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Data Analytics: Industrial Perspective & Solutions for Streaming Data

Mohsin Munir; Sebastian Baumbach; Ying Gu; Andreas Dengel; Sheraz Ahmed
In: Mark Last; Horst Bunke; Abraham Kandel (Hrsg.). Data Mining in Time Series and Streaming Databases. Chapter 7, Pages 144-168, Series in Machine Perception and Artificial Intelligence, Vol. 83, ISBN 978-981-3228-05-4, WORLD SCIENTIFIC, 3/2018.

Abstract

Over the past few years, a lot of devices and machines around us are becoming 'smart'. Based on the idea of the Internet of Things (IoT), different devices and machines can connect to the internet and communicate with each other. Such internet enabled devices are continuously observing their environment and logging a lot of data in the back-end database. By applying data analytics on the gathered Big Data, smart decisions are made to facilitate the end user according to the current situation. This capability of adaptive decision making actually makes ordinary devices and machines 'smart'. These devices and machines are becoming intelligent by learning about their surroundings from different sources, and develop the ability to avoid unforeseen situations by analyzing that data. In this chapter, we provide a comprehensive overview of how different industrial players are using data analytics to provide better services to their customers and improve their internal processes and workflows. We discuss how different industries use data analytics to gain vital insights for providing better healthcare to public, making homes more secure, increasing crop yield, delivering goods more quickly, reducing the downtime of a machine, avoiding a disease, etc. An overview of different analytics platforms and solutions used in different industries for time series and streaming data are also discussed in this chapter.

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data-analytics-industrial.pdf

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