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tutorial

Streaming Analytics

Published: 13 August 2016 Publication History

Abstract

Recently we have seen emergence and huge adoption of social media, internet of things for home, industrial internet of things, mobile applications and online transactions. These systems generate streaming data at very large scale. Building technologies and distributed systems that can capture, process and analyze this streaming data in real time is very important for gaining real time insights. Real-time analysis of streaming data can be used for applications as diverse as fraud detection, in-session targeting and recommendations, control systems for transportation systems and smarter cities, earthquake prediction and control of autonomous vehicles. This tutorial will provide overview of streaming systems and hands on tutorial on building streaming analytics systems using open source technologies.

Supplementary Material

Part 1 of 2 (kdd2016_tutorial_streaming_analytics_01-acm.mp4)
Part 2 of 2 (kdd2016_tutorial_streaming_analytics_02-acm.mp4)

Cited By

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  • (2024)AI-Driven Personalization in Omnichannel MarketingLeveraging AI for Effective Digital Relationship Marketing10.4018/979-8-3693-5340-0.ch004(97-130)Online publication date: 18-Oct-2024

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Published In

cover image ACM Conferences
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2016
2176 pages
ISBN:9781450342322
DOI:10.1145/2939672
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 August 2016

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Author Tags

  1. IoT
  2. analytics
  3. distributed systems
  4. machine learning
  5. stream

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  • Tutorial

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KDD '16
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Acceptance Rates

KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2024)AI-Driven Personalization in Omnichannel MarketingLeveraging AI for Effective Digital Relationship Marketing10.4018/979-8-3693-5340-0.ch004(97-130)Online publication date: 18-Oct-2024

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