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An Equational Chase for PathConjunctive Queries, Constraints, and Views
 In ICDT
, 1999
"... We consider the class of pathconjunctive queries and constraints (dependencies) defined over complex values with dictionaries. ..."
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Cited by 44 (12 self)
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We consider the class of pathconjunctive queries and constraints (dependencies) defined over complex values with dictionaries.
Physical Data Independence, Constraints, and Optimization with Universal Plans
, 1999
"... We present an optimization method and algorithm designed for three objectives: physical data independence, semantic optimization, and generalized tableau minimization. The method relies on generalized forms of chase and "backchase" with constraints (dependencies). By using dictionaries (finite funct ..."
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Cited by 41 (10 self)
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We present an optimization method and algorithm designed for three objectives: physical data independence, semantic optimization, and generalized tableau minimization. The method relies on generalized forms of chase and "backchase" with constraints (dependencies). By using dictionaries (finite functions) in physical schemas we can capture with constraints useful access structures such as indexes, materialized views, source capabilities, access support relations, gmaps, etc. The search space for query plans is de ned and enumerated in a novel manner: the chase phase rewrites the original query into a "universal" plan that integrates all the access structures and alternative pathways that are allowed by applicable constraints. Then, the backchase phase produces optimal plans by eliminating various combinations of redundancies, again according to constraints. This method is applicable (sound) to a large class of queries, physical access structures, and semantic constraints. We prove that it is in fact complete for "pathconjunctive" queries and views with complex objects, classes and dictionaries, going beyond previous theoretical work on processing queries using materialized views.
Object/Relational Query Optimization with Chase and Backchase
, 2000
"... Traditionally, query optimizers assume a direct mapping from the logical entities modeling the data (e.g. relations) and the physical entities storing the data (e.g. indexes), each physical entity corresponding precisely to one logical entity. This assumption is no longer true in nontraditional app ..."
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Cited by 12 (0 self)
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Traditionally, query optimizers assume a direct mapping from the logical entities modeling the data (e.g. relations) and the physical entities storing the data (e.g. indexes), each physical entity corresponding precisely to one logical entity. This assumption is no longer true in nontraditional applications (objectoriented and semistructured databases, data integration), which often exhibit a mismatch between the logical view and the actual storage of data. In addition, there is an increased amount of redundancy, even at the logical level, that can greatly enhance optimization opportunities, if exploited. To deal with all this, we propose a novel architecture for query optimization, in which physical optimization is leveraged at the level of query rewriting. As a consequence, the other important aspect of query optimization, semantic optimization (that takes advantage of the redundancy at the logical level), can be naturally incorporated. The optimizer can then make global decisions based on both semantic and physical knowledge, leading to plans of higher quality than those obtainable by a traditional twolevel approach. The main idea
Query optimization  The CROQUE project
, 1996
"... This paper describes parts of a concept for the evaluation and optimization of ODMGOQL queries. We present a logical object algebra for the internal representation of OQL queries. Algebraic expressions are also represented as query trees. Different optimization techniques are sketched: factorizat ..."
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Cited by 8 (3 self)
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This paper describes parts of a concept for the evaluation and optimization of ODMGOQL queries. We present a logical object algebra for the internal representation of OQL queries. Algebraic expressions are also represented as query trees. Different optimization techniques are sketched: factorization of common subexpressions, dependencybased optimization and query rewriting. Afterwards, execution plan generation from query trees is presented. The transformation of physical queries into ObjectStore DML code and the developed cost model for the calculation of the costs of the execution plans are not considered here. Keywords: OQL, logical and physical algebra, optimization of objectoriented queries. 1 Introduction In the framework of the CROQUE project 1 we focus on the development of optimization techniques for object databases. Central topics of our work are examinations about rulebased rewriting of algebraic queries, costbased selection of evaluation mechanisms for obje...
Processing OODB Queries by OAlgebra
 In International Conference on Information and Knowledge Management (CIKM
, 1996
"... OAlgebra is an object algebra designed for processing ObjectOriented Database (OODB) queries. We present the concept of internal type objects, which are uniform and represent the frontend objects. OAlgebra is an algebra whose operands are collections of internal objects. Due to uniform operands a ..."
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Cited by 8 (0 self)
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OAlgebra is an object algebra designed for processing ObjectOriented Database (OODB) queries. We present the concept of internal type objects, which are uniform and represent the frontend objects. OAlgebra is an algebra whose operands are collections of internal objects. Due to uniform operands and simple operators defined in OAlgebra, a small yet powerful set of OAlgebra laws can be obtained, which is important for query optimization by algebraic rewriting. After presenting OAlgebra, we introduce an approach to transform OQL queries to OAlgebra queries. Since OAlgebra operations do not have complex arguments, the nested queries of OQL can be reduced by a general method after they are transformed to OAlgebra queries. Compared to other approaches of reducing nested queries, this approach is more general because it is not restricted by the patterns of nested queries. 1 Introduction OODB query languages, such as OQL [Ca94], are usually converted to some internal representations ...