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Streaming graph analysis: new models, new architectures

Published: 08 May 2018 Publication History

Abstract

Applications in computer network security, social media analysis, health care, and other areas rely on analyzing a changing environment. The data is rich in relationships and lends itself to graph analysis. Traditional static graph analysis cannot keep pace with network security applications analyzing nearly one million events per second. Streaming frameworks like STINGER support ingesting up three million of edge changes per second but there are few streaming analysis kernels that keep up with these rates. We present a new computational model along with promising novel architectures to tackle massive data rates.

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  • (2018)Detecting Incremental Frequent Subgraph Patterns in IoT EnvironmentsSensors10.3390/s1811402018:11(4020)Online publication date: 18-Nov-2018

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cover image ACM Conferences
CF '18: Proceedings of the 15th ACM International Conference on Computing Frontiers
May 2018
401 pages
ISBN:9781450357616
DOI:10.1145/3203217
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 08 May 2018

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  • Research-article

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CF '18
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CF '18: Computing Frontiers Conference
May 8 - 10, 2018
Ischia, Italy

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Overall Acceptance Rate 273 of 785 submissions, 35%

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Cited By

View all
  • (2018)Detecting Incremental Frequent Subgraph Patterns in IoT EnvironmentsSensors10.3390/s1811402018:11(4020)Online publication date: 18-Nov-2018

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