Y. Ioannidis. Query Optimization. ACM Computing Surveys, symposium issue on the 50th Anniversary of ACM. Vol. 28, No. 1, March 1996, pp. 121-123.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... 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 the form: p ....

Y.E. Ioannidis. Query optimization. ACM Computing Surveys, 28(1):121--123, 1996.

....optimal selection of materialized views because a different set may lead to better materialized views. It is important to consider all three problems together in materialized view selection. A. Query Optimization A lot of research has been done on this topic. A valuable review can be found in [13] and [14] In query optimization, join operation is one of the most expensive operations. For simplicity we only consider join operation in this paper. That is, query optimization will be regarded as join order optimization here. Assume that a database is given a set of relations . A local ....

Y. E. Ioannidis, "Query optimization," ACM Comput. Surv., vol. 28, no. 1, pp. 121--123, Mar. 1996.

....of the query optimiser is a major factor in the performance of the database. A great deal of research has been done in this area during the history of database systems, and we will limit ourselves to the applications of density estimation. A good survey of the discipline was given by Ioannidis [Ioa96], which is the source of much of the following summary. Terminology. A database consists of tables, consisting of some (unordered) set of records each with a fixed number and type of fields. In the terms of this paper, each table is a set of sample data where a record is one data point and its ....

Yannis E. Ioannidis. Query optimization. ACM Computing Surveys, 28(1):121--123, 1996. http://citeseer.nj.nec.com/ioannidis96query.html .

....focuses on query processing techniques that can adapt to changing conditions during execution time. Also, 11] uses a very limited set of classification criteria. 14] extends this set by proposing some dimensions over which AQP systems may di#er. Other previous surveys on query optimisation, like [10, 12], include techniques that have a dynamic flavour, in the sense that the QP they produce at compile time is not completely fixed, but such techniques are not covered by the definition of adaptivity given earlier. 2 The Scope of the Survey The query evaluator of a database management system (DBMS) ....

.... by a collection of independent, semi autonomous processing sites that are connected via a network that could be spread over a large geographic area, whereas the latter are tightly coupled systems controlling multiple processors that are in the same location, usually in the same machine room [12]. The responsibility for adaptivity decisions (AD) may also be assigned di#erently. Such decisions can be taken: by the physical operators (O) when physical operators are implemented in such a way that they can adapt their behaviour during runtime without being invoked by any other DBMS ....

Y. Ioannidis. Query optimization. ACM Computing Surveys, 28(1):121--123, 1996.

....the optimal selection of materialized views because a di erent set may lead to better materialized views. It is important to consider all three problems together in materialized view selection. A. Query Optimization A lot of research has been done on this topic. A valuable review can be found in [13], 14] In query optimization, join operation is one of the most expensive operations. For simplicity we only consider join operation in this paper. That is, query optimization will be regarded as join order optimization here. Assume that a database D is given a set of relations R 1 ; R 2 ; ....

Y. E. Ioannidis, \Query optimization," ACM Computing Surveys, vol. 28, no. 1, pp. 121-123, March 1996.

....be used for joins involving relatively few variables. Next we apply hill climbing techniques for the case of numerous inputs. 5.1. Optimization Using Dynamic Programming. Dynamic programming has been successfully applied for optimization of relational queries involving a small number of inputs [18]. DP plan algorithm (Figure 10) computes the best execution strategy for a query incrementally, based on optimal plans of its subgraphs. The recursive equation implemented by the algorithm is Cost(Q x,y ) 5.3) min Cost ST (Q x,y ) min # decomposition y (Cost(Q x 1,y ) Cost WR (Q ....

Ioannidis, Y. Query Optimization. ACM Computing Surveys, 28(1): 121--123, 1996.

....Cost Factors Figure 1: Middleware Architecture while the T D operator results in an SQL CREATE TABLE statement being issued, followed by the invocation of a DBMS specific data loader. Although both heuristic and cost based, this optimizer is lighter weight than a full blown DBMS optimizer [Jar84, Ioa96], because less information is available to it. While the middleware treats the underlying DBMS as a (quite full featured ) file system, it is not possible for the middleware to accurately estimate the time for the DBMS to deliver a block of tuples from a perhaps involved SQL statement associated ....

Y. Ioannidis. Query Optimization. ACM Computing Surveys, 28(1): 121--123 (1996).

....in the field of ooDBMS, to our best knowledge this is the first approach which tries to invert complex regular path expressions in order to exploit index structures defined for the destination object types of the paths. For a general overview on query optimization problems in ooDBMS we refer to [13]. In [15] Mitschang presents the basic concepts and implementation aspects 16 of query processing and query optimization. Furthermore multiple interesting articles dealing with various aspects of query optimization can be found in [7] Finally some rule based approaches to query optimization are ....

Y.E. Ioannidis. Query optimization. ACM Computing Surveys, 28(1):121 -- 123, Marz 1996.

....can be used for joins involving relatively few variables. Next we apply hill climbing techniques for the case of numerous inputs. 5. 1 Optimization using Dynamic Programming Dynamic programming has been successfully applied for optimization of relational queries involving a small number of inputs [18]. DP plan algorithm (Figure 10) computes the best execution strategy for a query incrementally, based on the optimal plans of its subgraphs. The recursive equation implemented by the algorithm is: Cost(Q x,y ) min Cost ST (Q x,y ) min y ion decomposit (Cost(Q x 1,y ) Cost WR (Q x 1,y ,v x ....

Ioannidis, Y. Query Optimization. ACM Computing Surveys, 28(1), 121-123, 1996.

....frequently [ST99] The query processing and optimization for SQL TP can draw on the rich literature for standard relational queries [Cha98] due to our translation based approach. However, the translation produces fairly complex SQL queries, which in turn require advanced optimization techniques [FMV94,Ioa96]. In addition, we may have to consider a native implementation of the normalization technique used to evaluate SQL TP queries, similarly to coalescing [BSS96] 6 Without resorting to kludges, e.g. to coercing temporal values to integers. SQL TP: a Temporal Extension of SQL 9 ....

Yannis E. Ioannidis. Query Optimization. Computing Surveys, 28(1):121-- 123, 1996.

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Y. Ioannidis. Query Optimization. ACM Computing Surveys, symposium issue on the 50th Anniversary of ACM. Vol. 28, No. 1, March 1996, pp. 121-123.

No context found.

Y. Ioannidis. Query Optimization. ACM Computing Surveys, symposium issue on the 50th Anniversary of ACM. Vol. 28, No. 1, March 1996, pp. 121-123.

No context found.

Y. Ioannidis. Query Optimization. ACM Computing Surveys, symposium issue on the 50th Anniversary of ACM. Vol. 28, No. 1, March 1996, pp. 121-123.

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Yannis Ioannidis. Query Optimization. ACM Computing Surveys, 1996.

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Y. Ioannidis. Query Optimization. ACM Computing Surveys, 28(1): 121--123 (1996).

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Y. Ioannidis. Query optimization. ACM Computing Surveys, 28(1):121--123, 1996.

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Y.E. Ioannidis. Query Optimization. ACM Computing Surveys, 28(1):121--123, 1996.

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