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Awareness of data and information quality issues has grown rapidly in light of the critical role played by the quality of information in our data-intensive, knowledge-based economy. Research in the past two decades has produced a large body of data quality knowledge and has expanded our ability to solve many data and information quality problems. In this(More)
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 heterogeneities that, when not resolved, often cause data quality(More)
Technological advances such as Service Oriented Architecture (SOA) have increased the feasibility and importance of effectively integrating information from an ever widening number of systems within and across enterprises. A key difficulty of achieving this goal comes from the pervasive heterogeneity in all levels of information systems. A robust solution(More)
Web services have become the technology of choice for service-oriented computing implementation, where Web services can be composed in response to some users’ needs. It is critical to verify the compatibility of component Web services to ensure the correctness of the whole composition in which these components participate. Traditionally, two conditions need(More)
The availability of data on the Web and new data extraction technologies have made it increasingly easy to reuse existing data to create new databases and provide value-added services. Meanwhile, database creators have been seeking legal protection for their data, such as the European Union’s Database Directive. The legislative development shows that there(More)
Awareness of data and information quality issues has grown rapidly in light of the critical role played by the quality of information in our data-intensive, knowledge-based economy. Research in the past two decades has produced a large body of data quality knowledge and has expanded our ability to solve many data and information quality problems. We present(More)
The change in meaning of data over time poses significant challenges for the use of that data. These challenges exist in the use of an individual data source and are further compounded with the integration of multiple sources. In this paper, we identify three types of temporal semantic heterogeneity. We propose a solution based on extensions to the Context(More)
Many online services access a large number of autonomous data sources and at the same time need to meet different user requirements. It is essential for these services to achieve semantic interoperability among these information exchange entities. In the presence of an increasing number of proprietary business processes, heterogeneous data standards, and(More)
Information aggregation, a service that collects relevant information from multiple sources, has emerged to help individuals and businesses to effectively use the growing amount of information on the Web. In this paper, we analyzed a number of characteristics of information aggregation, namely comparison, relationship, and intra-organization aggregation.(More)