English

Real Time Analytics: Algorithms and Systems

Databases 2017-08-10 v1 Machine Learning

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.

Keywords

Cite

@article{arxiv.1708.02621,
  title  = {Real Time Analytics: Algorithms and Systems},
  author = {Arun Kejariwal and Sanjeev Kulkarni and Karthik Ramasamy},
  journal= {arXiv preprint arXiv:1708.02621},
  year   = {2017}
}

Comments

Extended version of VLDB'15 tutorial proposal

R2 v1 2026-06-22T21:09:55.375Z