Sanjay Chaudhary and Mehul Raval receive funding from Department of Science and Technology received for a research project on 'Developing Data Analytics Architecture'
Sanjay Chaudhary and Mehul Raval receive funding from Department of Science and Technology received for a research project on 'Developing Data Analytics Architecture'
Professor Sanjay Chaudhary
Dr Mehul Raval
ABSTRACT
1. Title: Developing Data Analytics Architecture Applications in Agriculture 2. Principal Investigator: Professor Sanjay Chaudhary 3. Co-Investigator : Dr Mehul Raval 4. Funding Organization: Department of Science and Technology, New Delhi, (NRDMS - NSDI) 5. Duration: Two years, June 2017 to May 2019 6. Project Summary: Geo-spatial data is very important to develop flexible and versatile functions and applications, for developing multidisciplinary applications, helping in planning, managing and utilizing natural resources efficiently using spatial analysis. We need common standards for data integration and effective analytical infrastructure and we aim to use OGC standards for big data processing of spatial data. The core scientific and technological objectives are:
Development of big data integration and analytical platform built on open source architecture for enrichment of large data sets including geo-spatial data bases. Agriculture will be used as the domain to realize various aspects of proposed architecture.
Integration with already developed and deployed Decision Support Systems andSpatial Decision Support Systems for an application domain.
Indexing, querying, analyzing, and visualizing Geo-spatial data at scale using open source.
To connect structured and unstructured data in real time or batch processing for data management and analytics of systems for decision making in operational environment, e.g applications like agro advisory system should be able to process event streams in a real time, extract relevant information and identify values that do not follow the general trends.
Develop and implement distributed algorithm(s) to store and process geo-spatial data.
Implement advanced data structures to store and process geo-spatial data in an efficient manner.