The HiPAC project: combining active databases and timing constraints
ACM SIGMOD Record - Special Issue on Real-Time Database Systems
Parallel database systems: the future of high performance database systems
Communications of the ACM
Principles of distributed database systems (2nd ed.)
Principles of distributed database systems (2nd ed.)
Intensive Data Management in Parallel Systems: A Survey
Distributed and Parallel Databases
Control Versus Data Flow in Parallel Database Machines
IEEE Transactions on Parallel and Distributed Systems
Hybrid-Range Partitioning Strategy: A New Declustering Strategy for Multiprocessor Database Machines
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
Parallel Event Detection in Active Database Systems: The Heart of the Matter
ARTDB '97 Proceedings of the Second International Workshop on Active, Real-Time, and Temporal Database Systems
Issues in data stream management
ACM SIGMOD Record
Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Nile: A Query Processing Engine for Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
SnoopIB: interval-based event specification and detection for active databases
Data & Knowledge Engineering
AQuery: query language for ordered data, optimization techniques, and experiments
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Query languages and data models for database sequences and data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Query-aware partitioning for monitoring massive network data streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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Advanced applications of sensors, network traffic, and financial markets have produced massive, continuous, and time-ordered data streams, calling for high-performance stream querying and event detection techniques. Beyond the widely adopted sequence operator in current data stream management systems, as well as inspired by the great work developed in temporal logic and active database fields, this paper presents a rich set of temporal operators on events, with an emphasis on the temporal properties and relative temporal relationships of events. We outline three temporal operators on unary events (Within,Last, and Periodic), and four ones on binary events (Concur, Sequence, Overlap and During). We employ two stream partitioning strategies, i.e., timedriven and task-driven, for parallel processing of the temporal operators. Our analysis and experimental results with both synthetic and real-data show that the better partitioning scheme in terms of system throughput is the one which can produce balanced data workload and less data duplication among the processing nodes.