Big Data and the SP Theory of Intelligence

Abstract

This paper is about how the SP theory of intelligence and its realization in the SP machine may, with advantage, be applied to the management and analysis of big data. The SP system-introduced in this paper and fully described elsewhere-may help to overcome the problem of variety in big data; it has potential as a universal framework for the representation and processing of diverse kinds of knowledge, helping to reduce the diversity of formalisms and formats for knowledge, and the different ways in which they are processed. It has strengths in the unsupervised learning or discovery of structure in data, in pattern recognition, in the parsing and production of natural language, in several kinds of reasoning, and more. It lends itself to the analysis of streaming data, helping to overcome the problem of velocity in big data. Central in the workings of the system is lossless compression of information: making big data smaller and reducing problems of storage and management. There is potential for substantial economies in the transmission of data, for big cuts in the use of energy in computing, for faster processing, and for smaller and lighter computers. The system provides a handle on the problem of veracity in big data, with potential to assist in the management of errors and uncertainties in data. It lends itself to the visualization of knowledge structures and inferential processes. A high-parallel, open-source version of the SP machine would provide a means for researchers everywhere to explore what can be done with the system and to create new versions of it.