Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
This foundational work aims to establish a methodology for big data computing, addressing challenges in science, technology, and industry, but it is incremental as it builds on existing discourse without introducing new empirical results.
The paper tackles the lack of a recognized methodology for big data by exploring fundamental questions about its nature, representation, and relationship to knowledge, proposing a multi-dimensional perspective for big data computing.
Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.