Authors: Sugam Sharma; Udoyara Sunday Tim; Shashi Gadia; Johnny Wong; Ritu Shandilya; Sateesh K. Peddoju
Addresses: Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa, USA ' Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa, USA ' Department of Computer Science, Iowa State University, Ames, Iowa, USA ' Department of Computer Science, Iowa State University, Ames, Iowa, USA ' School of Computing and Engineering, University of Missouri, Kansas City, USA ' Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Abstract: In the data science age, the decision-making processes are largely data dependent. Though, the concept of big data is in the midst of evolution with great research and business opportunities, the challenges are enormous and growing equally too. This motivates various scientific disciplines to conglomerate their efforts for deep exploration of all dimensions of big data to procure evolutionary outcomes. The existing data models are largely unable to illuminate the full potential of big data. The existing computation capacity falls short for the increasingly expanded storage capacity. The fast-paced volume expansion of the unorganised data entails a complete paradigm shift in new age data computation and witnesses the evolution of new capable data engineering techniques. In this paper, we provide the first level classification for some modern, leading NoSQL representatives. Also, the classification is further strengthened by the intra-class and inter-class comparisons and discussions of the undertaken models.
Keywords: big data; NoSQL databases; data modelling; data science; classification.
International Journal of Big Data Intelligence, 2015 Vol.2 No.3, pp.201 - 221
Available online: 11 Jul 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article