Introduction Handling large collections of digitized multimedia data, usually referred to as multimedia digital libraries, is a major challenge for information technology. The Mirror DBMS is a research database system that is developed to better understand the kind of data management that is required in the context of multimedia digital libraries (see also URL http://www.cs.utwente.nl/arjen/mmdb.html). Its main features are an integrated approach to both content management and (traditional) structured data management, and the implementation of an extensible objectoriented logical data model on a binary relational physical data model. The focus of this work is aimed at design for scalability. 2 Query processing The query facilities of the Mirror DBMS rely on the Moa Object Algebra [BWK98]. Moa constitutes an object data model and query algebra, designed to be used at the logical level of a DBMS. It

The mi R Ror project investigates the implications of multimedia information retrieval on database design. We assume a modern extensible database system with extensions for feature based search techniques. The multimedia query processor has to bridge the gap between the user's high level information need and the search techniques available in the database. We therefore propose an iterative query process using relevance feedback. The query processor identifies which of the available representations are most promising for answering the query. In addition, it combines evidence from di#erent sources. Our multimedia retrieval model is a generalization of a well-known text retrieval model. We discuss our prototype implementation of this model, based on Bayesian reasoning over a concept space of automatically generated clusters. The experimentation platform uses structural object-orientation to model the data and its meta-data flexibly, without compromising e#ciency and scalability. We illust...

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In this report the development of a specialised search engine for a digital library is described. The proposed system architecture consists of three levels: the conceptual, the logical and the physical level. The conceptual level schema enables by its exposure of a domain specific schema semantically rich conceptual search. The logical level provides a description language to achieve a high degree of flexibility for multimedia retrieval. The physical level takes care of scalable and e#cient persistent data storage. The role, played by each level, changes during the various stages of a search engine's lifecycle: (1) modeling the index, (2) populating and maintaining the index and (3) querying the index. The integration of all this functionality allows the combination of both conceptual and content-based querying in the query stage. A search engine for the Australian Open tennis tournament website is used as a running example, which shows the power of the complete architecture and its various components.

The Cambridge Research Laboratory was founded in 1987 to advance the state of the art in both core computing and human-computer interaction, and to use the knowledge so gained to support the Company’s corporate objectives. We believe this is best accomplished through interconnected pursuits in technology creation, advanced systems engineering, and business development. We are actively investigating scalable computing; mobile computing; vision-based human and scene sensing; speech interaction; computer-animated synthetic persona; intelligent information appliances; and the capture, coding, storage, indexing, retrieval, decoding, and rendering of multimedia data. We recognize and embrace a technology creation model which is characterized by three major phases: Freedom: The life blood of the Laboratory comes from the observations and imaginations of our research staff. It is here that challenging research problems are uncovered (through discussions with customers, through interactions with others in the Corporation, through other professional interactions, through reading, and the like) or that new ideas are born. For any such problem or idea, this phase culminates in the nucleation of a project team around a well articulated central research question and the outlining of a research plan. Focus: Once a team is formed, we aggressively pursue the creation of new technology based on

Abstract. StreetTiVo is a project that aims at bringing research results into the living room; in particular, a mix of current results in the areas of Peer-to-Peer XML Database Management System (P2P XDBMS), advanced multimedia analysis techniques, and advanced information retrieval techniques. The project develops a plug-in application for the so-called Home Theatre PCs, such as set-top boxes with MythTV or Windows Media Center Edition installed, that can be considered as programmable digital video recorders. StreetTiVo distributes computeintensive multimedia analysis tasks over multiple peers (i.e., StreetTiVo users) that have recorded the same TV program, such that a user can search in the content of a recorded TV program shortly after its broadcasting; i.e., it enables near real-time availability of the meta-data (e.g., speech recognition) required for searching the recorded content. Street-TiVo relies on our P2P XDBMS technology, which in turn is based on a DHT overlay network, for distributed collaborator discovery, work coordination and meta-data exchange in a volatile WAN environment. The technologies of video analysis and information retrieval are seamlessly integrated into the system as XQuery functions. 1

Moa and the multi-model architecture:

Advanced non-traditional application domains such as geographic information sys-tems and digital library systems demand advanced data management support. In an effort to cope with this demand, we present a novel multi-model DBMS architecture which provides efficient evaluation of queries on complexly structured data. A vital role in this architecture is played by the Moa language featuring a nested relational data model based on XNF 2, in which we placed renewed interest. Furthermore, the architecture allows extensibility on all of its levels providing the means to better inte-grate domain-specific algorithms into the system. In addition to this, the extensibility of the Moa language is designed in a way that optimization obstacles due to black-box treatment of ADTs is avoided. This combination of well-integrated domain-specific algorithms, extensibility open to optimization, and a mapping of queries on complexly structured data to an efficient physical algebra expression via a nested re-lational algebra, makes that the Moa system can efficiently handle complex queries from non-traditional application domains.