In cooperative information systems, the quality of data exchanged and provided by different data sources is extremely important. A lack of attention to data quality can imply data of low quality to spread all over the cooperative system. At the same time, improvement can be based on comparing data, correcting them and thus disseminating high quality data. In this paper, we present an architecture for managing data quality in cooperative information systems, by focusing on two specific modules, the Data Quality Broker and the Quality Notification Service. The Data Quality Broker allows for querying and improving data quality values. The Quality Notification Service is specifically targeted to the dissemination of changes on data quality values.

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The recent success of distributed and dynamic infrastructures for knowledge sharing has raised the need for semiautomatic/automatic ontology evolution strategies. Ontology evolution is generally defined as the timely adaptation of an ontology to changing requirements and the consistent propagation of changes to dependent artifacts. In this article, we present an ontology evolution approach in the context of multimedia interpretation. Ontology evolution in this context relies on the results obtained through reasoning for the interpretation of multimedia resources, through population of the ontology with new individuals, or through enrichment of the ontology with new concepts and new semantic relations. The article analyses the results of interpretation, population and enrichment obtained in evaluation experiments in terms of measures such as precision and recall. The evaluation reveals encouraging results.

Abstract. In cooperative information systems, the quality of data exchanged and provided by different data sources is extremely important. A lack of attention to data quality can imply data of low quality to spread all over the cooperative system. At the same time, improvement can be based on comparing data, correcting them and disseminating high quality data. In this paper, a framework and a related architecture for managing data quality in cooperative information systems is proposed, as developed in the context of the DaQuinCIS research project. Then the focus concentrates (i) on an XML-based model for data and quality data, and (ii) on the design of a broker, which selects the best available data from different sources; such a broker also supports the improvement of data based on feedbacks to data sources. The broker is the basic component of the DaQuinCIS architecture. 1

Abstract. Solving conflicts between overlapping databases requires an understanding of the reasons that lead to the inconsistencies. Provided that conflicts do not occur randomly but follow certain regularities, patterns in the form of ”If condition Then conflict ” provide a valuable means to facilitate their understanding. In previous work, we adopt existing association rule mining algorithms to identify such patterns. Within this paper we discuss extensions to our initial approach aimed at identifying possible update operations that caused the conflicts between the databases. This is done by restricting the items used for pattern mining. We further propose a classification of patterns based on mappings between the contradicting values to represent special cases of conflict generating updates. 1 Conflicts in Overlapping Databases Many databases exist with overlaps in their sets of represented real-world entities. There are different reasons for these overlaps, like: replication of data sources at different sites to improve the performance of web-services and the availability of the data,

A recent National Research Council study found that: “Although there are many private and public databases that contain information potentially relevant to counter terrorism programs, they lack the necessary context definitions (i.e., metadata) and access tools to enable interoperation with other databases and the extraction of meaningful and timely information ” [NRC02, p.304, emphasis added] That sentence succinctly describes the objectives of this project. Improved access and use of information are essential to better identify and anticipate threats, protect against and respond to threats, and enhance national and homeland security (NHS), as well as other national priority areas, such as Economic Prosperity and a Vibrant Civil Society (ECS) and Advances in Science and Engineering (ASE). This project focuses on the creation and contributions of a Laboratory for Information Globalization and Harmonization Technologies (LIGHT) with two interrelated goals: (1) Theory and Technologies: To research, design, develop, test, and implement theory and technologies for improving the reliability, quality, and responsiveness of automated mechanisms for reasoning and resolving semantic differences that hinder the rapid and effective integration (int) of systems and data (dmc) across multiple autonomous sources, and the use of that information by public and private agencies involved in national and homeland security and the other national priority areas involving complex and interdependent social systems (soc).