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有关个人资料、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递了大量可用于社会效益、商业智能、公共安全和国防的知识。挖掘大量和复杂的序列数据进行预测分析是这个程序的主要目的。所处理的数据类型是多源的，其特点是异质性、显著的噪声和缺失值、属性之间的高维和强相互关系。******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。这样的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。新框架与传统模型的区别在于：1)优化使用历史数据构建模型；2)他们对分析模型相关变量的识别；3)发现和使用关系模式，如结构关系和因果关系；4)他们的计划库建立和计划/活动识别。******该方案将通过完成一些相互关联的项目来实施。1)开发高效的算法，用于聚类非常高维的数据，以便从序列中建模和发现语义行为；2)开发有效的算法来挖掘具有潜在表示的序列事件和聚类轨迹，以降低维数并便于跟踪。我们还将制定新的措施来应对概念漂移；3)开发高效的“大”序列数据分类算法，识别每一类的相关子空间，并结合漂移检测；4)利用变阶马尔可夫链和稀疏马尔可夫技术开发新的模式和关系发现算法，以优化序列信息的发现。5)在概率图形模型的帮助下，设计新的算法来构建行动计划。6)开发新的基于crf的算法来预测行动，以便及早识别目标/计划。我们还将研究行动模式的使用，以建立更有效的Cox比例风险模型，用于生存分析。******一个支持预测模型的综合平台将建立在位于舍布鲁克大学的超级计算机Mammouth上，这是加拿大最强大的计算机之一。这个平台将作为一个测试平台，不仅用于验证我们的方法，也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.