Similarity comparison and analysis of sequential data

This paper discusses approaches to problems associated with the processing of experimental data for complex domains in such areas as the behavioural and social sciences. It explores computational techniques which are to be implemented to build tools bringing higher levels of computational intelligence to the analysis of coded event sequences. Approaches in which inherent redundancy, recurrency, or dependency in sequences may be exploited include pattern recognition, information theory based methods, and Petri nets. Experimental examples which illustrate the matching, alignment and identification of patterns in sequences are presented