R. J. Lipton, J. F. Naughton, D. A. Schneider, and S. Seshadri. "Efficient sampling strategies for relational database operations". Theoretical Comput. Sci., 116, 1993.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....1000 Number of iterations Multithreaded Model (b) Half width of inner query. 0 0.1 0.2 0.3 0.4 0 200 400 600 800 1000 Number of iterations Multithreaded Model (c) Half width of outer query. Figure 7: Type A nested query where the outer query involves an aggregate. Several earlier work [6, 8, 9, 11] have also addressed the issue of obtaining confidence intervals. There has also been some work on fast first query processing, that returns the first few answers to users quickly. These work largely focused on developing pipelined join methods or cost models that can predict the cost to obtain ....

R.J. Lipton, J.F. Naughton, D.A. Schneider, and S. Seshadri. Efficient sampling strategies for relational database operations. Theoretical Computer Science, 116, 1993.

.... works on incremental maintenance of approximate synopses include [FM83, FM85, WVZT90, HNSS95, AMS96, GMP97b, GP97] Finally, there has been considerable work on sampling based estimation algorithms for use within a query optimizer (e.g. H OT88, H OT89, LN89, LN90, LNS90, H OD91, HS92, LS92, LNSS93, HNSS93, HNS94, LN95, HNSS95, GGMS96] None of this previous work uses the new techniques described in this paper. 9 Conclusions This paper describes the Aqua system, for fast, highly accurate approximate query answers. It is well known that join operators seriously degrade estimation ....

R. J. Lipton, J. F. Naughton, D. A. Schneider, and S. Seshadri. Efficient sampling strategies for relational database operations. Theoretical Computer Science, 116(1-2):195--226, 1993.

.... Histograms have been studied extensively for application in selectivity estimation in query optimizers [12, 14, 15] In our earlier work, we have identified several novel classes of histograms to build on one or more attributes [19, 18] and also proposed techniques for their efficient computation [11] and incremental maintenance [5] We recently extended histograms for selectivity estimation in spatial databases [2] Much of the work presented in this article on query algebra for histograms has appeared in [10] 8 Conclusions In this article, we have described various histogram based ....

....histogram optimality and practicality for query result size estimation. Proc. of ACM SIGMOD Conf, pages 233 244, May 1995. 10] Y. Ioannidis and V. Poosala. Histogram based techniques for approximating set valued query answers. Proc. of the 25th Int. Conf. on Very Large Databases, 1999. [11] H. V. Jagadish, N. Koudas, S. Muthukrishnan, V. Poosala, K. Sevcik, and T. Suel. Optimal histograms with quality guarantees. Proc. of ACM SIGMOD Conf, 1998. 12] R. P. Kooi. The optimization of queries in relational databases. PhD thesis, Case Western Reserve University, Sept 1980. 14 [13] S. ....

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R.J. Lipton, J.F. Naughton, D.A. Schneider, and S. Seshadri. Efficient Sampling Strategies for Relational Database Operations. Theoretical Computer Science 116(1993): 195--226.

....smaller overheads while reporting an approximate min in response to findmin and deletemin operations. These data structures have linear space footprints. The design of sampling based estimation algorithms is a popular area of research [H OT88, H OT89, LN89, LN90, LNS90, H OD91, HS92, LS92, LNSS93, HNSS93, HNS94, LN95, HNSS95, GGMS96] Results in [LNS90, H OD91, HS92, HNS94] and elsewhere demonstrate the practicality of estimation procedures based on sampling by showing that the time taken to compute the estimate is a small fraction of the time taken to compute the actual query. Studies ....

R. J. Lipton, J. F. Naughton, D. A. Schneider, and S. Seshadri. Efficient sampling strategies for relational database operations. Theoretical Computer Science, 116(12) :195--226, 1993.

.... Histograms have been studied extensively for application in selectivity estimation in query optimizers [12, 14, 15] In our earlier work, we have identified several novel classes of histograms to build on one or more attributes [19, 18] and also proposed techniques for their efficient computation [11] and incremental maintenance [5] We recently extended histograms for selectivity estimation in spatial databases [2] Much of the work presented in this article on query algebra for histograms has appeared in [10] 8 Conclusions In this article, we have described various histogram based ....

....histogram optimality and practicality for query result size estimation. Proc. of ACM SIGMOD Conf, pages 233 244, May 1995. 10] Y. Ioannidis and V. Poosala. Histogram based techniques for approximating set valued query answers. Proc. of the 25th Int. Conf. on Very Large Databases, 1999. [11] H. V. Jagadish, N. Koudas, S. Muthukrishnan, V. Poosala, K. Sevcik, and T. Suel. Optimal histograms with quality guarantees. Proc. of ACM SIGMOD Conf, 1998. 12] R. P. Kooi. The optimization of queries in relational databases. PhD thesis, Case Western Reserve University, Sept 1980. 13 [13] S. ....

[Article contains additional citation context not shown here]

R.J. Lipton, J.F. Naughton, D.A. Schneider, and S. Seshadri. Efficient Sampling Strategies for Relational Database Operations. Theoretical Computer Science 116(1993): 195--226.

....card(input(p) and make the assumption that selectivities of different predicates are independent. Typically these estimations are based on default values and system statistics [SAC 79] although recent work suggests that accurate and inexpensive sampling techniques can be used [LNSS93, HOT88] 2.1 Cost of User Defined Functions in POSTGRES In an extensible system such as POSTGRES, arbitrary userdefined functions may be introduced into both restriction and join predicates. These functions may be written in a general programming language such as C, or in the database query ....

Richard J. Lipton, Jeffrey F. Naughton, Donovan A. Schneider, and S. Seshadri. Efficient Sampling Strategies for Relational Database Operations. To appear in Theoretical Computer Science, 1993.

....algorithms alluded to above. 1.2. Related Work The idea of sampling from base relations in order to quickly estimate the answer to a COUNT query goes back to the work of Hou, et al. HOT88, HOT89] This topic also has been treated in [GGMS96, HNSS96, HNS94, HS92, HS95, HOD91, LN90, LNS90, LNSS93] Figure 2: The elements of R Theta S that have been seen after n sampling steps of a square ripple join (n = 1; 2; 3; 4) usually under the assumption that there exists an index on one or more of the base relations. Techniques that are applicable to other types of aggregation queries follow ....

R. J. Lipton, J. F. Naughton, D. A. Schneider, and S. Seshadri. Efficient sampling strategies for relational database operations. Theoret. Comput. Sci., 116:195--226, 1993.

....card(input(p) and make the assumption that selectivities of different predicates are independent. Typically these estimations are based on default values and system statistics [SAC 79] although recent work suggests that accurate and inexpensive sampling techniques can be used [LNSS93, HOT88] 2.1 Cost of User Defined Functions in POSTGRES In an extensible system such as POSTGRES, arbitrary user defined functions may be introduced into both restriction and join predicates. These functions may be written in a general programming language such as C, or in the database query ....

Richard J. Lipton, Jeffrey F. Naughton, Donovan A. Schneider, and S. Seshadri. Efficient Sampling Strategies for Relational Database Operations. To appear in Theoretical Computer Science, 1993.

.... or selection) they estimate the value selectivity(p) cardinality(output(p) cardinality(input(p) Typically these estimations are based on default values and statistics stored by the DBMS [Selinger et al. 1979] although recent work suggests that inexpensive sampling techniques can be used [Lipton et al. 1993; Hou et al. 1988; Haas et al. 1995] Accurate selectivity estimation is a difficult problem in query optimization, and has generated increasing interest in recent years [Ioannidis and Christodoulakis 1991; Faloutsos and Kamel 1994; Ioannidis and Poosala 1995; Poosala et al. 1996; Poosala and ....

Lipton, R. J., Naughton, J. F., Schneider, D. A., and Seshadri, S. 1993. Efficient Sampling Strategies for Relational Database Operations. Theoretical Computer Science 116, 195--226.

....lead to inaccurate cost estimates, which in turn can cause the optimizer to select an expensive query execution plan. In an effort to avoid these problems, a number of researchers have considered approaches in which selectivities and costs are estimated directly from a sample; see, for example, [GGMS96, HNSS96, HS92, HS95, HOD91, LNS90, LNSS93, NS90]. Several authors have outlined complete sampling based approaches to query optimization [Ant93a, SBM93, Wil91] ffl Parallel processing of queries Balancing the workload between processors is a critical objective of any parallel query processing algorithm. Typically, records are assigned to ....

R. J. Lipton, J. F. Naughton, D. A. Schneider, and S. Seshadri. Efficient sampling strategies for relational database operations. Theoret. Comput. Sci., 116:195--226, 1993.

....result. The proximity of the running aggregate to the final result can therefore be expressed, for example, in terms of a running confidence interval as illustrated above. The width of such a confidence interval serves as a measure of the precision of the estimator. Previous work [HOT88, HNSS96, LNSS93] has been concerned with methods for producing a confidence interval with a width that is specified prior to the start of query processing (e.g. get within 2 of the actual answer with 95 probability ) The underlying idea in most of these methods is to effectively maintain a running confidence ....

R. J. Lipton, J. F. Naughton, D. A. Schneider, and S. Seshadri. Efficient sampling strategies for relational database operations. Theoretical Computer Science, 116:195-- 226, 1993.

....representing properties of the query components (e.g. sizes of groups, selectivities of predicates) Much research has focused on how to make accurate estimates of selectivities and result sizes. These techniques typically use histograms [PIHS96] part of the data properties) or sampling [LNSS93] 3. Parameters representing properties of the run time environment (e.g. amount of available memory, processor speed, multiprogramming level, access characteristics of secondary storage) These are gathered from observations of the realistic deployment environments. If the value of a parameter ....

Richard J. Lipton, Jeffrey F. Naughton, Donovan A. Schneider, and S. Seshadri. Efficient Sampling Strategies for Relational Database Operations. Theoretical Computer Science, pages 195-- 226, 1993.

....presented in Section 2.2 requires a second pass through the optimizer after magic rewriting is performed. The extent of the filtering effect of the filter set (i.e. its selectivity) depends on its cardinality. While it is difficult to estimate the cardinality of projections accurately [LNSS93], existing optimizers do use some assumptions to estimate projection cardinality [Yao77] What is needed is a parameterized plan for the restriction of R k , whose parameter is the filter set. Further, we would like to be able to generate the parameterized plan just once. Each specific plan is ....

R. J. Lipton, J. F. Naughton, D. A. Schneider and S. Seshadri. Efficient sampling strategies for relational database operations. Theoretical Computer Science, 116:195--226, 1993.

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R. J. Lipton, J. F. Naughton, D. A. Schneider, and S. Seshadri. "Efficient sampling strategies for relational database operations". Theoretical Comput. Sci., 116, 1993.

No context found.

Richard J. Lipton, Jeffrey F. Naughton, Donovan A. Schneider, and S. Seshadri. Efficient sampling strategies for relational database operations. Theoretical Computer Science, 116:195--226, 1993.

No context found.

R. J. Lipton, J. F. Naughton, D. A. Schneider, and S. Seshadri. Efficient sampling strategies for relational database operations. Theoretical Computer Science, 116:195--226, 1993.

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Richard J. Lipton, Jeffrey F. Naughton, Donovan A. Schneider, and S. Seshadri. Efficient Sampling Strategies for Relational Database Operations. Theoretical Computer Science, (116):195-226, 1993.

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