Abstract:

Global Skyline query is a variant of the Skyline query which has been used for multiple objective decision making, business planning, network monitoring and data mining etc. The result set of Global Skyline query is close to the ones of dynamic Skyline query and reverse Skyline query. With the number of historical data increases, Skyline query on centralized system is not competent for big data and Skyline query for largescale data on time series is a challenge. A parallel algorithm of Global Skyline on time series is proposed. Firstly, we present a inverted index based on data on time series. Secondly, we provide the concept of Global Skyline cell which can eliminate the dominated cells according to the cell dominance relationship. The coarse grained pruning strategy can help to avoid a lot of meaningless computation. The query point divides the data space into the four quadrants, Global Skyline query can be executed in eachquadrant circularly. Lastly through extensive experiments with both realworld and synthetic datasets, we show that our algorithm is much more efficient for big data on time series.