Abstract
Random sampling is a popular technique for providing fast approximate query answers, especially in data warehouse environments. Compared to other types of synopses, random sampling bears the advantage of retaining the dataset’s dimensionality; it also associates probabilistic error bounds with the query results. Most of the available sampling techniques focus on table-level sampling, that is, they produce a sample of only a single database table. Queries that contain joins over multiple tables cannot be answered with such samples because join results on random samples are often small and skewed. On the contrary, schema-level sampling techniques by design support queries containing joins. In this paper, we introduce Linked Bernoulli Synopses, a schema-level sampling scheme based upon the well-known Join Synopses. Both schemes rely on the idea of maintaining foreign-key integrity in the synopses; they are therefore suited to process queries containing arbitrary foreign-key joins. In contrast to Join Synopses, however, Linked Bernoulli Synopses correlate the sampling processes of the different tables in the database so as to minimize the space overhead, without destroying the uniformity of the individual samples. We also discuss how to compute Linked Bernoulli Synopses which maximize the effective sampling fraction for a given memory budget. The computation of the optimum solution is often computationally prohibitive so that approximate solutions are needed. We propose a simple heuristic approach which is fast and seems to produce close-to-optimum results in practice. We conclude the paper with an evaluation of our methods on both synthetic and real-world datasets.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Olken, F.: Random Sampling from Databases. Ph.d. thesis, Lawrence Berkeley National Laboratory (1993)
Acharya, S., Gibbons, P.B., Poosala, V., Ramaswamy, S.: Join Synopses for Approximate Query Answering. In: SIGMOD, pp. 275–286 (1999)
Chaudhuri, S., Motwani, R., Narasayya, V.: On Random Sampling over Joins. In: SIGMOD, pp. 263–274 (1999)
Gemulla, R., Rösch, P., Lehner, W.: Linked Bernoulli Synopses: Sampling Along Foreign Keys (Full Version). Technical report (2007), http://wwwdb.inf.tu-dresden.de/publications
Tuy, H.: Monotonic optimization: Problems and solution approaches. SIAM J. on Optimization 11(2), 464–494 (2000)
Chaudhuri, S., Das, G., Datar, M., Narasayya, R.M.V.R.: Overcoming Limitations of Sampling for Aggregation Queries. In: ICDE, pp. 534–544 (2001)
Rösch, P., Gemulla, R., Lehner, W.: Designing Random Sample Synopses with Outliers. In: ICDE (2008)
Acharya, S., Gibbons, P., Poosala, V.: Congressional Samples for Approximate Answering of Group-By Queries. In: SIGMOD, pp. 487–498 (2000)
Babcock, B., Chaudhuri, S., Das, G.: Dynamic Sample Selection for Approximate Query Processing. In: SIGMOD, pp. 539–550 (2003)
Spiegel, J., Polyzotis, N.: Graph-Based Synopses for Relational Selectivity Estimation. In: SIGMOD, pp. 205–216 (2006)
Getoor, L., Taskar, B., Koller, D.: Selectivity Estimation using Probabilistic Models. In: SIGMOD, pp. 461–472 (2001)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gemulla, R., Rösch, P., Lehner, W. (2008). Linked Bernoulli Synopses: Sampling along Foreign Keys. 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_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-69497-7_4
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)