Mining High-Dimensional Event Sequences for Predictive Modelling

挖掘高维事件序列以进行预测建模

• 批准号: RGPIN-2015-04592
• 负责人: Wang, Shengrui
• 金额: $ 3.13万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634359
• 关键词: Mining; Dimensional; Event; Sequences; Predictive

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.

互联网不断地产生大量的数据流，并形成事件序列，传达关于个人概况、社区、观点、影响、意图和趋势的知识。同样，在医疗、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福利、商业情报、公共安全和国防的知识。挖掘海量和复杂的序列数据以进行预测分析是该程序的主要目的。所涉及的数据类型是多源的，其特点是异构性、显著的噪声和缺失值、高维及其属性之间的强相互关系。