Abstract
Many commercial relational Data Base Management Systems (DBMSs) maintain histograms to approximate the distribution of values in the relation attributes and based on them estimate query result sizes. A histogram approximates the distribution by grouping data into buckets. The estimation-errors resulting from the loss of information during the grouping process affect the accuracy of the decision, made by query optimizers, about choosing the most economical evaluation plan for a query. In front of this challenging problem, many histogram-based estimation techniques including the equi-depth, the v-optimal, the max-diff and the compressed histograms have well contributed to approximate the cost of a query evaluation plan. But, most of the times the obtained estimates have much error. Motivated by the fact that inaccurate estimations can lead to wrong decisions, we propose in this paper an efficient algorithm, called Compressed-V2, for accurate histogram constructions. Both theoretical and effective experiments are done using benchmark data set showing the promising results obtained using the proposed algorithm. We think that this algorithm will significantly contribute for helping to solve the problem of Multi-Query Optimization (MQO) resulting from queries interactions especially in Relational Data Warehouses (RDW) which represent the ideal environment in which complex OLAP queries interact with each other.
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References
Ioannidis, Y., Poosala, V.: Balancing histogram optimality and practicality for query result size estimation. In: Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, pp. 233–244 (1995)
Jagadish, H.V., Koudas, N., Muthukrishnan, S., Poosala, V., Sevcik, K., Suel, T.: Optimal histograms with quality guarantees. In: Proceedings of the 24th International Conference on Very Large Data Bases (VLDB), New York, USA, pp. 275–286 (1998)
Poosala, V., Ioannidis, Y.E., Haas, P.J., Shekita, E.J.: Improved histograms for selectivity estimation of range predicates. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 294–305 (1996)
Jagadish, H.V., Jin, H., Ooi, B.C., Tan, K.-L.: Global optimization of histograms. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pp. 223–234 (2001)
Yu, C., Philip, G., Meng, W.: Distributed top-N query processing with possibly uncooperative local systems. In: Proc. 29th VLDB Conf., Berlin, Germany, pp. 117–128 (2003)
Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access path selection in a relational database management system. In: Proceedings of the ACM SIGMOD International Symposium on Management of Data, Boston, Mass., pp. 23–34 (June 1979)
John Oommen, B., Rueda, L.G.: An empirical comparison of histogram-like techniques for query optimization. In: Proceedings of the 2nd International Conference on Entreprise Information Systems, Stafford, UK, July 4-7, pp. 71–78 (2000)
Ioannidis, Y., Christodoulakis, S.: On the propagation of errors in the size of join results. In: Proceedings of the 1991 ACM SIGMOD Conference, Denver, CO, pp. 268–277 (May 1991)
Ioannidis, Y., Christodoulakis, S.: Optimal histograms for limiting worst-case error propagation in the estimates of query optimizers. To appear in ACM-TODS (1992)
Kooi, R.P.: The optimization of queries in relational databases. PhD thesis, Case Western Reserver University (September 1980)
Shapiro, G.P., Connell, C.: Accurate Estimation of the Number of Tuples Satisfying a Condition. In: Proceedings of ACM-SIGMOD Conference, pp. 256–276 (1984)
Ioannidis, Y.: Universality of serial histograms. In: Proceedings of the 19th Int. Conf. on Very Large Databases, pp. 256–267 (December 1993)
Poosala, V., Ioannidis, Y.: Estimation of query-result distribution and its application in parallel-join load balancing. In: Proceedings of the 22nd Int. Conf. on Very Large Databases, pp. 448–459 (1996)
Gupta, A., Sudarshan, S., Viswanathan, S.: Query scheduling in multi query optimization. In: IDEAS, pp. 11–19 (2001)
Thomas, D., Diwan, A.A., Sudarshan, S.: Scheduling and caching in multi query optimization. In: COMAD, pp. 150–153 (2006)
Kerkad, A., Bellatreche, L., Geniet, D.: Queen-Bee: Query interaction- aware for buffer allocation and scheduling problem. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 156–167. Springer, Heidelberg (2012)
Ioannidis, Y.: Query optimization. In: ACM Computing Surveys, Symposium Issue on the 50th Anniversary of ACM, vol. 28, pp. 121–123 (1996)
Christodoulakis, S.: Implications of certain assumptions in database performance evaluation. ACM TODS 9(2), 163–186 (1984)
Zipf, G.K.: Human Behavior and the Principle of Least Effort: an Introduction to Human Ecology. Addison-Wesley, Cambridge (1949)
Liu, Y.: Data preprocessing. Department of Biomedical, Industrial and Human Factors Engineering Wright State University (2010)
Ioannidis, Y., Poosala, V.: Histogram-based solutions to diverse database estimation problems. IEEE Data Engineering Bulletin 18(3), 10–18 (1995)
Muralikrishna, M., Dewitt, D.J.: Equi-depth histograms for estimating selectivity factors for multi-dimensional queries. In: Proceedings of ACM SIGMOD Conference, pp. 28–36 (1988)
Mousavi, H., Zaniolo, C.: Fast and Accurate Computation of Equi-Depth Histograms over Data Streams. In: Proceedings of EDBT, Uppsala, Sweden, March 22-24 (2011)
Gomes, J.S.: Adaptive Histogram Algorithms for Approximating Frequency Queries in Dynamic Data Streams. In: 12th International Conference on Internet Computing, ICOMP 2011, Las Vegas, NV, July 18-21 (2011)
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Labbadi, W., Akaichi, J. (2013). Improving Range Query Result Size Estimation Based on a New Optimal Histogram. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2013. Lecture Notes in Computer Science(), vol 8132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40769-7_4
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DOI: https://doi.org/10.1007/978-3-642-40769-7_4
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