34: Transactions On Large-scale Data- And Knowledge-centered Systems Xxxiv: Special Issue On Consistency And Inconsistency In Data-centric Applications (lecture Notes In Computer Science)
by Abdelkader Hameurlain /
2017 / English / PDF
6.8 MB Download
This volume, the 34th issue of Transactions on Large-Scale Data-
and Knowledge-Centered Systems, constitutes a special issue
consisting of seven papers on the subject of Consistency and
Inconsistency in Data-Centric Applications. The volume opens with
an invited article on basic postulates for inconsistency measures.
Three of the remaining six papers are revised, extended versions of
papers presented at the First International Workshop on Consistency
and Inconsistency, COIN 2016, held in conjunction with DEXA 2016 in
Porto, Portugal, in September 2016. The other three papers were
selected from submissions to a call for contributions to this
edition. Each of the papers highlights a particular subtopic.
However, all are concerned with logical inconsistencies that are
either to be systematically avoided, or reasoned with consistently,
i.e., without running the danger of an explosion of inferences.
This volume, the 34th issue of Transactions on Large-Scale Data-
and Knowledge-Centered Systems, constitutes a special issue
consisting of seven papers on the subject of Consistency and
Inconsistency in Data-Centric Applications. The volume opens with
an invited article on basic postulates for inconsistency measures.
Three of the remaining six papers are revised, extended versions of
papers presented at the First International Workshop on Consistency
and Inconsistency, COIN 2016, held in conjunction with DEXA 2016 in
Porto, Portugal, in September 2016. The other three papers were
selected from submissions to a call for contributions to this
edition. Each of the papers highlights a particular subtopic.
However, all are concerned with logical inconsistencies that are
either to be systematically avoided, or reasoned with consistently,
i.e., without running the danger of an explosion of inferences.