Data partitioning based on sampling for power load streams

摘要

A novel data streams partitioning method is proposed to resolve problems of range-aggregation continuous queries over parallel streams for power industry. The first step of this method is to parallel sample the data, which is implemented as an extended reservoir-sampling algorithm. A skip factor based on the change ratio of data-values is introduced to describe the distribution characteristics of data-values adaptively. The second step of this method is to partition the fluxes of data streams averagely, which is implemented with two alternative equal-depth histogram generating algorithms that fit the different cases: one for incremental maintenance based on heuristics and the other for periodical updates to generate an approximate partition vector. The experimental results on actual data prove that the method is efficient, practical and suitable for time-varying data streams processing.