Modeling temporal aspects of sensor data for MongoDB NoSQL database

Proliferation of structural, semi-structural and no-structural data, has challenged the scalability, flexibility and processability of the traditional relational database management systems (RDBMS). The next generation systems demand horizontal scaling by distributing data over autonomously addable nodes to a running system. For schema flexibility, they also want to process and store different data formats along the sequence factor in the data. NoSQL approaches are solutions to these, hence big data solutions are vital nowadays. But in monitoring scenarios sensors transmit the data continuously over certain intervals of time and temporal factor is the main property of the data. Therefore the key research aspect is to investigate schema flexibility and temporal data integration aspects together. We need to know that: what data modelling should we adopt for a data driven real-time scenario; that we could store the data effectively and evolve the schema accordingly during data integration in NoSQL environments without losing big data advantages. In this paper we explain a middleware based schema model to support the temporal oriented storage of real-time data of ANT+ sensors as hierarchical documents. We explain how to adopt a schema for the data integration by using an algorithm based approach for flexible evolution of the model for a document oriented database, i.e, MongoDB. The proposed model is logical, compact for storage and evolves seamlessly upon new data integration.