Real Time Analytics: Algorithms and Systems
August 07, 2017 ยท Declared Dead ยท ๐ Proceedings of the VLDB Endowment
"No code URL or promise found in abstract"
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Authors
Arun Kejariwal, Sanjeev Kulkarni, Karthik Ramasamy
arXiv ID
1708.02621
Category
cs.DB: Databases
Cross-listed
cs.LG
Citations
53
Venue
Proceedings of the VLDB Endowment
Last Checked
3 months ago
Abstract
Velocity is one of the 4 Vs commonly used to characterize Big Data. In this regard, Forrester remarked the following in Q3 2014: "The high velocity, white-water flow of data from innumerable real-time data sources such as market data, Internet of Things, mobile, sensors, click-stream, and even transactions remain largely unnavigated by most firms. The opportunity to leverage streaming analytics has never been greater." Example use cases of streaming analytics include, but not limited to: (a) visualization of business metrics in real-time (b) facilitating highly personalized experiences (c) expediting response during emergencies. Streaming analytics is extensively used in a wide variety of domains such as healthcare, e-commerce, financial services, telecommunications, energy and utilities, manufacturing, government and transportation. In this tutorial, we shall present an in-depth overview of streaming analytics - applications, algorithms and platforms - landscape. We shall walk through how the field has evolved over the last decade and then discuss the current challenges - the impact of the other three Vs, viz., Volume, Variety and Veracity, on Big Data streaming analytics. The tutorial is intended for both researchers and practitioners in the industry. We shall also present state-of-the-affairs of streaming analytics at Twitter.
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