Author : Vinesh kumar 1
Date of Publication :22nd February 2018
Abstract: Data Representation in memory is one of the tasks in Big Data. Data structures include several types of tree data structures through the system can access accurate and efficient data in Big Data. Succinct data structures can play important role in data representation while data is processed in RAM memory for Big Data. Choosing a data structure for Data representation is a very difficult problem in Big Data. We proposed some solution of problems of data representation in Big Data. Data mining in Big Data can be utilized to take a decision by Data processing. We know the functions and rules for query processing. We have to either change method of data processing or we can change the way of data representation in memory. In this paper, different kind of tree data structures is presented for data representation in RAM of a computer system for Big Data by using succinct data structures. Data mining is often required in Big Data. Data must be processed in parallel or steaming manner. In this paper, we first compare all data structures by the table and then we proposed succinct data structures those are very popular now. Each tree presented for Data representation has different time and space complexities.
Reference :
-
- G. Jacobson. Space-efficient static trees and graphs. In FOCS, pages 549–554, 1989.
- J. I. Munro. Tables. In FSTTCS, pages 37–42, 1996.
- R. Grossi, A. Gupta, and J. S. Vitter. High-order entropy-compressed text indexes. In SODA, pages 841– 850, 2003.
- G. Gottlob and T. Schwentick. Rewriting ontological queries into small nonrecursive datalog programs. In KR, 2012.
- R. Grossi, A. Gupta, and J. Vitter. High-order entropycompressed text indexes. In Proc. 14th Symposium on Discrete Algorithms (SODA), pages 841–850, 2003
- Ladra. Algorithms and Compressed Data Structures for Information Retrieval. PhD thesis, University of A Coruña, 2011.
- S. Muthukrishnan. Data streams: Algorithms and applications, 2003. Plenary talk at the 14th Annual ACMSIAM Symposium on Discrete Algorithms (SODA 2003).
- Bernard Chazelle. Who says you have to look at the input? The brave new world of sublinear computing, 2004. Plenary talk at at the 15th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2004).
- Scott Aaronson. NP-complete problems and physical reality. SIGACT News, 36(1):30, 2005.
- Eric Baum. What is Thought? MIT Press, 2004.
- David Benoit, Erik D. Demaine, J. Ian Munro, Rajeev Raman, Venkatesh Raman, and Srinivasa Rao. Representing trees of higher degree. Algorithmica, 43(4):275{292, 2005.
- Richard F. Geary, Rajeev Raman, and Venkatesh Raman. Succinct ordinal trees with level-ancestor queries. In SODA '04: Proceedings of the _fiffteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1{10. Society for Industrial and Applied Mathematics, 2004.
- Sunil Kumar and Varun Sharma, “Working of Cloud Architecture and Components”, In Proceedings of International Multi Track Conference on Sciences, Engineering & Technical Innovations(IMTC-14), June3- 4,2014, Vol.1, pp.313-316, ISBN: 978-81-929077-0-3.
- Gupta, A., Hon, W.K., Shah, R., Vitter, J.S.: Compressed data structures: Dictionaries and data-aware measures. In: Proc. of the 2006 IEEE Data Compression Conference (DCC ’06). (2006)
- Yanbin Sun, Yu Zhang, Binxing Fang, Hongli Zhang Succinct and practical greedy embedding for geometric routing Computer Communications, Volume 114, 1 December 2017, Pages 51-61
- Dekel Tsur Succinct data structures for nearest colored node in a tree Information Processing Letters, Volume 132, April 2018, Pages 6-10
- Sebastian Rudolph Succinctness and tractability of closure operator representations Theoretical Computer Science, Volume 658, Part B, 7 January 2017, Pages 327- 345