26,439 research outputs found
A Review of integrity constraint maintenance and view updating techniques
Two interrelated problems may arise when updating a database. On one
hand, when an update is applied to the database, integrity constraints
may become violated. In such case, the integrity constraint maintenance
approach tries to obtain additional updates to keep integrity
constraints satisfied. On the other hand, when updates of derived or
view facts are requested, a view updating mechanism must be applied to
translate the update request into correct updates of the underlying base
facts.
This survey reviews the research performed on integrity constraint
maintenance and view updating. It is proposed a general framework to
classify and to compare methods that tackle integrity constraint
maintenance and/or view updating. Then, we analyze some of these methods
in more detail to identify their actual contribution and the main
limitations they may present.Postprint (published version
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
MonetDB/XQuery: a fast XQuery processor powered by a relational engine
Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met
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