Comparative Study on Skyline Query Processing Techniques on Big Data
Praveen Kumar Sadineni
Abstract
In this digital era, humans are intended to access huge amount of data to fulfill their needs. The so called big data has been generated across the globe from various sources such as e-commerce, social networking sites, banking applications etc. and makes it difficult to capture, store, access and manage the huge amount of data resources. To overcome this disadvantage, a new technique is proposed to access the useful insights from a pool of data to deliver better decisions.Skyline query is one such efficient and popular decision support approaches, which remains as an enhanced version of database queries that help to extract the desired data from large databases and recognizes the objects that present along with the optimum combination of the features of the dataset in an efficient manner. This paper deals with various skyline query processing techniques such as basic distributed skyline (BDS), improved distributed skyline (IDS), progressive distributed skyline (PDS), mobile ad-hoc network (MANET), SKYPEERISKYPEER+, parallel distributed skyline (PaDSkyline), feedback based distributed Skyline (FDS), distributed skyline (DSL), skyline space partitioning (SSP), SKYFRAME, iSky which can be used on Big Data to extract the required information and summarizes its comparison.