Scalable online first-order monitoring

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Scalable online first-order monitoring. / Schneider, Joshua; Basin, David; Brix, Frederik; Krstić, Srđan; Traytel, Dmitriy.

I: International Journal on Software Tools for Technology Transfer, Bind 23, Nr. 2, 2021, s. 185-208.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Schneider, J, Basin, D, Brix, F, Krstić, S & Traytel, D 2021, 'Scalable online first-order monitoring', International Journal on Software Tools for Technology Transfer, bind 23, nr. 2, s. 185-208. https://doi.org/10.1007/S10009-021-00607-1

APA

Schneider, J., Basin, D., Brix, F., Krstić, S., & Traytel, D. (2021). Scalable online first-order monitoring. International Journal on Software Tools for Technology Transfer, 23(2), 185-208. https://doi.org/10.1007/S10009-021-00607-1

Vancouver

Schneider J, Basin D, Brix F, Krstić S, Traytel D. Scalable online first-order monitoring. International Journal on Software Tools for Technology Transfer. 2021;23(2):185-208. https://doi.org/10.1007/S10009-021-00607-1

Author

Schneider, Joshua ; Basin, David ; Brix, Frederik ; Krstić, Srđan ; Traytel, Dmitriy. / Scalable online first-order monitoring. I: International Journal on Software Tools for Technology Transfer. 2021 ; Bind 23, Nr. 2. s. 185-208.

Bibtex

@article{8db25355cf2345cc973f2093b844a237,
title = "Scalable online first-order monitoring",
abstract = "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{\textquoteright} 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.",
author = "Joshua Schneider and David Basin and Frederik Brix and Sr{\d}an Krsti{\'c} and Dmitriy Traytel",
note = "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.",
year = "2021",
doi = "10.1007/S10009-021-00607-1",
language = "English",
volume = "23",
pages = "185--208",
journal = "Software-Concepts and Tools",
issn = "1433-2779",
publisher = "Springer",
number = "2",

}

RIS

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