International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 10, No. 11, 8 Nov. 2018
Cover page and Table of Contents: PDF (size: 554KB)
Sorted r-Train: An Improved Dynamic Data Structure for Handling Big Data
Full Text (PDF, 554KB), PP.27-35
Views: 0 Downloads: 0
Author(s)
Mohd Abdul Ahad
1,*
Ranjit Biswas
1
Index Terms
r-Train, trie, HAT, Linked List, Arrays, Big Data, larray
Abstract
In today’s computing era, the world is dealing with big data which has enormously expanded in terms of 7Vs (volume, velocity, veracity, variability, value, variety, visualization). The conventional data structures like arrays, linked list, trees, graphs etc. are not able to effectively handle these big data. Therefore new and dynamic tools and techniques which can handle these big data effectively and efficiently are the need of the hour. This paper aims to provide an enhancement to the recently proposed “dynamic” data structure “r-Train” for handling big data. With the emergence of the “Internet of Things (IoT)” technology, real-time handling of requests and services are pivotal. Therefore it becomes necessary to promptly fetch the required data as and when required from the enormous piles of big data that are generally located at different sites. Therefore an effective searching and retrieval mechanism must be provided that can handle these challenging issues. The primary aim of this proposed refinement is to provide an effective means of insertion, deletion and searching techniques to efficiently handle the big data.
Cite This Paper
Mohd Abdul Ahad, Ranjit Biswas, "Sorted r-Train: An Improved Dynamic Data Structure for Handling Big Data", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.11, pp.27-35, 2018. DOI:10.5815/ijisa.2018.11.04
Reference
[1]Biswas, Ranjit. "“Atrain Distributed System”(ADS): An Infinitely Scalable Architecture for Processing Big Data of Any 4Vs." In Computational Intelligence for Big Data Analysis, pp. 3-54. Springer, Cham, 2015.
[2]Biswas, Ranjit. "Heterogeneous Data Structure “r-Atrain”." In Global Trends in Intelligent Computing Research and Development, pp. 338-359. IGI Global, 2014. doi:10.4018/978-1-4666-4936-1.ch012
[3]Biswas, Ranjit. "Introducing Data Structures for Big Data." In Effective Big Data Management and Opportunities for Implementation, pp. 25-52. IGI Global, 2016.
[4]Biswas, Ranjit. "Data Structures for Big Data." International Journal Computing and Optimization, no. 2, 73-93 (204).
[5]Biswas, R.,Processing of Heterogeneous Big Data in an Atrain Distributed System (ADS) Using the Heterogeneous Data Structure r-Atrain. International Journal Computing and Optimization, 1(1), pp.17-45, (2014). doi: http://dx.doi.org/10.12988/ijco.2014.445
[6]Sleator, Daniel Dominic, and Robert Endre Tarjan. "Self-adjusting binary search trees." Journal of the ACM (JACM) 32, no. 3 (1985): 652-686. dio:http://dx.doi.org/10.1145/3828.3835
[7]G.M.Velskii & E.M. Landis. An algorithm for the organization of information. Soviet Mathemtics Doklady 3:1259-1263, 1962.
[8]H. Cai, B. Xu, L. Jiang and A. V. Vasilakos, "IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges," in IEEE Internet of Things Journal, vol. 4, no. 1, pp. 75-87, Feb. 2017. doi: 10.1109/JIOT.2016.2619369
[9]Askitis, Nikolas, and Ranjan Sinha. "HAT-trie: a cache-conscious trie-based data structure for strings." In Proceedings of the thirtieth Australasian conference on Computer science-Volume 62, pp. 97-105. Australian Computer Society, Inc., 2007.
[10]He, Xing, Qian Ai, Robert Caiming Qiu, Wentao Huang, Longjian Piao, and Haichun Liu. "A big data architecture design for smart grids based on random matrix theory." IEEE transactions on smart Grid, 8, no. 2 (2017): 674-686.
[11]M. Tahmassebpour, "A New Method for Time-Series Big Data Effective Storage," in IEEE Access, vol. 5, pp. 10694-10699, 2017. doi: 10.1109/ACCESS.2017.2708080
[12]Bertil Schmidt, Andreas Hildebrandt, Next-generation sequencing: big data meets high performance computing, Drug Discovery Today, Volume 22, Issue 4, 2017, Pp: 712-717, doi: https://doi.org/10.1016/j.drudis.2017.01. 014.
[13]Sitarski, E.: HATs: Hashed array trees. Dr. Dobb’s Journal 21(11) (1996), http://www.ddj.com/architect/184409965?pgno=5
[14]Park, Eunhui, and David M. Mount. "A self-adjusting data structure for multidimensional point sets." In European Symposium on Algorithms, pp. 778-789. Springer, Berlin, Heidelberg, 2012. doi =http://dx.doi.org/10.1007/978-3-642-33090-2_67
[15]Gu, Min, Xiangping Li, and Yaoyu Cao. "Optical storage arrays: a perspective for future big data storage." Light: Science & Applications, 3, no. 5 (2014): e177.
[16]Strohbach M., Daubert J., Ravkin H., Lischka M. Big Data Storage. In: Cavanillas J., Curry E., Wahlster W. (eds) New Horizons for a Data-Driven Economy. 2016, Springer, Cham
[17]Van Doren, James R., and Joseph L. Gray. "An algorithm for maintaining dynamic AVL trees." In Information Systems, pp. 161-180. Springer, Boston, MA, 1974.
[18]Berman, Jules J. Principles of big data: preparing, sharing, and analyzing complex information. Newnes, 2013.
[19]Feinleib, David. Big Data Demystified: How Big Data is Changing the Way We Live, Love, and Learn. Big Data Group, 2013.
[20]Needham, J.: Disruptive Possibilities: How Big Data Changes Everything. O’reilly Publisher, Cambridge (2013)
[21]Simon, P.: Too Big to Ignore: The Business Case for Big Data. John Wiley & Sons, New Jersey (2013)
[22]Makrufa Sh. Hajirahimova, Aybeniz S. Aliyeva, "About Big Data Measurement Methodologies and Indicators", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.10, pp. 1-9, 2017.DOI: 10.5815/ijmecs.2017.10.01
[23]Rasim M. Alguliyev, Rena T. Gasimova, Rahim N. Abbasl ,"The Obstacles in Big Data Process", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.4, pp.31-38, 2017. dio: 10.5815/ijitcs.2017.04.05
[24]Entesar Althagafy, M. Rizwan Jameel Qureshi,"Novel Cloud Architecture to Decrease Problems Related to Big Data", International Journal of Computer Network and Information Security (IJCNIS), Vol.9, No.2, pp.53-60, 2017. doi: 10.5815/ijcnis.2017.02.07
[25]Mai Abdrabo, Mohammed Elmogy, Ghada Eltaweel, Sherif Barakat,"Enhancing Big Data Value Using Knowledge Discovery Techniques", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.8, pp.1-12, 2016. doi: 10.5815/ijitcs.2016.08.01
[26]PankajDeep Kaur, Awal Adesh Monga,"Managing Big Data: A Step towards Huge Data Security", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.6, No.2, pp.10-20, 2016. doi: 10.5815/ijwmt.2016.02.02
[27]Rohit More, R H Goudar,"DataViz Model: A Novel Approach towards Big Data Analytics and Visualization", International Journal of Engineering and Manufacturing(IJEM), Vol.7, No.6, pp.43-49, 2017. doi: 10.5815/ijem.2017.06.04
[28]Liang, Kaitai, Willy Susilo, and Joseph K. Liu. "Privacy-preserving ciphertext multi-sharing control for big data storage." IEEE transactions on information forensics and security, 10.8, (2015): 1578-1589.
[29]Alam, B.: Matrix Multiplication using r-Train Data Structure. In: AASRI Conference on Parallel and Distributed Computing Systems, AASRI (Elsevier) Procedia 5, 189–193 (2013), doi: 10.1016/j.aasri.2013.10.077
[30]Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press and McGraw-Hill (2001)
[31]Xiong, Qing, Chanle Wu, Jianbing Xing, Libing Wu, and Huyin Zhang. “A linked-list data structure for advance reservation admission control.” In Networking and mobile computing, pp. 901-910. Springer, Berlin, Heidelberg, 2005.