Centre of Applied Software Engineering (CASE)
The Centre of Applied Software Engineering (CASE) was founded in the autumn of 2001 by Prof. Giancarlo Succi, with the objective of promoting excellence in research and collaboration between the industry and the University in the area of Applied Software Engineering.
The mission of CASE is to excel in research in Applied Software Engineering, bridging the world of academia and industry and supplying a unique learning environment for undergraduate, graduate, and doctoral students.
The key research areas of CASE are: Agile Methodologies and Lean Management; Open Source Development; Experimental Software Engineering and Software Engineering Knowledge Bases; Distance Learning in Software Engineering; Software Quality; Software Product Lines; Software Reuse and Component Based Development; Software Metrics; Development of Service Oriented Systems.
The operational strategy of CASE is:
Forming partnerships with local, national and international research and development institutions in the area of applied software engineering.Creating a cooperative environment to transfer the know-how and advanced technologies to local industry through consulting.
Participating in National, European, and International research projects.
Educating future Software Engineering researchers and professionals.
-> Go to CASE Web Site
-> Go to CASE Publications
Centre for Information and Database Systems Engineering (IDSE)
The past century witnessed huge efforts to manage an ever-increasing amount of data. Most prominently the invention of the Internet has led to an explosion of the amount of available data. Hard- and software alike have progressed at a tremendous pace to successfully cope with masses of data never seen before.
Ultimately, data is collected to extract information from it. Information science is the discipline that covers all aspects of extracting information from data. In contrast to the successes in data management the advances in information science have been less striking. Often, applications and end users are left with a plethora of tools to process and manage data but without support to extract information from this data. We design, develop, and evaluate new data analysis techniques to extract information from large, changing, and possibly volatile data repositories.
A crucial aspect of all real-world data is the ubiquitous presence of time. Financial data, whether information, medical histories, etc. alike change over time. Data models and database schemas have to be adapted to new requirements and hence have a dynamic behaviour as well. Dealing with time-varying information at various levels introduces a new dimension of complexity. In the past decades various formalisms and methods for the storage of and reasoning about time-varying information have been developed. We work on scalable solutions that provide a solid basis to deploy these techniques in the context of real-world applications.
In the Centre for Information and Database Systems Engineering (IDSE), we progress and extend today's data management technologies to actively support the extraction of information from massive data sets. We leverage temporal reasoning formalisms to temporal database and information systems. All research problems investigated in IDSE originate from real-world applications (e.g., e-government and bioinformatics) and are validated with real-world data.
-> Go to IDSE Web Site
-> Go to IDSE Publications
KRDB research centre for Knowledge and Data (KRDB)
In recent years, knowledge and data base applications have progressively converged towards integrated technologies which try to overcome the limits of each single discipline. Research in Knowledge Representation (KR) originally concentrated around logic-based formalisms that are typically tuned to deal with relatively small knowledge bases, but provide powerful deduction services, and the language to structure information is highly expressive. For example, research on formal languages for ontologies was originated from KR, as well as research in computational semantics for natural language. In contrast, Information Systems and Database (DB) research mainly dealt with efficient storage and retrieval of powerful query languages, and with sharing and displaying large amounts of (multimedia) documents. However, data representations were relatively simple and flat, and reasoning over the structure and the content of the documents played only a minor role.
This distinction between the requirements in Knowledge Representation and Databases is vanishing rapidly. On the one hand, to be useful in realistic applications, a modern KR system must be able to handle large data sets, and to provide expressive query languages. This suggests that techniques developed in the DB area could be useful for KR systems. On the other hand, the information stored on the web, in digital libraries, and in data warehouses is now very complex and with deep semantic structures, thus requiring more intelligent modelling languages and methodologies, and reasoning services on those complex representations to support design, management, flexible access, and integration. Therefore, a great call for an integrated logic-based view of Knowledge Representation and Database technologies is emerging. KRDB technologies offer promising formalisms for solving several problems concerning Conceptual Data Modelling and Ontology Design, Intelligent Information Access and Query processing, Information Integration, Peer to Peer systems, Semistructured Data, Distributed and Web Information Systems, E-services, Computational Logic, Logic-based Computational Linguistics, and Bio-informatics.
The KRDB Research Centre at the Faculty of Computer Science of the Free University of Bozen-Bolzano was founded in 2002, and it aims to be an international centre of excellence in basic and applied research on KRDB technologies and to propose to selected enterprises innovative ideas and technologies based on the research developed in the centre.
-> Go to KRDB Web Site
-> Go to KRDB Publications