Abstract
Abstract The requirement of applications to change their structure and schema, in recent times has been more than it has been in the past. With data being in big volumes, high level of sparseness and different varieties, the uncertainty of having a predefined schema calls for inventing and coming up with an effective way to handle data. Not only is the problem restricted to modification of the schema, it also penetrates the data stored within the schema, and the queries posed on the schema. This necessitates the need for having a crystal-clear understanding of how and where to store data depending on its kind. This study aims at critically analyzing data storage approaches by considering various parameters such as schema evolution, sparseness and query execution time since state of the art applications that are up and coming demand data schemas that are populated sparsely, evolving continuously and are rapid in responding to queries. For this, we have considered both Schema-based and Schema-less Techniques.
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