Distributed temporal multi-dimensional data mining

分布式时态多维数据挖掘

• 批准号: 356020-2009
• 负责人: McGregor, Carolyn
• 金额: $ 1.09万
• 依托单位: University of Ontario Institute of Technology
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2009
• 资助国家: 加拿大
• 起止时间: 2009-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=431895
• 关键词: Distributed; temporal; multi; dimensional; data

The process of temporal abstraction (TA) takes either raw or pre-processed stream data as input and produces context sensitive and qualitative interval based representations. Data Mining provides a mechanism to perform knowledge discovery on ever increasing volumes of data. That is, to transform it from data to information and ultimately to knowledge. Research has begun to apply TA concepts to the pre- processing steps of data mining. A number of limitations of existing temporal abstraction systems in particular relate to (1) their focus on one particular clinical event/condition, rather than creating an environment to explore multiple clinical events/conditions; (2) an inability to support complex abstracts that for example represent abstractions of behaviour across multiple streams and (3) an inability to support abstractions within a multidimensional context. These limitations are impacted by limitations within the supporting data mining principles for pre-processing temporally abstracted time series data.

时间抽象（TA）的过程将原始或预处理的流数据作为输入，并产生上下文敏感和基于定性区间的表示。数据挖掘提供了一种在不断增加的数据量上进行知识发现的机制。也就是说，将其从数据转化为信息，最终转化为知识。研究已经开始将TA概念应用到数据挖掘的预处理步骤中。现有的时间抽象系统的许多限制特别涉及：（1）它们关注于一个特定的临床事件/状况，而不是创建环境来探索多个临床事件/状况;（2）不能支持复杂的抽象，例如表示跨多个流的行为的抽象;以及（3）不能支持多维背景内的抽象。这些限制的影响，支持数据挖掘原则的限制，预处理时间抽象的时间序列数据。