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Stream-based data management enables the efficient
analysis and processing of large volumes of data in
distributed environments. This book presents
network-aware optimization techniques which allow an
effective resource usage considering computational
load and network bandwidth in a distributed data
stream management system. The data stream sharing
approach is thereby based on distributing query
processing in the network and on sharing preprocessed
data streams for satisfying multiple similar queries.
To increase the possibilities for sharing, the
extended approach of data stream widening is able to
alter existing streams to additionally contain all
the necessary data for a new query. Since data stream
widening requires the treatment of disjunctive
predicates, this book further describes methods for
matching and evaluating such predicates. The book is
thus a suitable source of information for computer
scientists and engineers interested in the optimized
processing, evaluation, and management of distributed
data streams.
analysis and processing of large volumes of data in
distributed environments. This book presents
network-aware optimization techniques which allow an
effective resource usage considering computational
load and network bandwidth in a distributed data
stream management system. The data stream sharing
approach is thereby based on distributing query
processing in the network and on sharing preprocessed
data streams for satisfying multiple similar queries.
To increase the possibilities for sharing, the
extended approach of data stream widening is able to
alter existing streams to additionally contain all
the necessary data for a new query. Since data stream
widening requires the treatment of disjunctive
predicates, this book further describes methods for
matching and evaluating such predicates. The book is
thus a suitable source of information for computer
scientists and engineers interested in the optimized
processing, evaluation, and management of distributed
data streams.