Due to the impact of the big data environment and the fourth paradigm of scientific research, the trend of computability in information analysis has become more obvious. First, as information equipment becomes cheaper, the cost of tools for information analysis decreases and the path for the computability of information analysis widens. Second, most of the objects to be processed are in digital forms which enable a wider span of data resource to be analyzed. This creates more opportunities for computing in information analysis. Third, computing methods have become more intelligent, which brings about the ability to carry out an in depth analysis and provides a deep understanding of the information. These three factors play a decisive role in making the transition from information analysis to Computational Information Analysis(CIA).
CIA is both a theory and an approach which derives from information analysis and information technology of which computer technology is at the center. It is proposed to address the limitation of human's ability in collecting, processing and analyzing large amount of information. In CIA, computers and related information technology are adopted as basic tools and intelligent technologies like machine learning and knowledge understanding are incorporated as core technology. Information analysis methods and mathematical models are used to organize and analyze information. Through a deeper analysis and mining in the content, relationship and pattern in the data, CIA helps analysts solve analysis problem and tasks and make decisions.
While human is inadequate in gathering, processing and analyzing large amount of data, CIA makes up for that shortcoming with the ability to harness the fast computing power of computers and is also more objective when compared with human. CIA provides a more comprehensive, faster, accurate, objective and deeper analysis result for the target problems thanks to its advantages in harnessing the power of computer technology and quantitative analysis methods. To be more specific, CIA is driven by data mining and machine learning techniques on the analysis environment which is built upon computers. The analysis of CIA is mainly quantitative and places an emphasis on methods that can be processed by machines to undergo quantitative, computational and testable process. Besides, CIA also contributes to the knowledge system as it not only inherits theories and methods from information analysis but also borrows theories and methods from computer science, data science, mathematical statistics and other fields and develops new theories and methods with computational characteristics.
CIA has its own thinking and ideas. Quantitative thinking is to abstract quantitative mathematical forms from objective things and phenomena. Automatic thinking is to transform data and analysis method in a form that can be processed by a computer. Integrative thinking places an importance on the relevance of different data sources. By fusing multiple data sources, it is possible to not only reveal correlations but also complement or cross validate using different kinds of data. Fault tolerance thinking means that precision loss at a micro level and a certain amount of errors and confusions is tolerable.
Finally, this paper raises research topics which deserve more attention under different perspectives, i.e. theory, method and technique, system, and application practice, including but not limited to: constructing a theory of data advanced analytics based on the integration of information science theory and related disciplines, discussing on automatic implementation and improvement of different information analysis methods in CIA, designing an intelligence analysis system within task data method which is interrelated, combining with new computing technology or multiple data sources for deep data content mining, etc. 51 refs.