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Query evaluation techniques for large databases
 ACM COMPUTING SURVEYS
, 1993
"... Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of objectoriented and extensible database systems will not solve this problem. On ..."
Abstract

Cited by 644 (9 self)
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Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of objectoriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate it: In order to manipulate large sets of complex objects as efficiently as today’s database systems manipulate simple records, query processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and postrelational database systems, including iterative execution of complex query evaluation plans, the duality of sort and hashbased set matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.
Query optimization in database systems
 ACM Computing Surveys
, 1984
"... Efficient methods of processing unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches to improve the performance of query evaluation algorithms have been proposed: logicbased and semantic transformations, fast imple ..."
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Cited by 207 (0 self)
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Efficient methods of processing unanticipated queries are a crucial prerequisite for the success of generalized database management systems. A wide variety of approaches to improve the performance of query evaluation algorithms have been proposed: logicbased and semantic transformations, fast implementations of basic operations, and combinatorial or heuristic algorithms for generating alternative access plans and choosing among them. These methods are presented in the framework of a general query evaluation procedure using the relational calculus representation of queries. In addition, nonstandard query optimization issues such as higher level query evaluation, query optimization in distributed databases, and use of database machines are addressed. The focus, however, is on query optimization in centralized database systems.
The EXODUS Optimizer Generator
, 1987
"... This paper presents the design and an initial performance evaluation of the query optimizer generator designed for the EXODUS extensible database system. Algebraic transformation rules are translated into an executable query optimizer, which transforms query trees and selects methods for executing o ..."
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Cited by 159 (7 self)
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This paper presents the design and an initial performance evaluation of the query optimizer generator designed for the EXODUS extensible database system. Algebraic transformation rules are translated into an executable query optimizer, which transforms query trees and selects methods for executing operations according to cost functions associated with the methods. The search strategy avoids exhaustive search and it modifies itself to take advantage of past experience. Computational results show that an optimizer generated for a relational system produces access plans almost as good as those produced by exhaustive search, with the search time cut to a small fraction.
A lineartime probabilistic counting algorithm for database applications
 ACM Transactions on Database Systems
, 1990
"... We present a probabilistic algorithm for counting the number of unique values in the presence of duplicates. This algorithm has O(q) time complexity, where q is the number of values including duplicates, and produces an estimation with an arbitrary accuracy prespecified by the user using only a smal ..."
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Cited by 92 (5 self)
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We present a probabilistic algorithm for counting the number of unique values in the presence of duplicates. This algorithm has O(q) time complexity, where q is the number of values including duplicates, and produces an estimation with an arbitrary accuracy prespecified by the user using only a small amount of space. Traditionally, accurate counts of unique values were obtained by sorting, which has O(q log q) time complexity. Our technique, called linear counting, is based on hashing. We present a comprehensive theoretical and experimental analysis of linear counting. The analysis reveals an interesting result: A load factor (number of unique values/hash table size) much larger than 1.0 (e.g., 12) can be used for accurate estimation (e.g., 1 % of error). We present this technique with two important applications to database problems: namely, (1) obtaining the column cardinality (the number of unique values in a column of a relation) and (2) obtaining the join selectivity (the number of unique values in the join column resulting from an unconditional join divided by the number of unique join column values in the relation to he joined). These two parameters are important statistics that are used in relational query optimization and physical database design.
Physical database design for relational databases
 ACM Transactions on Database Systems
, 1988
"... This paper describes the concepts used in the implementation of DBDSGN, an experimental physical design tool for relational databases developed at the IBM San Jose Research Laboratory. Given a workload for System R (consisting of a set of SQL statements and their execution frequencies), DBDSGN sugge ..."
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Cited by 76 (0 self)
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This paper describes the concepts used in the implementation of DBDSGN, an experimental physical design tool for relational databases developed at the IBM San Jose Research Laboratory. Given a workload for System R (consisting of a set of SQL statements and their execution frequencies), DBDSGN suggests physical configurations for efficient performance. Each configuration consists of a set of indices and an ordering for each table. Workload statements are evaluated only for atomic configurations of indices, which have only one index per table. Costs for any configuration can be obtained from those of the atomic configurations. DBDSGN uses information supplied by the System R optimizer both to determine which columns might be worth indexing and to obtain estimates of the cost of executing statements in different configurations. The tool finds efficient solutions to the indexselection problem; if we assume the cost estimates supplied by the optimizer are the actual execution costs, it finds the optimal solution. Optionally, heuristics can be used to reduce execution time. The approach taken by DBDSGN in solving the indexselection problem for multipletable statements significantly reduces the complexity of the problem. DBDSGN’s principles were used in the Relational Design Tool (RDT), an IBM product based on DBDSGN, which performs design for SQL/DS, a relational system based on System R. System R actually uses DBDSGN’s suggested solutions as the tool expects because cost estimates and other necessary information can be obtained from System R using a new SQL statement, the EXPLAIN statement. This illustrates how a system can export a model of its internal assumptions and behavior so that other systems (such as tools) can share this model.
Heuristic and Randomized Optimization for the Join Ordering Problem
 VLDB Journal
, 1997
"... Recent developments in database technology, such as deductive database systems, have given rise to the demand for new, costeffective optimization techniques for join expressions. In this paper many different algorithms that compute approximate solutions for optimizing join orders are studied since ..."
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Cited by 67 (2 self)
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Recent developments in database technology, such as deductive database systems, have given rise to the demand for new, costeffective optimization techniques for join expressions. In this paper many different algorithms that compute approximate solutions for optimizing join orders are studied since traditional dynamic programming techniques are not appropriate for complex problems. First, two possible solution spaces, the space of leftdeep and bushy processing trees, respectively, are evaluated from a statistical point of view. The result is that the common limitation to leftdeep processing trees is only advisable for certain join graph types. Basically, optimizers from three classes are analysed: heuristic, randomized and genetic algorithms. Each one is extensively scrutinized with respect to its working principle and its fitness for the desired application. It turns out that randomized and genetic algorithms are well suited for optimizing join expressions. They generate solutions of...
A New Heuristic for Optimizing Large Queries
 In 9th International Conference, DEXA'98
, 1998
"... There is a number of OODB optimization techniques proposed recently, such as the translation of path expressions into joins and query unnesting, that may generate a large number of implicit joins even for simple queries. Unfortunately, most current commercial query optimizers are still based on the ..."
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Cited by 13 (4 self)
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There is a number of OODB optimization techniques proposed recently, such as the translation of path expressions into joins and query unnesting, that may generate a large number of implicit joins even for simple queries. Unfortunately, most current commercial query optimizers are still based on the dynamic programming approach of System R, and cannot handle queries of more than ten tables. There is a number of recent proposals that advocate the use of combinatorial optimization techniques, such as iterative improvement and simulated annealing, to deal with the complexity of this problem. These techniques, though, fail to take advantage of the rich semantic information inherent in the query specification, such as the information available in query graphs, which gives a good handle to choose which relations to join each time. This paper presents a polynomialtime algorithm that generates a good quality order of relational joins. It can also be used with minor modifications to sort OODB a...
Optimization of Large OODB Queries
 In Fifth International Conference on Deductive and ObjectOriented Databases
, 1997
"... There is a number of OODB optimization techniques proposed recently, such as the translation of path expressions into joins and query unnesting, that may generate a large number of implicit joins even for simple queries. Unfortunately, most current commercial query optimizers are still based on the ..."
Abstract

Cited by 2 (1 self)
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There is a number of OODB optimization techniques proposed recently, such as the translation of path expressions into joins and query unnesting, that may generate a large number of implicit joins even for simple queries. Unfortunately, most current commercial query optimizers are still based on the dynamic programming approach of System R, and cannot handle queries of more than ten tables. There is a number of recent proposals that advocate the use of combinatorial optimization techniques, such as iterative improvement and simulated annealing, to deal with the complexity of this problem. These techniques, though, fail to take advantage of the rich semantic information inherent in the query specification, such as the information available in query graphs, which gives a good handle to choose which relations to join each time. This paper presents a polynomialtime algorithm that generates a good quality ordering of OODB algebraic operators. It is based on the mincut algorithm, which cuts...
M. Tech Student, CSE Dept.
"... Query optimization in databases has gain a lot of importance in recent years. In this paper, we have analyzed different techniques of query optimization in relational databases and compared their performance. We have covered the techniques which use different methods for query representation. ..."
Abstract
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Query optimization in databases has gain a lot of importance in recent years. In this paper, we have analyzed different techniques of query optimization in relational databases and compared their performance. We have covered the techniques which use different methods for query representation.