Compendious and succinct data structures for big data

Abstract

Recent growth of cloud data and cloud computing has been expediter and predecessor to the appearance of big data. Cloud computing has improved data storage by calculating and saving time with the means of identical and relevant technologies. While cloud computing provides important benefits over conventional physical deployments, its platform has also originated in numerous forms from time to time (Gog and Petri in Softw Pract Exp 44:1287–1314 [1]). In this proposed paper, the main data structure used in big data is tree. Quad tree is used for graphics and spatial data in the main memory. Traditional sub-linear algorithms that are used to handle Quad tree were inefficient. Also, SDS can be optimized for query handling and space. Optimized SDS can improve functionality of different SDS like rank and select, FM index (Blandford et al. in Proceedings of the fourteenth annual ACM-SIAM symposium on discrete algorithms, pp 679–688, 2003 [2]). As geometric data, proteins database, Gnome data, DNA data are large databases for main memory, an efficient and simple representation is required in main memory of computer system. However, overall quantity of storing area is not a vital problem in recent times, considering the fact that external memory can store large quantity of data and may be inexpensive, time needed to get access to information is a vital blockage in numerous programs. Number of access for hitting outside of memory is conventionally lower than number of access for hitting into main memory which has caused examine of recent compressed demonstrations of information that might be capable to save identical data in a reduced area.