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Multimedia retrieval is an essential part of today's world. This situation is observable in industrial domains, e.g., medical imaging, as well as in the private sector, visible by activities in manifold Social Media platforms. This trend led to the creation of a huge environment of multimedia information retrieval services offering multimedia resources for almost any user requests. Indeed, the encompassed data is in general retrievable by (proprietary) APIs and query languages, but unfortunately a unified access is not given due to arising interoperability issues between those services. In this regard, this thesis focuses on two application scenarios, namely a medical retrieval system supporting a radiologist's workflow, as well as an interoperable image retrieval service interconnecting diverse data silos. The scientific contribution of this dissertation is split in three different parts: the first part of this thesis improves the metadata interoperability issue. Here, major contributions to a community-driven, international standardization have been proposed leading to the specification of an API and ontology to enable a unified annotation and retrieval of media resources. The second part issues a metasearch engine especially designed for unified retrieval in distributed and heterogeneous multimedia retrieval environments. This metasearch engine is capable of being operated in a federated as well as autonomous manner inside the aforementioned application scenarios. The remaining third part ensures an efficient retrieval due to the integration of optimization techniques for multimedia retrieval in the overall query execution process of the metasearch engine.
In this work we present novel query evaluation techniques for data integration systems in different environments, ranging from a central data-warehouse approach, over distributed virtual market places, to peer-to-peer (P2P) systems. Based on a new distributed evaluation technique, the so-called HyperQueries, we present a reference architecture for distributed virtual market places. These HyperQueries enable us to dynamically construct query evaluation plans by referencing sub-plans in the Internet. Furthermore, the process of data integration is structured. Subsequently, we investigate P2P data integration systems without central instances. We introduce so-called Super-Peers which structure a P2P network. Using this Super-Peer based network we "unroll" queries. This allows us to execute even user-defined operators nearby the data sources. Finally, we propose novel, efficient join algorithms for decision support queries in central data-warehouse systems. The proposed order-preserving hashjoins and generalized hashteams are based on early sorting and early partitioning of the inputs and can speed up the query evaluation up to orders of magnitutes.