Storage and Computation on Big Data: A Comparative Study

A huge data space includes set of interesting points; Skyline is an important operation in many applications to return a set of interesting points from a potentially huge data space [1]. This survey paper highlights the characteristics of big data and their challenges. This paper also discusses the tools and techniques of big data. The existing algorithms like SaLSa, SSPL are novel computation algorithms. SaLSa exploits the idea of presorting the input data so as to effectively limit the number of tuples to be read and compared [2]. SSPL utilizes sorted positional index lists which require low space overhead to reduce I/O cost significantly [1]. SSPL consists of two phases. In phase 1, SSPL computes scan depth of the involved sorted positional index lists. During retrieving the lists in a round-robin fashion, SSPL performs pruning on any candidate positional index to discard the candidate whose corresponding tuple is not skyline result. Phase 1 ends when there is a candidate positional index seen in all of the involved lists. In phase 2, SSPL exploits the obtained candidate positional indexes to get skyline results by a selective and sequential scan on the table [1].