The World’s First Multilingual Big Data Terminology Book

The World’s First Multilingual Big Data Terminology Book Published in China by CSPM


 The world's first multilingual big data terminology book was published in China this month (May 2020) and was also released globally. This book was a cooperation between many organizations and experts from within and outside of China.

This book brings us nine different categories (such as basics of big data, big data economics, big data standards, big data legislation, among others) and was released in eleven different languages that represents four of the world's language families that are spoken on six continents. It offers a systematic guide to academic terminology.

The book follows internationally accepted norms and is an innovative project that will not only serve as a reference for people working in this area, but will also be on the center of the transformation from Big Data to Big Knowledge.

In my opinion it is a really interesting book for people that work or is starting to study Big Data seriously, because the book covers from basic terminology all the way to more complex, professional ones. For me, it will be a great addition for the library of people studying big data. If you have any doubt about any word or terminology used in any work, article, books, or any other big data related text, you will have a very concrete and reliable source of information to search the meaning of the word you did not understand. You will not need to rely on the internet information, that many times does not provide a source and maybe can be not one hundred percent accurate.

Comments

  1. Paul LiMay 10, 2020 at 10:43 PM

    Amazing eric, great perspective on the topic, I learn a lot from your blog about The World’s First Multilingual Big Data Terminology Book Published in China by CSPM.

    ReplyDelete
  2. Paul LiJune 26, 2020 at 8:38 AM

    This article really highlights the importance of big data and shows why currently big data which is itself is Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. When we handle big data, we may not sample but simply observe and track what happens. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value and i really that articles like this highlight the usefulness and endless possibilities that big data provide, not only in this day and age but for the future aswell.

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