Design and evaluation of a wide-area event notification service
ACM Transactions on Computer Systems (TOCS)
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
TelegraphCQ: continuous dataflow processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
ACM SIGMOD Record
Design, implementation, and evaluation of the linear road bnchmark on the stream processing core
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Event-Driven Architectures and Complex Event Processing
SCC '06 Proceedings of the IEEE International Conference on Services Computing
Concepts and models for typing events for event-based systems
Proceedings of the 2007 inaugural international conference on Distributed event-based systems
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
WhiteWater: Distributed Processing of Fast Streams
IEEE Transactions on Knowledge and Data Engineering
Web services and business process management
IBM Systems Journal
Automatic optimization of stream programs via source program operator graph transformations
Distributed and Parallel Databases
An application of sensor and streaming analytics to oil production
Proceedings of the 17th International Conference on Management of Data
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We report the experience of implementing event detection analytics to monitor and forewarn oil production failures in modern, digitized oil fields. Modern oil fields are equipped with thousands of sensors and gauges to measure various physical and chemical characteristics of oil and gas from underground reservoirs to distribution systems. Data from these massive sensor networks weave a picture depicting the state of oil production and potentially hinting at troubles ahead. Continuous streams of sensor readings can be tapped and fed into analytical algorithms in real time to estimate the likelihood of failure events and generate alerts for possible engineering actions. However, the large amount of main memory required to maintain algorithmic states on cumulative stream data poses challenges to today's web-centric, short-message oriented IT infrastructure. Familiar techniques such as data aggregation, selective sampling and window truncating cannot be applied to some sophisticated algorithms. The paper details our end-to-end solution, points out mismatches with the prevalent transactional web model and suggests new research directions.