S. Shekhar, J. Srivastava, S. Dutta, A Formal Model of Trade-off Between Optimization and Execution Costs in Semantic Query Optimization, Proc 14th VLDB Conference 1988, pp 457-467.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....to each query. But this can provide a random sample with low probability of containing useful rules. The histogram based rule sets described in this paper are small, easily accessed, and all rules have a good chance of being used. Previous semantic optimisation algorithms [e.g. Hs 95, Ki 81, Sh 88, Si 88] have been iterative, progressively applying new rules as the query is changed by previous rules. Rule application means adding consequent conditions to the query tom rules whose antecedents are implied by query conditions) This is slow. Its sequential character is tindesirable in a ....

....[13. 30] D Branches also occur when the same antecedent condition appears in more than one rule, whose consequents describe different attributes. Eg: a = PC 49 ) 115.04 b 208.63) and (a = PC 49 ) 3.6.80 c 12.7. 82) The traditional approach to semantic query reformulation [e.g. Sh 88] does not refer to a graph. It entails iteratively adding consequent conditions tom rules to the query if the rule antecedents are implied either by Original Conditions in the query or by conditions subsequently added to the query. The process, known as Semantic Expansion, can be seen as path ....

S. Shekhar, J. Srivastava, S. Dutta, A Formal Model of Trade-off Between Optimization and Execution Costs in Semantic Query Optimization, Proc 14th VLDB Conference 1988, pp 457-467.

....Maintenance, Dynamic Semantic Rules, Query Optimization. 1 Introduction Previous experimental results have shown that semantic query optimization may significantly reduce query execution time by reformulating a query into a semantically equivalent query that is more efficient to execute [HK94, HK93, SSS92, SSD92, CGM90, SO89, Kin81, HZ80]. Semantic query optimization has traditionally derived transformation rules from schemabased constraints which are valid for all instances of a database. However, schema based constraints are often difficult to define and are typically too general to be useful. Consequently, a number of ....

S. Shekhar, J. Srivastava, and S. Dutta. A formal model of trade-off between optimization and execution costs in semantic query optimization. Data and Knowledge Engineering, 8:131--151, 1992.

....complexity of queries and the heterogeneity of information sources. However, it is difficult for conventional query optimization techniques to solve all the problems for the next generation information systems. Semantic query optimization (SQO) Hammer and Zdonik, 1980, King, 1981, Siegel, 1988, Shekhar et al. 1988, Shenoy and Ozsoyoglu, 1989, Yu and Sun, 1989, Chakravarthy et al. 1990, Sun and Yu, 1994] is a promising query optimization technique that can complement conventional techniques to overcome the heterogeneity and considerably reduce query execution cost. The essential idea of semantic query ....

....in query execution and relational rules allow an SQO optimizer to detect redundant joins, it is important to use relational rules in the optimization. We note that previous work in SQO cannot apply general relational rules in the optimization [Hammer and Zdonik, 1980, King, 1981, Siegel, 1988, Shekhar et al. 1988, Shenoy and Ozsoyoglu, 1989, Yu and Sun, 1989, Chakravarthy et al. 1990, Sun and Yu, 1994] To detect redundant joins, they use referential integrity constraints [Ullman, 1988] a restrictive form of relational rules that allow only one literal as the antecedent. One of the new features of the ....

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Shashi Shekhar, Jaideep Srivastava, and Soumitra Dutta. A formal model of trade-off between optimization and execution costs in semantic query optimization. In Proceedings of the 14th VLDB Conference, Los Angeles, CA, 1988.

....at run time. The paper then analyzes how to apply updates to instance based constraints in order to refresh them. Keywords: Metadata View Graph, Instance Based Constraints, Semantic Query Optimization, Constraint Discovery, Metadata Maintenance. 1 Introduction Semantic query optimization [HZ80, Kin81, SO89, CGM90, SSD92, HK93] uses transformation rules to reformulate a query into a semantically equivalent query that is more efficient to execute. Traditionally, transformation rules have been derived exclusively from schemebased integrity constraints that are valid for all instances of a database. Transformation rules ....

Shashi Shekhar, Jaideep Srivastava, and Soumitra Dutta. A formal model of trade-off between optimization and execution costs in semantic query optimization. Data and Knowledge Engineering, 8:131--151, 1992.

....and 2,000,000 ft 3 of storage space. From the rule given above, we can reformulate the query so that there is no need to check the railroad access of seaports, which may save some execution time. Many algorithms for semantic query optimization have been developed (Hsu Knoblock 1993; King 1981; Shekhar, Srivastava, Dutta 1988; Shenoy Ozsoyoglu 1989) Average speedup ratios from 20 to 40 percent using hand coded knowledge are reported in the literature. This approach to query optimization has gained increasing attention recently because it is applicable to almost all existing data knowledge base systems. This feature ....

....are obtained by executing each query 10 times and then computing the average. The reformulation yields significant cost reduction for high cost queries. The overall average gain is 57.10 percent, which is better than systems using hand coded rules for semantic optimization (Hsu Knoblock 1993; Shekhar, Srivastava, Dutta 1988; Shenoy Ozsoyoglu 1989) The gains are not so high for the low cost group. This is not unexpected, because the queries in this group are already very cheap and the cost cannot be reduced much further. The average overheads listed in the table show the time in seconds used in reformulation. This ....

Shekhar, S.; Srivastava, J.; and Dutta, S. (1988). A formal model of trade-off between optimization and execution costs in semantic query optimization. In Proceedings of the 14th VLDB Conference.

.... this issue, researchers have proposed interleaving query planning and execution so that the query processor can use intermediate data to refine the part of the query plan that has not been completely executed [9, 12, 13] A relatively unexplored area is the use of semantic query optimization (SQO) [14, 15, 16, 17, 18, 19, 20, 21] for multi source query plan optimization. The advantage of SQO is that the optimizer can infer the information about intermediate data from semantic knowledge prepared prior to query execution time. Another reason is that SQO supports the extensibility of multidatabase systems because it ....

....for global optimization. This section describes these features and presents the complete local optimization algorithm. 4. 1 Implication Closures An intuitive search algorithm for SQO is to repeat proposing and applying reformulations to the query until the optimal equivalent query is found [15, 17, 16]. However, such a generate andtest algorithm might miss applicable rules and hence miss optimization opportunities because an applicable rule to a query may become inapplicable if some literals are deleted from the query. To address this problem, when the optimizer detects a redundant literal, ....

S. Shekhar, J. Srivastava, and S. Dutta, "A formal model of trade-off between optimization and execution costs in semantic query optimization," in Proceedings of the 14th VLDB Conference, (Los Angeles, CA), 1988.

....maintainer. As other optimization problems, semantic query optimization needs to balance a variety of tradeoffs under the resource constraints. For example, the query optimizer must trade off the time spent for the optimization and the quality of the resulting query. Previously, Shekhar et al. (Shekhar, Srivastava, Dutta 1988) presented an approach to the tradeoff in the query optimizer. This paper focuses on the tradeoff in the rule induction. Tradeoffs between Effectiveness and Robustness The rule induction problem for SQO is to learn a set of high utility semantic rules that maximize the net performance of the ....

Shekhar, S.; Srivastava, J.; and Dutta, S. 1988. A formal model of trade-off between optimization and execution costs in semantic query optimization. In Proceedings of the 14th VLDB Conference.

....replan parts of a query that fail while continuing to execute the other subqueries of the overall plan. The semantic approach to query optimization was first developed by King [ King, 1981 ] and has since been extended in a number of systems [ Adam et al. 1993; Shenoy and Ozsoyoglu, 1989; Shekhar et al. 1988; Siegel, 1988 ] Our approach to this problem differs from other related work in that we do not rely on explicit heuristics of the database implementation to guide search for optimized queries in the combinatorially large space of the potential optimizations. Instead, our algorithm considers all ....

Shashi Shekhar, Jaideep Srivastava, and Soumitra Dutta. A formal model of trade-off between optimization and execution costs in semantic query optimization. In Proceedings of the 14th VLDB Conference, Los Angeles, CA, 1988.

....parts of a query that fail while continuing to execute the other subqueries of the overall plan. The semantic reformulation approach to query optimization was first developed by King [ King, 1981 ] and has since been extended in a number of systems [ Adam et al. 1993, Shenoy and Ozsoyoglu, 1989, Shekhar et al. 1988, Siegel, 1988 ] Our approach to this problem differs from other related work in that we do not rely on explicit heuristics of the database implementation to guide search for reformulations in the combinatorially large space of the potential reformulated subqueries. Instead, our algorithm ....

Shashi Shekhar, Jaideep Srivastava, and Soumitra Dutta. A formal model of trade-off between optimization and execution costs in semantic query optimization. In Proceedings of the 14th VLDB Conference, Los Angeles, CA, 1988.

....with railroad access and 2,000,000 ft 3 of storage space. From the rule given above, we can reformulate the query so that there is no need to check the railroad access of seaports, which may save some execution time. Many SQO algorithms have been developed (Hammer and Zdonik 1980; King 1981; Shekhar et al. 1988; Shenoy and Ozsoyoglu 1989) Average savings from 20 to 40 percent using hand coded knowledge are reported in the literature. 202 Hsu Knoblock A learning approach to automatic acquisition of semantic knowledge is crucial to SQO. Most of the previous work in SQO assumes that semantic knowledge ....

....describes in detail the usage and acquisition of range facts. The performance statistics on those queries are shown in Table 17.4. There are 11 out of 26 testing queries in this set. The overall saving of this class is 43.48 percent, comparable to the SQO systems using hand coded rules (King 1981; Shekhar et al. 1988; Shenoy and Ozsoyoglu 1989) 17.6 Related Work Previously, systems for learning background knowledge for semantic query optimization were proposed by Siegel (1988) and by Shekhar et al. 1993) Siegel s system uses predefined heuristics and an example query to drive the learning. This approach ....

Shekhar, S., Srivastava, J., and Dutta, S. 1988. A Formal Model of Trade-off Between Optimization and Execution Costs in Semantic Query Optimization. In Proceedings of the 14th VLDB Conference. Los Angeles, California.

....in two ways. First, it selects a rule base which is small in size and depends on the query. Second, it uses theory resolution which reduces substantially the number of inference steps during query transformation. A different approach to control the cost of semantic query optimization is taken in [SSD88] The main focus in this work is the query modification phase of query optimization. The preliminary performance results are very encouraging. The next step is to extend our experiments for various distributions of the temporal data and rules and investigate the access planning phase. As the ....

S. Shekhar, J. Srivastava, and S. Dutta. A formal model of trade off between optimization and execution costs in semantic query optimization. In Proc. of VLDB-88, 1988.

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S. Shekhar, J. Srivastava, S. Dutta, A Formal Model of Trade-off between Optimization and Execution Costs in Semantic Query Optimization, Proc 14th VLDB Conference, 1988, pp 457-467.

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