Sellis, T., "Global Query Optimization," Proc. 1986 ACM-SIGMOD Conference on Management of Data, Washington, D.C., May 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....a parallel command. However, we expect high performance systems to store procedures in the system which are subsequently executed. A mechanism is needed to expedite inter query parallelism in this situation. Although it is possible to build a semantic analyzer to detect possible parallelism [SELL86], we are 5 following a much simpler path. Specifically, we are extending POSTGRES with a single keyword parallel that can be placed between any two POSTGRES commands. This will be a marker to the run time system that it is acceptable to execute the two commands in parallel. Hence, TP1 can be ....

Sellis, T., "Global Query Optimization," Proc. 1986 ACM-SIGMOD Conference on Management of Data, Washington, D.C., May 1986.

....are temporally persistent. That is, they have a longer life span and as a result are likely to be evaluated many times. This suggests that several rules can be optimized simultaneously in a group, possibly using some of the techniques developed for multiple query optimization [Fin82, CM86, RC88, Sel86, Cha91] The effect of multiple query optimization can be further enhanced by materializing intermediate results (e.g. common subexpressions) judiciously. Second, rules used for some applications are likely to have priorities or timing requirements associated with their execution. Optimization ....

T. Sellis. Global Query Optimization. In Proceedings of SIGMOD, pages 191--205, 1986.

....divided into regions an idea also proposed by [MDZ93] which contain incomplete query evaluation plans (QEPs) QEPs are composed from basic building blocks which are, step by step, augmented to complete evaluation plans. This building block approach also used for global query optimization [Sel86] incorporates the factorization of common subexpressions (cf. CD92] and the early pruning of non promising alternatives. Knowledge sources are associated with the regions and carry out the augmentation of the (still incomplete) QEPs until, at the top most region, complete QEPs are generated. ....

....heuristics, as e.g. the determination of a good join order (cf. SMK93] if some more regions with the appropriate knowledge sources are integrated into the iteration. The optimization process is controlled by the global search strategy A [Pea84] also used for global query optimization [Sel86]. For that, history and future costs derived from the state of the composition are assigned to each item. The current expressions determine the history, and the remaining operations called future work the future costs. At all times, the item for which the sum of history and future costs is ....

T.K. Sellis. Global query optimization. In Proc. of the ACM SIGMOD Conf. on Management of Data, pages 191--205, Washington, USA, Jun 1986.

....a conventional optimizer. In addition to the work on materialized views [3, 28] such efforts include research whose goal was not physical data independence but simply processing efficiency. Examples include research on reusing common subexpressions within a query [9] or between multiple queries [22], reusing results of previous queries [8] and using integrity constraints for semantic query optimization [6] Kemper and Moerkotte [13] opt for a unified approach of translation and opPage timization for the ASRs by extending a rule based optimizer to include appropriate rewriting rules. Our ....

T. Sellis. Global Query Optimization. In Proc. of the ACM SIGMOD Conf., pages 191--205, 1986.

....in Section 7.3.2 that the choice of the optimal plan from the compiled set of plans can be done quite efficiently. Hence, the overhead imposed by the server initiation rule on the server is not overwhelming. An alternative approach to the problem is to extend global query optimization techniques [34]. 3 Examples In this section, we present an example to show that the energy consumption of work optimal plans may be significantly higher (factors of 3 12) than the energy consumption of energy optimal plans. Figure 1 depicts the power consumption of a typical state of the art portable computer, ....

T.K. Sellis. "Global Query Optimization", In Proceedings of the 1986 ACM SIGMOD Conference on Management of Data.

.... estimates on the size of the results of various temporal joins have been derived [70, 174] In global query optimization, a collection of queries is simultaneously optimized, the goal being to produce a single query evaluation plan that is more efficient than the collection of individual plans [171, 177]. A state transition network appears to be a good way to organize this complex task [94] Materialized views are expected to play an important role in achieving high performance in the face of temporal databases of monotonically increasing size. For an algebra to utilize this approach, incremental ....

Sellis, T.K., "Global query optimization," In C. Zaniolo, editor, Proceedings of the ACM International Conference on Management of Data, pp. 191--205, Washington, DC, May 1986.

....on extended query optimization problems such as optimizing large join queries and considering bushy join trees [11, 12] is also relevant. There is also some similarity between the problem of optimizing the discrimination network for a collection of rules and the multiple query optimization problem [4, 21, 23]. A major difference between work on discrimination network optimization and query optimization is that in discrimination network optimization, update frequency is a key variable. Moreover, discrimination network construction requires making decisions about whether or not to construct memory ....

Timos Sellis. Global query optimization. ACM TODS, 13(1):23--52, 1988.

....into regions an idea also proposed by [MDZ93] which contain incomplete query evaluation plans (QEPs) QEPs are composed from basic building blocks [Loh88] which are, step by step, augmented to complete evaluation plans. This building block approach also used for global query optimization [Sel86] incorporates the factorization of common subexpressions (cf. CD92] and the early pruning of non promising alternatives. Local optimization algorithms (in our terminology called knowledge sources) are associated with the regions and carry out the augmentation of the (still incomplete) QEPs ....

....heuristics, as e.g. the determination of a good join order (cf. SMK93] if some more regions with the appropriate knowledge sources are integrated into the iteration. The optimization process is controlled by the global search strategy A [Pea84] also used for global query optimization [Sel86] For that, history and future costs derived from the state of the composition are assigned to each item. The current expressions determine the history, and the remaining operations called future work the future costs. At all times, the item for which the sum of history and future costs is ....

T. K. Sellis. Global query optimization. In Proc. of the ACM SIGMOD Conf. on Management of Data, pages 191--205, Washington, USA, June 1986.

.... EMP.name= Sam and EMP.age 40 retrieve (desk = null) where EMP.name = Sam and EMP.dept = candy Hence, a user query must be rewritten for each rule, resulting in a serious degradation of performance unless all queries are processed as a group using multiple query optimization techniques [SELL86]. Moreover, a query rewrite system has great difficulty with exceptions [BORG85] For example consider the rule all employees have a steel desk together with the exception Jones is an employee who has a wood desk . If one ask for the kind of desk and age for all employees over 35, then the ....

Sellis, T., "Global Query Optimization," Proc 1986 ACM-SIGMOD Conference on Management of Data, Washington, D.C., June 1986.

....such as optimizing large join queries and considering bushy join trees [9, 10] is also relevant. There is also some similarity between the problem of optimizing the discrimination network for a collection of rules, and optimizing an execution plan for simultaneously evaluating a set of queries [4, 14, 16]. A major difference between work on discrimination network optimization and query optimization is that in discrimination network optimization, update frequency is a key variable. Moreover, discrimination network construction requires making decisions about whether or not to construct memory ....

Timos Sellis. Global query optimization. ACM TODS, 13(1):23--52, 1988.

.... optimization problems such as optimizing large join queries and considering bushy join trees [6, 7] There is also some similarity between the problem of optimizing the discrimination network for a collection of rules, and optimizing an execution plan for simultaneously evaluating a set of queries [2, 10, 12]. The key differences between work on discrimination network optimization and query optimization is that in discrimination network optimization, update frequency is a key variable. Moreover, discrimination network construction requires making decisions about whether or not to construct memory ....

Timos Sellis. Global query optimization. ACM TODS, 13(1):23--52, 1988.

....optimizer. In addition to the work on materialized views [BCL89, YL87] such efforts include research whose goal was not physical data independence but simply processing efficiency. Examples include research on reusing common subexpressions within a query [Hal76] or between multiple queries [Sel86] reusing results of previous queries [Fin82] and using integrity constraints for semantic query optimization [Cha90] Kemper and Moerkotte [KM90b] opt for a unified approach of translation and optimization for the ASRs by extending a rule based optimizer to include appropriate rewriting rules. ....

T. Sellis. Global Query Optimization. In Proc. of the ACM SIGMOD Conf., pages 191--205, 1986.

....ones. First, rules are temporally persistent. That is, they have a longer life span and as a result are likely to be evaluated many times. This suggests that several rules can be optimized simultaneously in a group, possibly using some of the techniques developed for multiple query optimization [10, 5, 20, 1]. The effect of multiple query optimization can be further enhanced by materializing intermediate results (e.g. common subexpressions) judiciously. Second, rules used for real time applications are likely to have priorities or timing requirements associated with their execution. Optimization of ....

T. Sellis. Global Query Optimization. In Proceedings of SIGMOD, pages 191--205, 1986.

No context found.

SE86 Sellis, T.K., Global Query Optimization. Proc. 1986 SIGMOD Conf. on Management of Data (to appear).

No context found.

Sellis T (1986) Global query optimization. In: Proc ACM SIGMOD Conf, pp 191--205, Washington

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