Cong, Gao

Fan, Wenfei

Geerts, Floris

Jia, Xibei

Ma, Shuai

English

This paper revisits the analysis of annotation propagation from source databases to views defined in terms of conjunctive (SPJ) queries. Given a source database D, an SPJ query Q, the view Q(D) and a tuple ΔV in the view, the view (resp. source) side-effect problem is to find a minimal set ΔD of tuples such that the deletion of ΔD from D results in the deletion of ΔV from Q(D) while minimizing the side effects on the view (resp. the source). A third problem, referred to as the annotation placement problem, is to find a single base tuple ΔD such that annotation in a field of ΔD propagates to ΔV while minimizing the propagation to other fields in the view Q(D). These are important for data provenance and the management of view updates. However important, these problems are unfortunately NP-hard for most subclasses of SPJ views [5].To make the annotation propagation analysis feasible in practice, we propose a key preserving condition on SPJ views, which requires that the projection fields of an SPJ view Q retain a key of each base relation involved in Q. While this condition is less restrictive than other proposals [11, 14], it often simplifies the annotation propagation analysis. Indeed, for key-preserving SPJ views the annotation placement problem coincides with the view side-effect problem, and the view and source side-effect problems become tractable. In addition we generalize the setting of [5] by allowing ΔV to be a group of tuples to be deleted, and investigate the insertion of tuples to the view. We show that group updates make the analysis harder: these problems become NP-hard for several subclasses of SPJ views. We also show that for SPJ views the source and view side-effect problems are NP-hard for single-tuple insertion, but are tractable for some subclasses of SPJ for group insertions, in the presence or in the absence of the key preservation condition.<br/

Edinburgh Research Explorer

A comparison of stringdistance metrics for name-matching tasks. In IIWeb,

A cost-based model and effective heuristic for repairing constraints by value modiﬁcation.

A systematic approach to automatic edit and imputation.

AJAX: An extensible data cleaning tool.

ARKTOS: towards the modeling, design, control and execution of ETL processes.

Census data repair: a challenging application of disjunctive logic programming.

Chasing Constrained TupleGenerating Dependencies.

Complexity of consistent query answering in databases under cardinality-based and incremental repair semantics.

Computers and Intractability: A Guide to the Theory of NP-Completeness.

Condensed representation of database repairs for consistent query answering.

Conditional dependencies for horizontal decompositions.

Conditional functional dependencies for data cleaning.

Consistent query answers in inconsistent databases.

Constrained dependencies.

Constraint-Generating Dependencies.

Data cleaning: Problems and current approaches.

Data Mining: Concepts and Techniques”.

Declarative data cleaning: Language, model and algorithms.

DISTANCE-SAT: Complexity and algorithms.

Efﬁcient approximation algorithms for repairing inconsistent databases.

Errors detection and correction in large scale data collecting.

Greed is good: approximating independent sets in sparse and bounded-degree graphs.

Halld´ orsson. Approximating maximum independent sets by excluding subgraphs.

Incomplete information in relational databases.

Matching algorithm within a duplicate detection system.

Methods for evaluating and creating data quality.

Minimal-change integrity maintenance using tuple deletions.

Plain English on data quality: Information quality management: The next frontier. DM Review Magazine,

Improving Data Quality: Consistency and Accuracy

Abstract

Similar works

Full text

Available Versions

Edinburgh Research Explorer