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 issues which are discussed are general and need to be addressed in any scenario. The SP system -- introduced in the article and fully described elsewhere -- may help to overcome the problem of variety in big data: the great diversity of formalisms and formats for knowledge and the many different ways in which knowledge may be processed. It has potential as "a universal framework for the representation and processing of diverse kinds of knowledge" (UFK). 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. Central in the workings of the system is lossless compression of information -- making big data smaller -- with associated benefits. 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 may assist in the management of errors and uncertainties in data, and it lends itself to the visualisation 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.