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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 (fi ..."
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Cited by 43 (12 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.
A Chase Too Far?
 In SIGMOD
, 2000
"... In a previous paper we proposed a novel method for generating alternative query plans that uses chasing (and backchasing) with logical constraints. The method brings together use of indexes, use of materialized views, semantic optimization and join elimination (minimization). Each of these techniqu ..."
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Cited by 32 (7 self)
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In a previous paper we proposed a novel method for generating alternative query plans that uses chasing (and backchasing) with logical constraints. The method brings together use of indexes, use of materialized views, semantic optimization and join elimination (minimization). Each of these techniques is known separately to be beneficial to query optimization. The novelty of our approach is in allowing these techniques to interact systematically, eg. nontrivial use of indexes and materialized views may be enabled only by semantic constraints.
A Query Language and Processor for a WebSite Management System
 In Proc. of Workshop on Management of Semistructured Data
, 1997
"... syntax level. At this level, we apply sourcetosource transformations to reduce the number of edges that must be traversed in the input graph. We do this by finding common prefixes in multiple regular path expressions. For example, consider: where Root(p); p ("A":"B"):"A&q ..."
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Cited by 30 (1 self)
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syntax level. At this level, we apply sourcetosource transformations to reduce the number of edges that must be traversed in the input graph. We do this by finding common prefixes in multiple regular path expressions. For example, consider: where Root(p); p ("A":"B"):"A" q; p "A":"C":"D" r We can rewrite the first regular expression into an equivalent expression: where Root(p); p "A":("B":"A") q; p "A":"C":"D" r Next, we introduce two node variables, x and y: where Root(p); p "A" x; x ("B":"A") q; p "A" y; y "C":"D" r Finally, if q and r are "independent", meaning that none of the clauses in link creates and edge between them, then we can collapse the x and y node variables into a single variable z, and get: where Root(p); p "A" z; z ("B":"A") q; z "C":"D" r The resulting query is equivalent to the original, but it traverses each edge "A" accessible from the root once. Automaton level. At this level, each regular expression is translated into a generalized NDFA. There is one ...
Semantic query optimization for object databases
 In Proc. of the 13th Int'l. Conference on Data Engineering
, 1997
"... ..."
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
LogicBased Semantic Query Optimization for Object Databases
"... We present a technique for semantic query optimization (SQO) for object databases. We use the ODMG standard languages ODL and OQL. The object schema and object query are translated into a Datalog representation. Semantic knowledge about the object model and the particular application is expressed ..."
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We present a technique for semantic query optimization (SQO) for object databases. We use the ODMG standard languages ODL and OQL. The object schema and object query are translated into a Datalog representation. Semantic knowledge about the object model and the particular application is expressed as integrity constraints. SQO is performed in the Datalog representation and an equivalent logic query, and subsequently an equivalent object query, is obtained. SQO is based on the residue technique of [4]. We show that our technique generalizes previous research on SQO for object databases. It can also be applied to queries with methods and structure constructors and it can utilize access support relations. 1 Introduction In this paper we show how to apply semantic query optimization (SQO) techniques to query processing in object databases. SQO uses semantic knowledge in the form of integrity constraints (ICs) to reformulate an object query into an equivalent form that can be evalu...