Authors: Ibrahim Gomaa; Hoda M.O. Mokhtar
Addresses: Faculty of Computers and Information, Cairo University, Giza, 12613, Egypt ' Faculty of Computers and Information, Cairo University, Giza, 12613, Egypt
Abstract: With the expanding number of communications from different mobile applications that acquire location information, the demand for continuous skyline queries has increased. In addition, the extremely fast increase in the data volume and mobile applications that deal with such volume of data such as check-ins recommendation, information services and applications of road networks; have both driven the need to adapt new processing environments to deal with huge amounts of data. In this paper, we present a number of efficient algorithms for processing continuous skyline queries on large datasets using MapReduce framework. The main idea of our proposed algorithms is to compute the skyline query only once at the starting position; then update on the result at the movement of the query point rather than computing the skyline at every time from scratch. In addition, experimental results are conducted which demonstrate the accuracy, performance and efficiency of the proposed algorithms.
Keywords: continuous query processing; moving object; parallel computation; skyline queries; big data management.
International Journal of Data Science, 2019 Vol.4 No.1, pp.45 - 62
Received: 13 Apr 2017
Accepted: 17 Sep 2017
Published online: 11 Mar 2019 *