
Gibbons, P.B., Matias, Y.: New sampling-based summary statistics for improving approximate query answers. In: Proc. of SIGMOD, pp. 331–342 (1998)
Gilbert, A.C., Kotidis, Y., Muthukrishnan, S., Strauss, M.J.: Optimal and approximate computation of summary statistics for range aggregates. In: Proc. of PODS (2001)
Gunopulos, D., Kollios, G., Tsotras, V.J., Domeniconi, C.: Approximating multidimensional aggregate range queries over real attributes. In: Proc. of SIGMOD (2000)
Hellerstein, J.M., Haas, P.J., Wang, H.: Online aggregation. In: Proc. of SIGMOD, pp. 171–182. ACM Press, New York (1997)
Ho, C., Agrawal, R., Megiddo, N., Srikant, R.: Range queries in OLAP data cubes. In: Proc. of SIGMOD
Jahangiri, M., Sacharidis, D., Shahabi, C.: Shift-Split: I/O Efficient Maintenance of Wavelet-Transformed Multidimensional Data. In: Proc. of SIGMOD (2005)
Jahangiri, M., Shahabi, C.: ProDA: A Suite of WebServices for Progressive Data Analysis. In: Proc. of ACM SIGMOD (demonstration) (2005)
Jahangiri, M., Shahabi, C.: Wolap: Wavelet-based range aggregate query processing. In: Department of Computer Science Technical Reports. USC (2007)
Lazaridis, I., Mehrotra, S.: Progressive approximate aggregate queries with a multi-resolution tree structure. In: Proc. of SIGMOD, pp. 401–412 (2001)
Nievergelt, Y.: Wavelets Made Easy. Springer, Heidelberg (1999)
Poosala, V., Ganti, V.: Fast approximate answers to aggregate queries on a data cube. In: Proc. of SSDBM, pp. 24–33. IEEE Computer Society, Los Alamitos (1999)
Riedewald, M., Agrawal, D., Abbadi, A.E.: pCube: Update-efficient online aggregation with progressive feedback. In: Proc. of SSDBM, pp. 95–108 (2000)
Schmidt, R., Shahabi, C.: How to evaluate multiple range-sum queries progressively. In: Proc. of ACM PODS, pp. 3–5 (2002)
Schmidt, R., Shahabi, C.: Propolyne: A fast wavelet-based technique for progressive evaluation of polynomial range-sum queries. In: Jensen, C.S., Jeffery, K.G., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287. Springer, Heidelberg (2002)
Shahabi, C., Jahangiri, M., Sacharidis, D.: Hybrid Query and Data Ordering for Fast and Progressive Range-Aggregate Query Answering. International Journal of Data Warehousing and Mining 1(2), 49–69 (2005)
Shahabi, C., Schmidt, R.: Wavelet disk placement for efficient querying of large multidimensional data sets. Technical Reports, USC (2004)
Vitter, J.S., Wang, M.: Approximate computation of multidimensional aggregates of sparse data using wavelets. In: Proc. of SIGMOD, pp. 193–204 (1999)
Wu, Y.-L., Agrawal, D., Abbadi, A.E.: Using wavelet decomposition to support progressive and approximate range-sum queries over data cubes. In: Proc. of CIKM, pp. 414–421 (2000)
Zhao, Y., Deshpande, P.M., Naughton, J.F., Shukla, A.: Simultaneous optimization and evaluation of multiple dimensional queries. In: Proc. of SIGMOD, pp. 271–282. ACM Press, New York (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jahangiri, M., Shahabi, C. (2008). Plot Query Processing with Wavelets. In: Ludäscher, B., Mamoulis, N. (eds) Scientific and Statistical Database Management. SSDBM 2008. Lecture Notes in Computer Science, vol 5069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69497-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-69497-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69476-2
Online ISBN: 978-3-540-69497-7
eBook Packages: Computer ScienceComputer Science (R0)