We are active in the areas related to the management and analysis of data, metadata, and knowledge in general. The overall goal is to process simple and (often times) uncorrelated facts about individual entities, and organize those into a meaningful context that can significantly enhance the human capacity to take effective actions in varied and uncertain situations.
More specifically, we are interested in designing and developing management systems for different types of data (relational, XML, time series, streaming, sensor, business process data, and others). We explore techniques for efficiently storing, querying, and analyzing these data. Our research includes, but is not limited to, the development of novel representation, summarization, and processing techniques for static and continuous data, and the discovery of structure, correlations, or other patterns in large collections of data. This line of research addresses the needs of business, scientific, and operational environments for organizing, processing and mining huge amounts of data, both in an offline and an online (i.e., real-time) fashion.
We are studying the problems of data management in peer to-peer systems. In particular, we are exploring the applications of game theory in this environment, modeling the cooperation of nodes to work on answering queries, an extensive game, and studying conditions under which subgame perfect equilibria exist, and when they are optimal. Another direction of our research is working on problems related to information integration and metadata management. We propose a methodology for the efficient management of metadata, which makes use of existing infrastructures. We are building tools, based on rigorous foundations, for schema and ontology mapping, interoperability, data translation, information integration, data exchange, view updates, view maintenance, and meta-data management. These tools are designed to support the evolution of data, while requiring minimum human support.
We are working on knowledge representation and knowledge management, with a focus on how to achieve interoperability across multiple local representations. This approach enables the effective management of diversity in knowledge. By treating diversity as a feature (and not as a problem), we can devise algorithms and systems that support the user in the creation, acquisition, adaptation, evolution, and sharing of knowledge. At the foundational level, we work on the logics of context in knowledge representation and reasoning, and on the notion of identity in web-based knowledge representation. The results of this research are applied mainly in the area of the Semantic Web, where we are working on the development of a global Entity Name System and entity-centric applications in the areas of knowledge management, authoring, search.
Some of the research areas in which we are active are the following: data management and analysis, view maintenance, caching and prefetching, data mining,personalization technologies, sensor data management, streaming data summarization and processing, business process monitoring and analysis, metadata management, schema mapping, data translation and integration, knowledge representation and management, semantic web, contexts and ontologies, user-centric data and knowledge search, XML and P2P data and knowledge management, and game theory in P2P systems.
Our research finds applications in the areas of business intelligence and business process management, data warehousing, sensor networks, metadata management systems, social and semantic web, intranet information systems, and others.
|Paolo Bouquet||Fausto Giunchiglia||Gabriel Mark Kuper|
|Themis Palpanas||Yannis Velegrakis|
Further InformationResearch Program's Technical reports
Research Program's Published papers