The success of organizations or business networks depends on fast and well-founded decisions taken by the relevant people in their specific area of responsibility. To enable timely and well-founded decisions, it is often necessary to perform ad-hoc analyses in a collaborative manner involving domain experts, line-of-business managers, key suppliers or customers. Current Business Intelligence (BI) solutions fail to meet the challenges of ad-hoc and collaborative decision support, slowing down and hurting organizations.The main goal of our envisioned system, which will be designed and implemented in a future research project, is to realize a highly scalable and flexible platform for collaborative, ad-hoc BI over large data sets. This will be achieved by developing methodologies, concepts and an infrastructure to enable an information self-service for business users and collaborative decision making over high-volume data sources within and across organizations.

A recurring manual task in data integration, ontology alignment or model management is finding mappings between complex meta data structures. In order to reduce the manual effort, many matching algorithms for semi-automatically computing mappings were introduced. Unfortunately, current matching systems severely lack performance when matching large schemas. Recently, some systems tried to tackle the performance problem within individual matching approaches. However, none of them developed solutions on the level of matching processes. In this paper we introduce a novel rewrite-based optimization technique that is generally applicable to different types of matching processes. We introduce filter-based rewrite rules similar to predicate push-down in query optimization. In addition we introduce a modeling tool and recommendation system for rewriting matching processes. Our evaluation on matching large web service message types shows significant performance improvements without losing the quality of automatically computed results.

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Today, a lot of work is dedicated to the development of systems that store, retrieve, or transport continuous-media data. Much less, however, is investigated to make these systems (i.e. media servers) interoperable, so that they can be used in cooperation with other systems in a single application. In particular, the cooperation of media servers and traditional database management systems (DBMS) would be useful for several reasons: (a) traditional DBMS are in widespread use already and serve very efficiently in the management of structured data, (b) media servers are in use already, too, and they provide efficient support for media data management, and (c) there is a trend to use media data as an add-on to existing information systems that are based on traditional DBMS. An integration of media-data management and structured-data management in one system seems to be a very tedious task and may in fact never work out, since the two types of data are very different and require special treatment, including internal aspect such as buffer management, transaction management, access paths, and so on. Hence, a federation of the two types of data managers seems to be a better way. It is the idea of the thesis, which is presented shortly in this paper, to investigate the various aspects and implications of such a specific kind of federation.