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Big data factories : collaborative approaches
Matei S., Jullien N., Goggins S., Springer International Publishing, New York, NY, 2017. 141 pp. Type: Book (978-3-319591-85-8)
Date Reviewed: Oct 16 2018

In the technology world, ABCD is a buzzword. “A” stands for artificial intelligence (AI), “B” for blockchain, “C” for cloud, and “D” for big data. “D” especially is the origin of the others: you need the cloud because you have to store big data; blockchains can be used to authenticate distributed big data; we can extract some intelligence from the available big data.

Big data is too big for traditional database methods (in the scale of zettabytes, that is, 1021 bytes). Big data issues include (but are not limited to): data processing, documentation, and formatting; standard ontologies and categorization; and platforms and automatic online data processing tools. Therefore, we need books on the critical issues involving all facets of big data.

Big data factories provides a preliminary solution using theoretical reasoning and real-life case studies. An edited book, it begins with an introductory chapter. Three parts follow: “Theoretical Principles and Approaches to Data Factories,” “Theoretical Principles and Ideas for Designing and Deploying Data Factory Approaches,” and “Approaches in Action Through Case Studies of Data Based Research, Best Practice Scenarios, and Educational Briefs.” As can be seen from the content, the first two parts are more research oriented while the last part is more or less supplementary.

Because there could be many origins of big data, this book basically focuses on social big data. With the proliferation and popularity of social media platforms such as Twitter, Facebook, and YouTube, zettabytes of data are produced each day. We need “quantitative and computational methods to model, analyze, and interpret [these] large-scale social phenomena.” Big data factories could be one of the few approaches that try to provide a feasible solution. But this is not a simple task. Social media big data analysis is explicitly transdisciplinary. It needs dynamic system analysis, AI, network theory, statistics, and so on. So this book just provides a glimpse of what all social media big data is all about.

If not done carefully, having access to big data tools will create a new digital divide. Those with the tools will know what is going on in the world and those who don’t will be left behind. Though it is not the responsibility of scientists, we should try to write more books and more articles to disseminate the ideas. This book is a good start.

Reviewer:  R. S. Chang Review #: CR146281 (1812-0621)
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