In this paper, we first identify semantic heterogeneities that, when
not resolved, often cause serious data quality problems. We discuss
the especially challenging problems of temporal and aggregational
ontological heterogeneity, which concerns how complex entities and
their relationships are aggregated and reinterpreted over time. Then
we illustrate how the COntext INterchange (COIN) technology can be
used to capture data semantics and reconcile semantic heterogeneities
in a scalable manner, thereby improving data quality.
%0 Report
%1 zhu2006
%A Zhu, Hongwei
%A Madnick, Stuart E.
%D 2006
%K Aggregation, Context Data Heterogeneity, Ontology, Semantic Semantics, Temporal,
%T Addressing the Challenges of Aggregational and Temporal Ontological
Heterogeneity
%U http://hdl.handle.net/1721.1/30208
%X In this paper, we first identify semantic heterogeneities that, when
not resolved, often cause serious data quality problems. We discuss
the especially challenging problems of temporal and aggregational
ontological heterogeneity, which concerns how complex entities and
their relationships are aggregated and reinterpreted over time. Then
we illustrate how the COntext INterchange (COIN) technology can be
used to capture data semantics and reconcile semantic heterogeneities
in a scalable manner, thereby improving data quality.
@techreport{zhu2006,
abstract = {In this paper, we first identify semantic heterogeneities that, when
not resolved, often cause serious data quality problems. We discuss
the especially challenging problems of temporal and aggregational
ontological heterogeneity, which concerns how complex entities and
their relationships are aggregated and reinterpreted over time. Then
we illustrate how the COntext INterchange (COIN) technology can be
used to capture data semantics and reconcile semantic heterogeneities
in a scalable manner, thereby improving data quality.},
added-at = {2007-05-04T05:48:10.000+0200},
author = {Zhu, Hongwei and Madnick, Stuart E.},
biburl = {https://www.bibsonomy.org/bibtex/2aa6d7ac3cf1109b107d97a562a8d3bfc/p_ansell},
description = {Context-aware business processes},
institution = {MIT Sloan School of Management},
interhash = {eb00573a632939f985d45e98a50ee673},
intrahash = {aa6d7ac3cf1109b107d97a562a8d3bfc},
keywords = {Aggregation, Context Data Heterogeneity, Ontology, Semantic Semantics, Temporal,},
owner = {peter},
pdf = {HonoursResearch/Zhu2006-AddressingTheChallengesOfAggregationalAndTemporalOntologicalHeterogeneity.pdf},
timestamp = {2007-05-04T05:48:14.000+0200},
title = {Addressing the Challenges of Aggregational and Temporal Ontological
Heterogeneity},
url = {http://hdl.handle.net/1721.1/30208},
year = 2006
}