A Model Architecture for Big Data Applications using Relational Databases
Effective Big Data applications dynamically handle the retrieval of decisioned results based on stored large datasets efficiently. One effective method of requesting decisioned results, or querying, large datasets is the use of SQL and database management systems such as MySQL. But a problem with using relational databases to store huge datasets is the decisioned result retrieval time, which is often slow largely due to poorly written queries / decision requests. This work presents a model to re-architect Big Data applications in order to efficiently present decisioned results: lowering the volume of data being handled by the application itself, and significantly decreasing response wait times while allowing the flexibility and permanence of a standard relational SQL database, supplying optimal user satisfaction in today's Data Analytics world. In this paper we review a Big Data case study in the telecommunications field and use it to experimentally demonstrate the effectiveness of our approach.
Durham, Erin-Elizabeth A., Andrew Rosen, and Robert W. Harrison. 2014. "A model architecture for Big Data applications using relational databases." Conference Proceeding - 2014 IEEE International Conference on Big Data (Big Data), pp. 9-16.