PQR: Predicting Query Execution Times for Autonomous Workload Management
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
ARIA: automatic resource inference and allocation for mapreduce environments
Proceedings of the 8th ACM international conference on Autonomic computing
Better drilling through sensor analytics: a case study in live operational intelligence
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Optimizing analytic data flows for multiple execution engines
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
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Modern data analytic flows involve complex data computations that may span multiple execution engines and need to be optimized for a variety of objectives like performance, fault-tolerance, freshness, and so on. In this paper, we present optimization techniques and tradeoffs in terms of a real-world, cyber-physical flow that starts with raw time series sensor data and external event data, and through a series of analytic operations produces automated actions and actionable insights.