MystiQ is a system that uses probabilistic query semantics [3] to find answers in large numbers of data sources of less than perfect quality. There are many reasons why the data originating from many different sources may be of poor quality, and therefore difficult to query: the same data item may have different representation in different sources; the schema alignments needed by a query system are imperfect and noisy; different sources may contain contradictory information, and, in particular, their combined data may violate some global integrity constraints; fuzzy matches between objects from different sources may return false positives or negatives. Even in such environment, users some-times want to ask complex, structurally rich queries, using query constructs typically found in SQL queries: joins, subqueries, existential/universal quantifiers, aggregate and group-by queries: for example scientists may use such queries to query multiple scientific data sources, or a law enforcement agency may use it in order to find rare associations from multiple data sources. If standard query semantics were applied to such queries, all but the most trivial queries will return an empty answer.

The paper discusses how the OLVP system, an innovative video-on-demand system, can be used to efficiently support video delivery applications over an asynchronous transfer mode (ATM) network. We show how the OLVP system provided a high quality of service (QoS) to video applications executing over a set of distributed and heterogeneous system resources. System architecture is described. Key functionality, including distributed video storage management, real-time admission control strategy and resource allocation routing mechanism, traffic management and routing algorithms (both in the ATM network and in the OLVP system) are investigated. To illustrate the success of our approach, we provide a performance evaluation based on simulations of the system.