Storage and Computation on Big Data: A Comparative study

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-35 Number-2
Year of Publication : 2016
Authors : K. Srinivas, P. Buddha Reddy, CH Sravan Kumar
  10.14445/22312803/IJCTT-V35P120

MLA

K. Srinivas, P. Buddha Reddy, CH Sravan Kumar "Storage and Computation on Big Data: A Comparative study". International Journal of Computer Trends and Technology (IJCTT) V35(2):114-117, May 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
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. 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. SSPL utilizes sorted positional index lists which require low space overhead to reduce I/O cost significantly. 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.

References
[1]. Xixian, Han., Jianzhong, Li., “Efficient Skyline Computation on Big Data,” IEEE transactions on knowledge and data engineering, vol. 25, no. 11, November 2013.
[2]. I., Bartolini, P., Ciaccia, M., Patella, “Efficient Sort- Based Skyline Evaluation,” ACM Trans. Database Systems, vol. 33, no. 4,pp. 31:1-31:49, 2008.
[3]. bigdataweek.com/blog/2013/04/08/five-steps-tohandling- big-data/
[4]. http://iveybusinessjournal.com/publication/fourstrategies- to-capture-and-create-value-from-big-data/
[5]. Sofiya Mujawar., Aishwarya Joshi., “Data Analytics Types, Tools and their Comparison”, ijarcce, Vol. 4, Issue 2, February 2015.
[6]. http://www.dataversity.net/3-types-data-analyticsdescriptivepredictive- prescriptive
[7]. http://www.computerworld.com
[8]. http://www.kdnuggets.com/2014/06/top-10-dataanalysis- toolsbusiness.html

Keywords
SaLSa, SSPL, Big Data.