Mining High-Dimensional Event Sequences for Predictive Modelling

互联网不断产生大量的数据流，并形成事件序列，这些事件序列传达了关于个人简介、社区、观点、影响、意图和趋势的知识。同样，在医疗保健、商业、金融、国防等领域，生成的事件序列也传达了大量可用于社会效益、商业智能、公共安全和国防的知识。挖掘大量复杂的序列数据进行预测分析是该计划的主要目的。处理的数据类型是多尺度的，其特征在于异质性，显着的噪声和缺失值，高维和它们的属性之间的强相互关系。该研究计划的长期目标是建立和验证用于挖掘复杂事件序列和开发预测模型的新型数学框架。这种框架将基于坚实的统计理论，以产生易于解释的知识，处理各种序列类型，并足够有效地处理大型和复杂的数据。新框架将通过以下方式与传统模型区分开来：1）优化使用历史数据进行模型构建; 2）识别分析模型的相关变量; 3）发现和使用关系模式，如结构和因果关系; 4）计划库构建和计划/活动识别。该计划将通过完成一些相互关联的项目来实施。1)开发有效的算法，用于聚类非常高维的数据，用于建模和发现序列的语义行为; 2）开发有效的算法，用于挖掘序列事件和聚类轨迹，并具有潜在的表示，以降低维度并促进跟踪。我们还将开发新的措施来处理概念漂移; 3）开发有效的算法来分类“大”序列数据，识别每个类的相关子空间并结合漂移检测; 4）开发新的算法来发现模式和关系，通过可变阶马尔可夫链和稀疏马尔可夫技术来优化序列信息的发现。5)设计新的算法，在概率图形模型的帮助下建立行动计划。6)开发新的基于CRF的算法，以预测目标/计划早期识别的行动。我们还将研究如何使用行为模式来建立一个更有效的生存分析的考克斯比例风险模型。** 将在谢布鲁克大学的超级计算机Mammouth上建立一个支持预测建模的综合平台，该计算机是加拿大最强大的计算机之一。该平台将作为一个测试平台，不仅用于验证我们的方法，还用于开发实际应用程序。

The internet continuously produces large flows of data and form events sequences which convey knowledge about individuals' profiles, communities, opinions, influences, intentions, and trends. Similarly, in areas such as healthcare, business, finance, defense, event sequences are generated conveying also a great deal of knowledge that can be used for social benefits, business intelligence, public security and national defense. Mining massive and complex sequence data for predictive analytics is the main purpose of this program. The data types addressed are multisource, characterized by heterogeneity, significant noise and missing values, high-dimensional and strong interrelations between their attributes. ******The long-term goal of this research program is to build and validate novel mathematical frameworks for mining complex event sequences and developing predictive models. Such frameworks will be based on solid statistical theories to produce easily interpretable knowledge, to deal with a variety of sequence types, and to be efficient enough to deal with large and complex data. The new frameworks will distinguish themselves from the conventional models by 1) their optimized use of historical data for model building; 2) their identification of relevant variables for analytics models; 3) their discovery and use of relational patterns such as structural and causal relations; 4) their plan-library building and plan/activity recognition.******This program will be carried out by accomplishing a number of interrelated projects. 1) Develop efficient algorithms for clustering very high-dimensional data for modeling and discovering semantic behaviour from sequences; 2) Develop efficient algorithms for mining sequence events and cluster trajectories with a latent representation to reduce the dimensionality and facilitate tracing. We will develop also new measures to deal with concept drift; 3) Develop efficient algorithms for classifying "big" sequence data, identifying relevant subspaces for each class and incorporating drift detection; 4) Develop new algorithms for discovering patterns and relations by variable-order Markov chains and sparse Markov techniques to optimize discovery of sequential information. 5) Design new algorithms for building plans of actions with the help of probabilistic graphic models. 6) Develop new CRF-based algorithms to anticipate actions for early identification of the goals/plans. We will also investigate the use of patterns of actions to build a more effective Cox proportional hazards model for survival analysis. ******An integrated platform to support the predictive modelling will be built on the supercomputer Mammouth, one of the most powerful computing machines in Canada, at the Université de Sherbrooke. This platform will serve as a test-bed not only for validating our methods but also for developing real-world applications.