Abstract
Big Data is too large to be handled by traditional methods for analysis. It is a new ubiquitous term, which describes huge amount of data. Dealing with “Variety”, one of the five characteristics of Big Data is a great challenge. Variety means a range of formats such as structured tables, semi-structured log files, and unstructured text, audio, and video data. Every format of data has its unique framework for analyzing it. In this paper, we present a detailed study about various frameworks for analyzing structured, semi-structured, and unstructured data individually. In addition, some frameworks, which deal with all the three formats together, are also explained.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sagiroglu S, Sinang D (2013) Big data : a review, IEEE
Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Hung Byers A (2011) Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute
Laney D (2001) 3D data management: controlling data volume, velocity and variety, In: Application delivery stratergies, Meta Group
Gruska N, Martin P (2010) Integrating mapreduce and RDBMS. In: Proceedings of the 2010 conference of the center for advanced studies on collaborative research, pp 212–223
Marx V (2013) Biology: the big challenges of big data. Nature 498(7453):255–260
Dumbill E (2012) What is big data? an introduction to the big data landscape. Strata
Fadnavis RA, Tabhane S (2015) Big data processing using Hadoop. In: IJCSIT, vol 1
Park K, Nguyen MC, Won H (2015) Web based collaborative big data analytics on big data as a service platform. In: ICACT
Arora Y, Goyal D (2016) Big data: a review of analytics methods and techniques. In: 2nd international conference on contemporary computing and informatics. IEEE
Pavlo A, Paulson E, Rasin A (2009) A comparison of approaches to Large Scale Data Analysis, ACM
Cubranic D, Murphy GC, Singer J, Booth KS (2005) Hipikat: a project memory for software development. TSE 31(6):446–465
Meyer André, Fritz Thomas, Murphy Gail C, Zimmermann & Thomas, Software Developers’ Perceptions of Productivity, In: FSE, Nov 2914
Deligiannidis L, Kochut KJ, Sheth AP (2007) RDF data exploration and visualization. In: CIMS, pp 39–46. ACM
Hernandiz ME, Falconer SM (2008) Synchronized tag clouds for exploring semi structurd clinical trial data. In: Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds. ACM
Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods and analytics. Int J Inf Manage 35(2)
Bansal SK (2014) Towards a semantic extract transform load (ETL) framework for big data integration. IEEE
Wu X, Zhu X, Wu G, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107
Robinson EB, Lichtenstein P, Anckarsäter H, Happé F, Ronald A (2013) Examining and interpreting the female protective effect against autistic behavior, Proc Natl Acad Sci USA
Liu H, Liu Z, Yuan T, Yao Y (2014) Adaptively incremental dictionary compression method for column-oriented database, pp. 628–632
Gu J, Lin Z (2014) Implementation and evaluation of deep neural networks (DNN) on mainstream heterogeneous systems. In: Proceedings of the 5th Asia-Pacific workshop on systems
Kehrer J, Hauser H (2013) Visualization and visual analysis of multifaceted scientific data: a survey. IEEE Trans Vis Comput Graph 19(3):495–513
Risi M, Sessa MI, Tucci M, Tortora G (2014) CoDe modeling of graph composition for data warehouse report visualization. IEEE Trans Knowl Data Eng 26(3):563–576
Das TK, Mohan Kumar P (2013) Big data analytics: a framework for unstructured data analysis. IJET 5(1)
Lomotey RK, Deters R (2014) Analytics-as-a-Service (AaaS) tool for unstructured data mining. IEEE
Vashisht P, Gupta V (2015) Big data analytics techniques: a survey. IEEE
Greene GJ (2015) A generic framework for concept-based exploration of semistructured software engineering data. In: 30th IEEE/ACM international conference on automated software engineering (ASE). IEEE
Sindhu CS, Hedge NP (2013) A framework to handle data heterogeneity contextual to medical big data. IEEE
Chen Z, Zhong F, Yuan X, Hu Y (2016) Framework of integrated big data: a review. IEEE
Reddy V, Arnina Salim MS (2016) A comparative study of various clustering techniques on big data sets using Apache Mahout in 3D. In: MEC international conference on big data smart city. IEEE
Gharehchopogh FS, Khalifelu ZA (2011) Analysis and evaluation of unstructured data: text mining versus natural language processing. IEEE
Arora S, Chana I (2014) A survey of clustering techniques for big data analytics. In: 5th international conference—confluence the next generation information technology summit. IEEE
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Arora, Y., Goyal, D. (2019). Review of Data Analysis Framework for Variety of Big Data. In: Rathore, V., Worring, M., Mishra, D., Joshi, A., Maheshwari, S. (eds) Emerging Trends in Expert Applications and Security. Advances in Intelligent Systems and Computing, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-2285-3_7
Download citation
DOI: https://doi.org/10.1007/978-981-13-2285-3_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2284-6
Online ISBN: 978-981-13-2285-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)