Data quality is crucial for operational efficiency and sound decision making. This paper focuses on believability, a major aspect of quality, measured along three dimensions: trustworthiness, reasonableness, and temporality. We ground ourapproach on provenance, i.e. the origin and subsequent...

Data quality is crucial for operational efficiency and sound decision making. This paper focuses on believability, a major aspect of data quality. The issue of believability is particularly relevant in the context of Web 2.0, where mashups facilitate the combination of data from different...

We use a context interchange mediation approach for detecting and resolving data quality and semantic integrity conflicts in information exchanged across organizational boundaries. Context models draw on a domain ontology to explain how source and receiver data models implement general...

Data quality issues have taken on increasing importance in recent years. In our research, we have discovered that many "data quality" problems are actually "data misinterpretation" problems - that is, problems with data semantics. In this paper, we first illustrate some examples of these...

Data quality issues have taken on increasing importance in recent years. In our research, we have discovered that many data quality problems are actually data misinterpretation problems - that is, problems caused by heterogeneous data semantics. In this paper, we first identify semantic...