Discovering Analytic Associate Rule Filtering on Multi-Dimensional Data Streams

摘要

Multidimensional data points discover structural and chronological association inside the data streams. The PaDSkyline framework in a distributed environment utilized intra group optimization and multi filtering technique for skyline query processing. Skyline query processes within each group of distributed data sets, but dynamic filtering point selection was not performed with cost effective system. Similarity- Profiled temporal Association MINing mEthod (SPAMINE) used reference time sequences and threshold value to filter the information from real world data. Different similarity models for filtering temporal patterns were not very effective for performing the phase shift in time. To attain minimal phase shift based cost effective filtering on multidimensional data stream, Analytic Associate Rule Filtering (AARF) mechanism is proposed in this study. The main objective of AARF is to identify the relationship between attributes on multidimensional data and to filter out the independent attributes from the data streams. Initially, analytic association rule uses the weight computing factor to identify relationship and make inferences while testing multidimensional samples. Secondly, with the help of the analyzed relationship, the AARF mechanism uses the attribute independent criterion to discard negligible weight from association rule. Finally, to filter the analytic association rule with specified phase shift time, the 'if-then' strategy is used in AARF mechanism. AARF mechanism has an ability to make an analytic filtering with minimal phase shift time on multidimensional test dataset. The minimal phase shift time reduces the execution time factor and attains cost effective filtering system. Experiment is conducted using Japanese vowel multidimensional data set extracted from UCI repository for measuring the factors such as the average precision level, execution time, filtering query traffic efficiency and true positive rate.