Scalable online first-order monitoring
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Published:2021-04
Issue:2
Volume:23
Page:185-208
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ISSN:1433-2779
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Container-title:International Journal on Software Tools for Technology Transfer
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language:en
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Short-container-title:Int J Softw Tools Technol Transfer
Author:
Schneider JoshuaORCID, Basin DavidORCID, Brix Frederik, Krstić SrđanORCID, Traytel DmitriyORCID
Abstract
AbstractOnline monitoring is the task of identifying complex temporal patterns while incrementally processing streams of data-carrying events. Existing state-of-the-art monitors for first-order patterns, which may refer to and quantify over data values, can process streams of modest velocity in real-time. We show how to scale up first-order monitoring to substantially higher velocities by slicing the stream, based on the events’ data values, into substreams that can be monitored independently. Because monitoring is not embarrassingly parallel in general, slicing can lead to data duplication. To reduce this overhead, we adapt hash-based partitioning techniques from databases to the monitoring setting. We implement these techniques in an automatic data slicer based on Apache Flink and empirically evaluate its performance using two tools—MonPoly and DejaVu—to monitor the substreams. Our evaluation attests to substantial scalability improvements for both tools.
Publisher
Springer Science and Business Media LLC
Subject
Information Systems,Software
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