A Cube Analytical Mining Framework for Stream Data - Canghong Jin; Zemin Liu; Minghui Wu; Jing Ying (Dianxin kexue)
Stream data has been one of the most significant data format recently. OLAM（online analytical mining） operation could provide multi-level data views for analysts. However, OLAM operations depend on data aggregation, which is in conflict with the continuous incensement and dynamic nature of stream data. Thus, partial materialized view from stream data directly by typical OLAP approaches cannot be created and all data cells for the limitation of storage cannot be saved. In order to solve the above problems, an advanced sketch based OLAM framework named sketch cube to analyze stream data was proposed. Sketch cube maps a set of attributes to a unique number and stores it in sub-linear data structure, and then builds an inverted index by tiled time window. The precondition of using sketch cube by theoretical analysis was given and the storage efficiency and query performance on mass mobile data corpus was evaluated, which supports requirements of real-time analysis.