Semi Structured Data Model for Big Data (SS DMBD)
New business applications require flexibility in data model structure and must support the next generation of web applications and handle complex data types. The performance of processing structured data through a relational database has become incompatible with big data challenges. Nowadays, there is a need to deal with semi structured data with a flexible schema for different applications. Not only SQL (NoSQL) has been presented to overcome the limitations of relational databases in terms of scale, performance, data model, and distribution system. Also, NoSQL supports semi structured data and can handle a huge amount of data and provide flexibility in the data schema. But the data models of NoSQL systems are very complex, as there are no tools available to represent a scheme for NoSQL databases. In addition, there is no standard schema for data modelling of document oriented databases. This study proposes a semi structured data model for big data (SS DMBD) that is compatible with a document oriented database, and also proposes an algorithm for mapping the entity relationship (ER) model to SS DMBD. A case study is used to evaluate the SS DMBD and its features. The results show that this model can address most features of semi structured data.
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