Visual analysis and frontier predictions of industrial big data

With the numerous development and continuous optimization of industrial technology, industrial big data is faced with the challenge of massive volume, multi-source, and heterogeneity. Industrial big data achievements present an exponential trend of growth because of the development strategy of national industry. Currently, massive scholars devote themselves to industrial big data researches, but the consistency of analysis and management on industrial big data results is inadequate, especially with the assistance of bibliometrics methods and visualization tools to intuitively and systematically demonstrate the regularity and value of data. It uses CiteSpace to conduct bibliometric analysis on the published literature (1901–2020) from the Web of Science Core Collection with a subject search related to industrial big data. In total, 1352 papers are synthetically reviewed by collaboration networks and co-citation networks involving countries, institutions, authors, journals, keywords, references. The objection of this study is to reveal the present status and potential frontiers for researchers in the field of industrial big data, providing them with valuable information and efficient guidance.