Hybrid Mist-Cloud Systems for Large Scale Geospatial Big Data Analytics and Processing: Opportunities and Challenges

dc.contributor.authorBarik, Rabindra Kumar
dc.contributor.authorMisra, Chinmaya
dc.contributor.authorLenka, Rakesh K.
dc.contributor.authorDubey, Harishchandra
dc.contributor.authorMankodiya, Kunal
dc.contributor.utdAuthorDubey, Harishchandra
dc.descriptionDue to copyright restrictions and/or publisher's policy full text access from Treasures at UT Dallas is not available. UTD affiliates may be able to acquire a copy through Interlibrary Loan by using the link to UTD ILL.
dc.description.abstractThe cloud and fog computing paradigms are developing area for storing, processing, and analysis of geospatial big data. Latest trend is mist computing which boost fog and cloud concepts for computing process where edge devices are used to help increase throughput and reduce latency to support at client edge. The present research article discussed the mist computing emergence for geospatial analysis of data from various geospatial applications. It also created a framework based on mist computing, i.e., MistGIS for analytics in mining domain from geospatial big data. The developed MistGIS platform is used in Tourism Information Infrastructure Management and Faculty Information Retrial System. Tourism Information Infrastructure Management is to assimilate entire geospatial data in context to travel/tourism places constitute of various lakes, mountains, rivers, forests, temples, mosques, churches, monuments, etc. It can aid all the stakeholders or users to acquire sufficient data in subsequent research studies. In this study, it has taken the Temple City of India, Bhubaneswar as the case study. Whereas Faculty Information Retrial System facilitated many functionalities with respect to finding the detail information of faculties according to their research area, contact details, and email ids, etc in all 31 National Institutes of Technology (NITs) in India. The framework is built with the Raspberry Pi microprocessor. The MistGIS platform has been confirmed by prelude analysis which includes cluster and overlay. The outcome show that mist computing assist cloud and fog computing to provide the analysis of geospatial big data.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.description.departmentCenter for Robust Speech Systems
dc.identifier.bibliographicCitationBarik, Rabindra Kumar, Chinmaya Misra, Rakesh K. Lenka, Harishchandra Dubey, et al. 2019. "Hybrid mist-cloud systems for large scale geospatial big data analytics and processing: opportunities and challenges." Arabian Journal of Geosciences 12: art. 32, doi: 10.1007/s12517-018-4104-3
dc.publisherSpringer Heidelberg
dc.rights©2019 Springer Nature
dc.source.journalArabian Journal of Geosciences
dc.subjectCloud computing
dc.subjectGeospatial data
dc.titleHybrid Mist-Cloud Systems for Large Scale Geospatial Big Data Analytics and Processing: Opportunities and Challenges


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