The management and analysis of big data, and the SP theory of intelligence

This article is about how the "SP theory of intelligence", and its realisation in the "SP machine", may, with advantage, be applied to the management and analysis of big data. The SP system has potential for the discovery of 'natural' structures in big data at various levels of abstraction, including recurrent patterns and associations, and discontinuous dependencies in data. Potential applications include the discovery of recurrent patterns in economics-related data, in meteorological data, in DNA sequences and amino-acid sequences, and in associations between biochemical structures and diseases. Since information compression is at the heart of the SP system, and since the system has potential to achieve relatively high levels of compression, it may achieve useful reductions in the volume of big data and thus facilitate its storage and management. There is potential for substantial economies in the transmission of big data, and for the efficient processing of compressed data, without the need for decompression. In the interpretation of data, the SP system has capabilities that include such things as the parsing and production of language, pattern recognition, and various kinds of probabilistic reasoning. But potentially the most useful facility with big data would be scanning for patterns, with recognition of family-resemblance or polythetic categories, at multiple levels of abstraction and with part-whole hierarchies, with inductive prediction and the calculation of associated probabilities, with a role for context in recognition, and robust in the face of errors of omission, commission or substitution. There are potential applications in several areas including, security, criminal investigations, finance, meteorology, and astronomy.