19,071 research outputs found
Updating Recursive XML Views of Relations
This paper investigates the view update problem for XML views published from relational data. We consider XML views defined in terms of mappings directed by possibly recursive DTDs, compressed into DAGs and stored in relations. We provide new techniques to efficiently support XML view updates specified in terms of XPath expressions with recursion and complex filters. The interaction between XPath recursion and DAG compression of XML views makes the analysis of XML view updates rather intriguing. In addition, many issues are still open even for relational view updates, and need to be explored. In response to these, on the XML side, we revise the notion of side effects and update semantics based on the semantics of XML views, and present efficient algorithms to translate XML updates to relational view updates. On the relational side, we propose a mild condition on SPJ views, and show that under this condition the analysis of deletions on relational views becomes PTIME while the insertion analysis is NP-complete. We develop an efficient algorithm to process relational view deletions, and a heuristic algorithm to handle view insertions. Finally, we present an experimental study to verify the effectiveness of our techniques. 1
Incremental View Maintenance For Collection Programming
In the context of incremental view maintenance (IVM), delta query derivation
is an essential technique for speeding up the processing of large, dynamic
datasets. The goal is to generate delta queries that, given a small change in
the input, can update the materialized view more efficiently than via
recomputation. In this work we propose the first solution for the efficient
incrementalization of positive nested relational calculus (NRC+) on bags (with
integer multiplicities). More precisely, we model the cost of NRC+ operators
and classify queries as efficiently incrementalizable if their delta has a
strictly lower cost than full re-evaluation. Then, we identify IncNRC+; a large
fragment of NRC+ that is efficiently incrementalizable and we provide a
semantics-preserving translation that takes any NRC+ query to a collection of
IncNRC+ queries. Furthermore, we prove that incremental maintenance for NRC+ is
within the complexity class NC0 and we showcase how recursive IVM, a technique
that has provided significant speedups over traditional IVM in the case of flat
queries [25], can also be applied to IncNRC+.Comment: 24 pages (12 pages plus appendix
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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A unifying approach for queries and updates in deductive databases
This dissertation presents a unifying approach to process (recursive) queries and updates in a deductive database. To improve query performance, a combined top-down and bottom-up evaluation method is used to compile rules into iterative programs that contain relational algebra operators. This method is based on the lemma resolution that retains previous results to guarantee termination.Due to locality in database processing, it is desirable to materialize frequently used queries against views of the database. Unfortunately, if updates are allowed, maintaining materialized view tables becomes a major problem. We propose to materialize views incrementally, as queries are being answered. Hence views in our approach are only partially materialized. For such views, we design algorithms to perform updates only when the underlying view tables are actually affected.We compare our approach to two conventional methods for dealing with views: total materialization and query-modification. The first method materializes the entire view when it is defined while the second recomputes the view on the fly without maintaining any physical view tables. We demonstrate that our approach is a compromise between these two methods and performs better than either one in many situations.It is also desirable to be able to update views just like updating base tables. However, view updates are inherently ambiguous and the semantics of update propagation on recursively defined views were not well understood in the past. Using dynamic logic programming and lemma resolution, we are able to define the semantics of recursive view updates. These are expressed in the form of update translators specified by the database administrator when the view is defined. To guarantee completeness, we identify a subset of safe update translators. We prove that this subset of translators always terminate and are complete
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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
Using Ontologies for Semantic Data Integration
While big data analytics is considered as one of the most important paths to competitive advantage of today’s enterprises, data scientists spend a comparatively large amount of time in the data preparation and data integration phase of a big data project. This shows that data integration is still a major challenge in IT applications. Over the past two decades, the idea of using semantics for data integration has become increasingly crucial, and has received much attention in the AI, database, web, and data mining communities. Here, we focus on a specific paradigm for semantic data integration, called Ontology-Based Data Access (OBDA). The goal of this paper is to provide an overview of OBDA, pointing out both the techniques that are at the basis of the paradigm, and the main challenges that remain to be addressed
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