Ahmedabad Professors Receive Funding From the Department of Science and Technology for Developing Data Analytics Architecture
13 June 2018
Ahmedabad Professors Receive Funding From the Department of Science and Technology for Developing Data Analytics Architecture
Professors at the School of Engineering and Applied Science Sanjay Chaudhary and Mehul S Raval have received funding for two years from the Department of Science and Technology to develop data analytics architecture applications in agriculture. Professor Chaudhary is the Principal Investigatorwhile Professor Mehul Raval is the Co-Investigator.
Geo-spatial data is very important to develop flexible and versatile functions and applications, for developing multidisciplinary applications, helping in planning, and managing and utilising 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 of the study are:
Development of big data integration and analytics platform built on open-source architecture to enrich large data sets, including geospatial databases. Agriculture will be used as the domain to realise various aspects of the proposed architecture.
Integration with already developed and deployed Decision Support Systems and Spatial Decision Support Systems for an application domain.
Indexing, querying, analysing, and visualising geospatial 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 the operational environment, e.g. applications like agro advisory systems should be able to process event streams in real-time, extract relevant information and identify values that do not follow the general trends.
Develop and implement a distributed algorithm(s) to store and process geospatial data.
Implement advanced data structures to store and efficiently process geospatial data.