An Analysis of Scientific Production in Big Data Knowledge Domain on Google Books, YouTube and IEEE Explore® Digital Library

The paper aims to reveal the current state of book, video and article production in Big Data Knowledge Domain on particular platforms by examining the capabilities of Application Programming Interface (API) technology in conducting scientific data-driven research. Queries append public records from Google Books, YouTube and IEEE Explore® Digital Library to two research paradigms (sets of data sets): Big Data (incl. Analysis, Engineering, Architecture, Governance, Management, Frameworks) and Big Data interdisciplinary fields (Data Science, Data Mining, Deep Learning, Machine Learning, Artificial Intelligence). Metadata from more than 25 000 conference papers, 2000 books over the past 50 years, and 4 000 videos for the last 12 years, matching the searching criteria, has been stored and analyzed. The outputs are summarized in statistics, forecasting, rating key findings by various attributes: title, author, publisher, research field, category, subject, publication year, description, view count, and a combination of mentioned metadata in cross-tables. Nearly a half of billion video views; a half of million article reference count; a twofold increase in the number of papers in Machine learning over past three years compared to the total number in the same field for entire 1988-2016 period; 1:2:12 overall books-to-videos-to conference papers ratio; 61.3% of last year's video production just in a month (Jan-2020); the earliest found usage of "Artificial Intelligence" expression in a printed law document dated 1848 are few curious examples of analysis findings. The paper presents non-commercial research and retrieved data is collected entirely from public records.