Abstract
In recent years, data quality issues have attracted wide attention. Data quality is mainly caused by dirty data. Currently, many methods for dirty data management have been proposed, and one of them is entity-based relational database in which one tuple represents an entity. The traditional query optimizations having the ability to estimate the cost of execution of a query plan have not been suitable for the new entity-based model. Then new query optimizations need to be developed. In this paper, we propose new query selectivity estimation based on histogram, and focus on solving the overestimation which traditional methods lead to. We prove our approaches are unbiased. The experimental results on both real and synthetic data sets show that our approaches can give good estimates with low error.
This paper was partially supported by NGFR 973 grant 2012CB316200 and NSFC grant 61003046, 6111113089. Doctoral Fund of Ministry of Education of China (No. 20102302120054).
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Zhang, Y., Yang, L., Wang, H. (2012). Range Query Estimation for Dirty Data Management System. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds) Web-Age Information Management. WAIM 2012. Lecture Notes in Computer Science, vol 7418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32281-5_15
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DOI: https://doi.org/10.1007/978-3-642-32281-5_15
Publisher Name: Springer, Berlin, Heidelberg
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