Pattern Discovery and Analysis for Mixed-Mode Data and Sequences: Theory and Applications

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The proposed research will develop effective and scalable methodologies for pattern discovery and analysis of mixed-mode data and sequences with emphasis on theoretical development, applications in industry and bioinformatics, and high quality personnel training for academia and industry. In a static database, mixed-mode data takes on a mixture of continuous and discrete values, where the later can be either ordinal or nominal. In a dynamic process, each data point, be it continuous or discrete valued, is associated with a time stamp. This acquired data then is a mix of continuous time series and discrete character sequences referred to as mixed-mode sequences. With the advancement of wireless sensors, these types of data are very common today and will continue to expand, posing a significant challenge to existing data mining techniques especially for the applications such as oil, energy, finance and bioinformatics. To respond to this challenge, the proposed research extends many years of research done by my university team and spinoff companies to advance pattern discovery and analysis for very large mixed-mode data and sequences. Our proposed approaches have moved (1) from defining patterns as tuples in the entire space to statistical event associations in subspaces; (2) from overwhelming number of discovered patterns to summarized pattern clusters; (3) from event associations to probabilistic structural representations (such as attributed and random hypergraphs); (4) from discovering patterns from the data itself to incorporating metadata closely into the discovery process. To scale well to very large databases, effective pruning techniques, efficient data structures and scalable algorithms need to be developed. The hypergraph models uncover subtle knowledge inherent in data and enables domain experts to make informed decisions - an important step towards the challenge "from data to knowledge" in this petabyte era. In the proposal, strong emphasis will be placed on the training of HQP in developing their strength in theory and applications as well as in technology transfer and commercialization skill.