Andere Kunden interessierten sich auch für
![Low Latency, High Performance Data Stream Processing]( "Low Latency, High Performance Data Stream Processing")
Low Latency, High Performance Data Stream Processing38,99 €![Kafka Streams in Action]( "Kafka Streams in Action")
Kafka Streams in Action35,99 €![Decision Tree Algorithms For Concept Drifted Data Stream]( "Decision Tree Algorithms For Concept Drifted Data Stream")
Decision Tree Algorithms For Concept Drifted Data Stream45,99 €![Congestion Aware Routing Algorithm For Data Center Communication]( "Congestion Aware Routing Algorithm For Data Center Communication")
Congestion Aware Routing Algorithm For Data Center Communication42,99 €![Cost Aware Genetic Optimization for Task Allocation in Cloud IaaS]( "Cost Aware Genetic Optimization for Task Allocation in Cloud IaaS")
Cost Aware Genetic Optimization for Task Allocation in Cloud IaaS23,99 €![Satellite Network Robust QoS-aware Routing]( "Satellite Network Robust QoS-aware Routing")
Satellite Network Robust QoS-aware Routing74,99 €![Quality-aware Scheduling for Key-value Data Stores]( "Quality-aware Scheduling for Key-value Data Stores")
Quality-aware Scheduling for Key-value Data Stores37,99 €
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.