Nicola Leone is the Project Coordinator of the Infomix Project.
Information integration is the task of providing a uniform interface to various pre-existing data sources, so as to enable users to focus on specifying what they want, rather than thinking of how to obtain the answer. As a result, an information integration system frees the users from the tasks of finding the relevant data sources, interacting with each source in isolation, and selecting, cleaning, and combining data from multiple sources. The proposed project aims at providing a solid theory and innovative methodologies for future flexible information integration systems. In particular, advanced reasoning capabilities by means of computational logic, handling of semi-structured data, declarative user interaction, possibilities to deal with incomplete and inconsistent data sources, extendibility and scalability are important and yet open issues, which will be tackled and resolved in this project. In addition, a prototype system will be developed in order to assess the validity and usability of our approach. We point out that especially the scalability desideratum is challenging and bears high risk since comparable techniques are currently insufficient in this respect, so it is unclear whether this requirement can be met. However, upon success the benefits will be ample: Not only will companies, organizations, and other enterprises be able to integrate their internal data at high level of competence and in an efficient manner; also inter-organizational, and large scale distributed information systems will become feasible.
The main goal of the INFOMIX project is to provide a set of techniques and associated tools for powerful information integration by using advanced reasoning capabilities. In a nutshell, we aim at developing a theory, comprising a comprehensive information model and information integration algorithms, and a prototype implementation of a knowledge-based system for advanced information integration, by using computational logic and integrating research results on data acquisition and transformation.
The ICONS project focuses on bringing together into a coherent, web-based system architecture the advanced research results, technologies, and standards, in order to develop and further exploit the knowledge-based, multimedia content management platform. Integrating and extending known results from the AI and database management fields, combined with advanced features of the emerging information architecture technologies, will result in a novel Intelligent CONtent Management System (ICONS) platform.
Three clearly defined research streams, controlled by precisely specified milestones and sound project management practices, as well as top quality academic and industrial partners working jointly towards achieving well specified project objectives ensure the success of the ICONS project.
Results of the ICONS project fall into three distinct project deliverable categories. The original research results will be presented in refereed scientific publications. The technological achievements will be presented in the form of a stable working ICONS prototype that will be presented and evaluated over the Internet. Feasibility and added value of the novel technologies will be demonstrated by the Structural Fund Project Knowledge Portal.
WASP is funded by the European commission, FET ("Forward Emerging Technologies") initiative under contract IST-FET-2001-37004 from September 15 2002 to March 15 2005.
In computational Logic, the new paradigm of Answer Set Programming is promising to help the specification and solution of problems that need both reasoning by cases and reasoning under incomplete information. This working group will enable a systematic exchange of information among the implementation teams and test users and foster synergies in research efforts.
In ASP, problem solving is achieved through evaluation of declarative problem specification. The strength and distinguishing feature of ASP is its advanced capability of dealing with incomplete information and defaults. This is crucial for applications which rely on decision making under missing data.
While ASP has proved a valuable vehicle for solving problems which require knowledge representation capabilities, the technology is still in an early stage. In order to develop ASP and, eventually, provide it as a technology that can be used for applications of complexity approaching that needed in industry, considerable further research efforts are needed, which exceed the capabilities and resources of a single small sized research group.
The main objectives of WASP are:
The project plans to design, verify, implement through adequate enabling technologies and validate a comprehensive model for the valorisation of the European Cultural Heritage by leveraging sustainable innovation and by exploiting the opportunities offered by the so-called "new economy" with its rapid shift towards the accessibility of user-driven cultural services and "experiential" entertainment values.
Gli sviluppi dell'informatica e delle telecomunicazioni hanno reso disponibile l'accesso ad un numero sempre piu' vasto di banche dati strutturate e semistrutturate, create in tempi diversi, su sistemi diversi e con criteri organizzativi diversi. Senza l'applicazione di opportuni metodi, gli utenti hanno a disposizione grandi quantita' di dati, ma trovano inevitabili difficolta' nel sintetizzare l'informazione utile ai propri scopi. Risulta quindi importante ricercare nuove metodologie per l'integrazione di sorgenti eterogenee di dati, per il progetto di basi di dati destinate all'analisi in linea di dati di sintesi (data warehouse), e per la scoperta di nuovi collegamenti e proprieta' non facilmente intuibili all'interno di una sorgente o di sorgenti diversi (data mining). L'obiettivo del progetto e' la definizione di un quadro metodologico generale per l'integrazione, il warehousing e il mining di sorgenti eterogenee (D2I: From Data to Information), e lo sviluppo di metodi e strumenti specifici per i tre temi:
Tema 1: integrazione di dati provenienti da sorgenti eterogenee
Tema 2: progettazione e interrogazione di data warehouse
Tema 3: data mining.