On the efficient computation of the difference between consecutive database states

Küchenhoff, Volker

doi:10.1007/3-540-55015-1_26

Cited by 54 publications

(27 citation statements)

References 21 publications

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“…Our approach, for query evaluation rather than for constraint checking, differs in storing previous derived relations and in transforming the programs used in query evaluation. [4,25]. The goal of this approach is to efficiently compute the standard model of a stratified database after a database update.…”

Section: Semi-naive Evaluationmentioning

confidence: 99%

“…It is similar to our approach in using the previous standard model (analogous to our stored relations) to simplify the task of computing the standard model (query answer) after the update. Our approach differs by storing intermediate relations rather than reasons (or "supports") for including computed facts [4], by not using meta-programs to compute the difference between successive models [25], and by transforming the programs used in query evaluation. Our approach is, however, more restricted as it does not allow negation in rules and queries.…”

Section: Semi-naive Evaluationmentioning

confidence: 99%

“…However, it also raises the complexity of query evaluation. The problem of efficiently computing Datalog queries has attracted a great deal of attention in the database and logic programming communities e.g., [6,8,12,14,17,18,24,25,27].…”

Section: Introductionmentioning

confidence: 99%

“…Our task is closely related to the problem of efficiently updating the standard model [2] of a definite or more generally stratified database [4,25]. Our approach is very useful in maintaining materialized views upon updates.…”

Section: Introductionmentioning

confidence: 99%

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Nonrecursive incremental evaluation of Datalog queries

Dong

Topor

1995

Ann Math Artif Intell

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We consider the problem of repeatedly evaluating the same (computationally expensive) query to a database that is being updated between successive query requests. In this situation, it should be possible to use the difference between successive database states and the answer to the query in one state to reduce the cost of evaluating the query in the next state. We use nonrecursive Datalog (which are unions of conjunctive queries) to compute the differences, and call this process "incremental query evaluation using conjunctive queries."After formalizing the notion of incremental query evaluation using conjunctive queries, we give an algorithm that constructs, for each regular chain query (including transitive closure as a special case), a nonrecursive Datalog program to compute the difference between the answer after an update and the answer before the update. We then extend this result to weakly regular queries, which are regular chain programs augmented with conjunctive queries having the so-called cartesian-closed increment property, and to the case of unbounded-set insertions where the sets are binary cartesian products. Finally, we show that the class of conjunctive queries with the cartesian-closed increment property is decidable.

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Section: Semi-naive Evaluationmentioning

confidence: 99%

Section: Semi-naive Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonrecursive incremental evaluation of Datalog queries

Dong

Topor

1995

Ann Math Artif Intell

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“…Many previous approaches [3,6,7,8,12,13,14,15] consider that the information of a Data Warehouse System consists of a set of materialized views that store information from one or more databases that the organization uses in its operational systems. The process of loading this information is usually done using daily batch processes at night in order to avoid the slowdown of the operation systems.…”

Section: Introductionmentioning

confidence: 99%

Multi-source Materialized Views Maintenance: Multi-level Views

Silva

Belenguer

Celma

2006

Advances in Databases and Information Systems

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Abstract. In many information systems, the databases that make up the system are distributed in different modules or branch offices according to the requirements of the business enterprise. In these systems, it is often necessary to combine the information of all the organization's databases in order to perform analysis and make decisions about the global operation. This is the case of Data Warehouse Systems. From a conceptual point of view, a Data Warehouse can be considered as a set of materialized views which are defined in terms of the tables stored in one or more databases. These materialized views store historical data that must be maintained in either real time or periodically by means of batch processes. During the maintenance process the systems must perform selections, projections, joins, etc. that can affect several databases. This is a complex problem since making a join among several tables requires (at least temporarily) having the information from these tables in the same place. This requires the Data Warehouse to store auxiliary materialized views that in many cases contain duplicated information. In this article, we study this problem, and we propose a method that minimizes the duplicated information in the auxiliary materialized views and also reduces the response time of the system.

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Incremental evaluation of Datalog queries

Dong

Topor

1992

Database Theory — ICDT '92

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On the efficient computation of the difference between consecutive database states

Cited by 54 publications

References 21 publications

Nonrecursive incremental evaluation of Datalog queries

Nonrecursive incremental evaluation of Datalog queries

Multi-source Materialized Views Maintenance: Multi-level Views

Incremental evaluation of Datalog queries

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