Bivariate, Cluster and Suitability Analysis of NoSQL Solutions for Different Application Areas
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Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for development of an optimized solution to a specific real world problem, big data systems are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL is the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. Moreover, the requirements of different applications vary on the basis of budget and functionality. This paper presents a feature analysis of 80 NoSQL solutions, elaborating on the criteria and points that a developer must consider while making a possible choice. Bivariate analysis of dataset created for the identified NoSQL solutions was performed to establish relationship between 9 features. Furthermore, cluster analysis of the dataset was used to create categories of solutions to present a statistically supported classification scheme. Finally, applications for different solutions were reviewed and classified under domain-specific categories. Random forest classification was used to determine the most relevant features for applications and correspondingly a decision tree-based prediction model was proposed, implemented and deployed in the form of a web application to determine the suitability of a NoSQL solution for an application area.
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