Simultaneous Processing of Multi-Skyline Queries with MapReduce

Abstract

SUMMARYWith rapid increase of the number of applications as well as the sizes of data, multi-query processing on the MapReduce framework has gained much attention. Meanwhile, there have been much interest in skyline query processing due to its power of multi-criteria decision making and analysis. Recently, there have been attempts to optimize multi-query processing in MapReduce. However, they are not appropriate to process multiple skyline queries efficiently and they also require modifications of the Hadoop internals. In this paper, we propose an efficient method for processing multi-skyline queries with MapReduce without any modification of the Hadoop internals. Through various experiments, we show that our approach outperforms previous studies by orders of magnitude. key words: multi-query processing, skyline query, MapReduce framework

Efficiently computing Pareto optimal G-skyline query in wireless sensor network

“…Top-k skyline [14][15][16][17][18] returned the first k points in ranking by a function, and it was suitable for queries that have specific requirements for the number of results. Kim et al 19 and Zaman et al 20 introduced MapReduce technology to do skyline query on distributed database efficiently. Not only that the probabilistic skyline [21][22][23] was also presented to do query on uncertain data set which was different from data set in above algorithms.…”

“…As the DSG is built from low layer to high layer, so when the points in layer i are checked, DSG has been constructed in precious i-1 layers, we should first find the least parent p l in layer i-2 of the least parent of p in layer i-1 , and the maximal parent p m of the maximal parent of p in layer i-1 , then all the points between p l and p m are also p's parents (lines 8-10). On the other hand, we should visit the points less than p l and bigger than p m with index in both two directions until a point not dominating p is visited respectively (lines [11][12][13][14][15][16][17][18][19][20].…”