Modeling Sparse and Evolving Data
Existing relational database management system (RDBMS) excels in providing transactional support. However, RDBMS performance declines when sparse and evolving data needs to be stored. Modeling of highly evolving and sparse data is a major issue that needs attention to provide faster and competent technology solutions. This research work is focused on providing a solution to handle sparseness and frequent evolution of data with adherence for the transactional support. Recently, authors propose an extension of binary table approach to overcome the lacking aspects. The proposed approach is termed as Multi Table Entity Attribute Value (MTEAV) model. To make users completely unaware about the underlying modeling approach, MTEAV is augmented with a translation layer. It translates conventional SQL query (as per the relational model) to a new SQL query (as per MTEAV structure) to provide the user friendly environment. In this research, authors extend the functionality of the translation layer to provide support for data definition (creating, reading, updating and deleting schema). Authors have experimented MTEAV for analyzing the effect of sparseness on the performance of MTEAV. Results achieved clearly indicate that the MTEAV performance increases with increase in sparseness.
KeywordsAttribute centric query Entity centric query Data models Frequent evolution Sparseness Storage
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