Big Data Analytics Framework for Spatial Data

In the world of mobile and Internet, large volume of data is generated with spatial components. Modern users demand fast, scalable and cost-effective solutions to perform relevant analytics on massively distributed data including spatial data. Traditional spatial data management systems are becoming less efficient to meet the current users demand due to poor scalability, limited computational power and storage. The potential approach is to develop data intensive spatial applications on parallel distributed architectures deployed on commodity clusters. The paper presents an open-source big data analytics framework to load, store, process and perform ad-hoc query processing on spatial and non-spatial data at scale. The system is built on top of Spark framework with a new input data source NoSQL database i.e. Cassandra. It is implemented by performing analytics operations like filtration, aggregation, exact match, proximity and K nearest neighbor search. It also provides an application architecture to accelerate ad-hoc query processing by diverting user queries to the suitable framework either Cassandra or Spark via a common web based REST interface. The framework is evaluated by analyzing the performance of the system in terms of latency against variable size of data.