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A Demonstration of Striim A Streaming Integration and Intelligence Platform

Published: 24 June 2019 Publication History

Abstract

Today's data-driven applications need to process, analyze and act on real-time data as it arrives. The massive amount of data is continuously generated from multiple sources and arrives in a streaming fashion with high volume and high velocity, which makes it hard to process and analyze in real time. We introduce Striim, a distributed streaming platform that enables real-time integration and intelligence. Striim provides high-throughput, low-latency event processing. It can ingest streaming data from multiple sources, process data with SQL-like query language, analyze data with sophisticated machine learning models, write data into a variety of targets, and visualize data for real-time decision making. In this demonstration, we showcase Striim's ability to collect, integrate, process, analyze and visualize large streaming data in real time.

References

[1]
Striim. https://www.striim.com.
[2]
P. Carbone, S. Ewen, S. Haridi, A. Katsifodimos, V. Markl, and K. Tzoumas. Apache flink: Stream and batch processing in a single engine. IEEE Data Eng. Bull., 2015.
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R. B. Cleveland, W. S. Cleveland, J. E. McRae, and I. Terpenning. Stl: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 1990.
[4]
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD, 2009.
[5]
N. Moustafa and J. Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems. MilCIS, 2015.
[6]
A. Pareek, B. Khaladkar, R. Sen, B. Onat, V. Nadimpalli, M. Agarwal, and N. Keene. Striim: A streaming analytics platform for real-time business decisions. BIRTE, 2017.
[7]
A. Pareek, B. Khaladkar, R. Sen, B. Onat, V. Nadimpalli, and M. Lakshminarayanan. Real-time etl in striim. BIRTE, 2018.

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  1. A Demonstration of Striim A Streaming Integration and Intelligence Platform

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    cover image ACM Conferences
    DEBS '19: Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems
    June 2019
    291 pages
    ISBN:9781450367943
    DOI:10.1145/3328905
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 24 June 2019

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    Author Tags

    1. data integration
    2. machine learning
    3. streaming

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    DEBS '19

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    DEBS '19 Paper Acceptance Rate 13 of 47 submissions, 28%;
    Overall Acceptance Rate 145 of 583 submissions, 25%

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