Welcome to yet another edition of Inside the Data Reading Room, a regular feature of my blog where I take a look at recent data management books. In today’s post we’ll examine three new books on various data-related topics, beginning with data agglomeration.
You may not have heard of data agglomeration but you’ll get the idea immediately – at least at a high level – when I describe it as gathering data in wireless sensor networks. For more details, you’ll want to read A Beginner’s Guide to Data Agglomeration and Intelligent Sensing by Amartya Mukherjee, Ayan Kumar Panja and Nilanjan Day (Academic Press, 2020, ISBN 978-0-12-620341-5). The authors are all professors who specialize in networking, IoT, and data issues.
The book offers provides a concise treatment of the topic starting out with an overview of the various types of sensors and transducers and how they are used. I always find it easier to learn-by-example, and this book is nice because the authors provide a variety of good examples.
Reading this book will provide you with descriptions and explanations of pertinent concepts like wireless sensor networks, cloud platforms, device-to-cloud and sensor cloud architecture but more importantly, it also describes how to gather and aggregate data from wireless sensor networks.
If you or your organization are involved gathering data from sensors, such as in IoT systems, this book will be a great help to you as you design and implement your applications.
Next up from the shelves of the Data Reading Room we have Rohit Bhargava’s Non Obvious Mega Trends (IdeaPress, 2020, ISBN 978-1-64687-002-8).
For those who do not know about this book series, every year since 2011 Rohit Bhargava has been publishing what he calls The Non Obvious Trend Report. He began writing these reports in response to the parade of annual articles talking about “the next big trends in the upcoming year,” which he found either to be too obvious (e.g. mobile phones still useful) or too self-serving (e.g. drone company CEO predicts this is the year of the drone) to be useful. In response, he created the Non Obvious Trend Report with the goal of being unbiased and digging deeper for nuances and trends missed elsewhere.
To a large extent, he succeeded. So much so that this book represents the 10th in the series. But what makes this particular book a must-have is that not only does it introduce 10 new trends, but it also documents and reviews all of the trends over the past decade.
For readers of this blog, Chapter 11, Data Abundance, will likely be the most useful chapter (although the entire book is great for research). In Chapter 11 he describes what data abundance is, how understanding it can be used to your advantage, as well as the various trends that have led to the evolution of data abundance.
I look forward to each new, annual edition of Non Obvious, but I think this year’s edition stands out as one that you will want to have on your bookshelf long-term.
The final book for today is Systems Simulation and Modeling for Cloud Computing and Big Data Applications edited by J. Dinesh Peter and Steven L. Fernandes (Academic Press, 2020, ISBN 978-0-12-620341-5).
Models and simulations are an important foundation for many aspects of IT, including AI and machine learning. As such, knowledge of them will be beneficial for data professionals and this book provides an education in using System Simulation and Modeling (SSM) for tasks such as performance testing and benchmarking.
The book analyzes the performance of several big data and cloud frameworks, including benchmarks such as BigDataBench, BigBench, HiBench, PigMix, CloudSuite and GridMix.
If you are dealing with big data and looking for ways to improve your testing and benchmarking through simulation and modeling, this book can be of help.