Scalable online first-order monitoring
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Scalable online first-order monitoring. / Schneider, Joshua; Basin, David; Brix, Frederik; Krstić, Srđan; Traytel, Dmitriy.
In: International Journal on Software Tools for Technology Transfer, Vol. 23, No. 2, 2021, p. 185-208.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Scalable online first-order monitoring
AU - Schneider, Joshua
AU - Basin, David
AU - Brix, Frederik
AU - Krstić, Srđan
AU - Traytel, Dmitriy
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2021
Y1 - 2021
N2 - Online 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.
AB - Online 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.
U2 - 10.1007/S10009-021-00607-1
DO - 10.1007/S10009-021-00607-1
M3 - Journal article
VL - 23
SP - 185
EP - 208
JO - Software-Concepts and Tools
JF - Software-Concepts and Tools
SN - 1433-2779
IS - 2
ER -
ID: 275272049