"Data Science and Visual Computing" is a reference guide on how to deal with big data.
We are awash in data. We know it, but it’s like the weather — we can’t manage it. We are struggling to get a grip on it, understand it, use it and exploit it, but it is being generated faster than we can harness it. Data has always been available to us; the difference now is that it’s all (or most of it) in digital form, and therefore easily to store. And we are storing it — saving every last bit, it seems.
The data comes from credit card transactions, the sale of everything and anything you can think of, remote sensors and the Internet of things, simulations, scientific experiments, monitoring of business processes, security data, social media, recording devices and more.
Global data are increasingly complex and heterogeneous, and are predicted to rise to over 100 zettabytes — that’s the equivalent of 10 billion PC disk drives. 10 billion.
Not only is the volume already huge and increasing, it is ever more important for safety, security, and forensics.
The need to get a grip on all this data across a variety of disciplines has led to the formation of national and institutional Data Science Institutes and Centers. Driven by national priority, they attract support for research and development within their organizations and institutions to bring together interdisciplinary expertise to address a wide variety of problems. And visual computing is the set of tools and methodologies used to extract information from data.
The methods are not new, and include data analysis, simulation, and interactive exploration using 2D and 3D visualization techniques. What is new is the user friendliness, ease of use, and most importantly, the compatibility of data. Whereas data from entity A could never be read by entity B, those artificial barriers are being swept away by a combination of international standards cooperation, more robust file translation programs, and AI which is used to help decipher the intent and context of the data to make it more useful.
These are big ideas, and big developments that are needed and demanded by the onslaught of all the big data.
This book, Data Science and Visual Computing, pulls together the work of experts, academicians commercial, and scientific researchers and developers into one volume. It’s not a book one reads from cover to cover, but rather as a reference guide on how to deal with, and even understand the meaning and impact of visualizing big data as a methodology of managing it.
Humans are visual processors. We understand things by seeing the relationship between the various elements. Translating mind-numbing data into images helps us understand it, see the differentials, discover the trends, and anticipate the problems. Data Science and Visual Computing will show you what’s being done, what’s being worked on and give you some hope and confidence that yes we can get a grip on the flood of data and pout it to use for our benefit. For those who want more detail on current R&D, the book includes a list of Further Reading and a comprehensive set of References.