Distributed temporal multi-dimensional data mining

点击翻译按钮获取中文摘要

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.