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

拟议的研究将开发有效和可扩展的方法，用于混合模式数据和序列的模式发现和分析，重点是理论发展，在工业和生物信息学中的应用，以及为学术界和工业界提供高质量的人才培训。 在静态数据库中，混合模式数据采用连续值和离散值的混合，后者可以是有序值或名义值。在动态过程中，每个数据点，无论是连续值还是离散值，都与时间戳相关联。所获取的数据是连续时间序列和离散字符序列的混合，称为混合模式序列。随着无线传感器的发展，这些类型的数据在今天非常普遍，并将继续扩大，对现有的数据挖掘技术提出了重大挑战，特别是对于石油，能源，金融和生物信息学等应用。 为了应对这一挑战，拟议的研究扩展了我的大学团队和衍生公司多年来的研究，以推进非常大的混合模式数据和序列的模式发现和分析。我们提出的方法已经从定义模式为整个空间中的元组转移到子空间中的统计事件关联;从大量发现的模式转移到概括的模式簇;从事件关联转移到概率结构表示（如属性超图和随机超图）;（4）从从数据本身发现模式到将元数据紧密结合到发现过程中。为了很好地扩展到非常大的数据库，需要开发有效的修剪技术，高效的数据结构和可扩展的算法。超图模型揭示了数据中固有的微妙知识，使领域专家能够做出明智的决策-这是在这个PB时代实现“从数据到知识”挑战的重要一步。在建议中，将重点放在HQP的培训上，以发展他们在理论和应用方面的优势，以及技术转移和商业化技能。

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.