S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In Proc. of the Int. Conf. on Extending Database Technology (EDBT), pages 167--182, 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....Rules and Semantic Optimization Related Work Within the intermediate query representations, there are a lot of opportunities for syntactic calculus or algebraic transformations. A lot of experience has been gained applying these optimizations in the relational or object oriented context [CS96, Cha98, GLR97, Hel98, Ioa96, KPH98, PS96, PS97, RS93, SdBB96, SO95a, SO95b, WM99] This thesis focuses on semantic query optimization, i.e. query transformations based on semantic knowledge rather than syntactic equivalence. In particular, semantic knowledge is represented by association rules of ....

S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In Conf on Extending Database Technology (EDBT), pages 167--182, 1996.

....set of types belonging to the set SourceSchema, denoting all the types found in the data sources, are treated as source nodes of a graph. For the rest of the types, we can derive an SQL expression by using existing view reduction algorithms, such as [33] corrected with the results of [19, 44, 45] [14, 11, 41, 48, 36]. Our algorithm is applicable to graphs of activities that do not involve updates. In most cases, an update operation can be considered as the combination of insertions and deletions or as the application of the appropriate function to the relevant attributes. The results of the application of ....

S. Chaudhuri, K. Shim. Optimizing Queries with Aggregate Views. In Proc. 5th Intl. Conf. on Extending Database Technology (EDBT), pp. 167-182, Avignon, France 1996.

....processes affecting them. For the rest of the types, we can derive an SQL expression by using existing view reduction algorithms. Several complementary proposals exist such as [Kim82 corrected with the results of GaWo87, Mura89, Mura92 (which we will mention as Kim82 in the sequel) Daya87] [ChSh96], MFPR90] PiHH92] LeMS94] The proposed algorithm is applicable to graphs of activities that do not involve updates. In most cases, an update operation can be considered as the combination of insertions and deletions or as the application of the appropriate function to the relevant ....

S. Chaudhuri, K. Shim. Optimizing Queries with Aggregate Views. In EDBT Conference, 1996.

....is usually done last. Query flock plans rectify these problems by using reduction of base relations to circumvent the shape of the query plan and auxiliary relations to use aggregation on partial results as early as possible The problem of including aggregation in query optimization is studied in [13, 6, 5]. In these papers, aggregation is pushed down, or sometimes up) the query plan tree. The key difference with our work is that we use aggregation on a subset of the original query and the result is used to reduce the size of intermediate steps. Eventually the aggregation must be performed again ....

S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In Proceedings of the 5th International Conference on Extending Database Technology, pages 167--182, Avignon, France, March 1996.

....of queries could be avoided completely. Yan and Larson in [YL94, YL95] describe a class of transformations that allow the query optimizer to push a group by past a join (eager aggregation) or pulls a group by above a join (lazy aggregation) In a similar direction, Chaudhuri and Shim in [CS94, CS96] present a similar class of pull up and pushdown transformations. Furthermore, they incorporate these transformations in optimizers and propose a cost based optimization algorithm to pick a plan. In [GHQ95] Gupta, Harinarayan and Quass try to unify these transformations, viewing aggregation as ....

Surajit Chaudhuri and Kyuseok Shim. Optimizing queries with aggregate views. In Extending Database Technology, pages 167--182, 1996.

....A large body of work exists on query optimization in databases. Graefe surveys various principles and techniques [Gra93] The issues of aggregation and join have been studied separately until quite recently, when a number of papers on optimization of both aggregation and join have appeared [YL94, YL95, CS94, CS96, GHQ95]. Yan and Larson in [YL94, YL95] describe a class of transformations that allow the query optimizer to push a group by past a join (eager aggregation) or pull a groupby above a join (lazy aggregation) In a similar direction, Chaudhuri and Shim in [CS94, CS96] present a similar class of pull up ....

.... join have appeared [YL94, YL95, CS94, CS96, GHQ95] Yan and Larson in [YL94, YL95] describe a class of transformations that allow the query optimizer to push a group by past a join (eager aggregation) or pull a groupby above a join (lazy aggregation) In a similar direction, Chaudhuri and Shim in [CS94, CS96] present a similar class of pull up and push down transformations. Furthermore, they incorporate these transformations in optimizers and propose a cost based optimization algorithm to pick a plan. In [GHQ95] Gupta, Harinarayan and Quass try to unify these transformations, viewing aggregation as ....

Surajit Chaudhuri and Kyuseok Shim. Optimizing queries with aggregate views. In Extending Database Technology, pages 167--182, 1996.

....query processing. This area is starting to receive the attention it deserves. A number of conventional relational query processing approaches have been applied to or extended for answering OLAP queries. Some of this work has concentrated on efficiently performing GROUP BY [8, 9, 20] aggregation [10, 23, 33, 30, 50, 68, 69], join or range queries [32, 60, 64] or supporting incomplete query answers [6, 29, 66] Several approaches have been proposed for supporting the SQL CUBE operator, including [2, 17, 23, 42, 53, 58] Yet another facet of query processing that has received attention in the literature is that of ....

S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In P. Apers, M. Bouzeghoub, and G. Gardaring, editors, Advances in Database Technology -- EDBT'96 5th Intl. Conf. on Extending Database Technology, volume 1057 of Lecture Notes in Computer Science, pages 167--182. Springer-Verlag, New York, 1996.

....of finding the equivalent rewritings for SQL queries with multiset semantics, grouping and aggregation, has received little attention. Several researchers have considered performing syntactic transformations on queries with grouping and aggregation that preserve equivalence of the query (e.g. [YL94, LMS94, CS94, RSSS95, GHQ95, CS96, LM96]) Gupta et al. GHQ95] have shown how these transformations can be used for finding rewritings of queries by transforming the query in a way that the definition of the view would be identical to a sub part of the definition of the query. In addition to being more restrictive than our semantic ....

....transformational approach. They perform syntactic transformations on the operator tree representation of the query such that the definition of the view would be identical to a sub part of the definition of the query. Additional transformations on queries involving aggregation have been proposed by [YL94, LMS94, CS94, RSSS95, GHQ95, CS96, LM96]. The transformational approach is more restrictive than our semantic approach in particular, the algorithm of Gupta et al. does not take the conditions in the WHERE and HAVING clauses into account when comparing Sel(Q) with Sel(V ) and Groups(Q) with Groups(V ) see, e.g. conditions C a ....

[Article contains additional citation context not shown here]

S. Chaudhuri and K .Shim. Optimizing queries with aggregate views. In Proc. EDBT, 1996.

....that accurate computation of the entire data cube will often be unnecessary; online approximations of the various aggregates are likely to suffice in numerous situations. Other recent results on relational aggregation have focused on new transformations for optimizing queries with aggregation [CS96, GHQ95, YL95, SPL96, SHP 96] The techniques in these papers allow query optimizers more latitude in reordering operators in a plan. They are therefore beneficial to any system supporting aggregation, including online aggregation systems. There has been some initial work on fast first query ....

S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In P. M. G. Apers, M. Bouzeghoub, and G. Gardarin, editors, Advances in Database Technology-- EDBT'96 5th Intl. Conf. on Extending Database Technology, volume 1057 of Lecture Notes in Computer Science, pages 167--182. Springer-Verlag, New York, 1996.

No context found.

S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In Proc. of the Int. Conf. on Extending Database Technology (EDBT), pages 167--182, 1996.

No context found.

S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In Proc. of the Int. Conf. on Extending Database Technology (EDBT), pages 167--182, 1996. 22

No context found.

S. Chaudhuri and K .Shim. Optimizing queries with aggregate views. In Proc. EDBT, 1996.

No context found.

S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In Proc. of the Int. Conf. on Extending Database Technology (EDBT), pages 167--182, 1996.

No context found.

S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In Proc. of the Int. Conf. on Extending Database Technology (EDBT), pages 167--182, 1996. 22

No context found.

S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In Proc. of the Int. Conf. on Extending Database Technology (EDBT), pages 167--182, 1996.

No context found.

Surajit Chaudhuri and Kyuseok Shim. Optimizing Queries with Aggregate Views. In Proc. EDBT96, to appear. 15

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC