Dominance Analyses Reduction in Skyline Query Processing over Data Stream with Data Mining Technique

The database community has observed in the past two decades, the growth of research interest in skyline queries, which aim to report to users interesting objects—commonly known as skylines—based on their preferences. The identification of skyline objects becomes more challenging when skylines are to be identified from a collection of continuously generated input data streams. In this paper, we proposed the Dominance Analyses Reduction (DAR) framework, which aims at addressing the issues of redundant dominance analyses that arise while determining skylines over data stream. Dominance analyses are repeated for objects that are in the overlapped frames of two windows and for pairs of objects that later reappear in the stream. DAR employs the Apriori algorithm, one of the most prevalent data mining algorithms, to identify the frequently occurring dominance analyses. Instead of conducting the dominance analyses again, their results are stored and utilised in the subsequent derivation of skylines. The DAR framework has been validated through several experiments. Its results exhibit significant reduction in the number of pairwise comparisons at both object and dimension levels and execution time.