FogLearn: Leveraging Fog-based Machine Learning for Smart System Big Data Analytics

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This paper discussed the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This paper proposed and developed fog computing based framework i.e. FogLearn for application of K-means clustering in Ganga River Basin Management and realworld feature data for detecting diabetes patients suffering from diabetes mellitus. Proposed architecture employed machine learning on deep learning framework for analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results showed that fog computing hold an immense promise for analysis of medical and geospatial big data

Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data

Big Earth Data has experienced a considerable increase in volume in recent years due to improved sensing technologies and improvement of numerical-weather prediction models. The traditional geospatial data analysis workflow hinders the use of large volumes of geospatial data due to limited disc space and computing capacity. Geospatial web service technologies bring new opportunities to access large volumes of Big Earth Data via the Internet and to process them at server-side. Four practical examples are presented from the marine, climate, planetary and earth observation science communities to show how the standard interface Web Coverage Service and its processing extension can be integrated into the traditional geospatial data workflow.Web service technologies offer a time- and cost-effective way to access multi-dimensional data in a user-tailored format and allow for rapid application development or time-series extraction. Data transport is minimised and enhanced processing capabilities are offered. More research is required to investigate web service implementations in an operational mode and large data centres have to become more progressive towards the adoption of geo-data standard interfaces. At the same time, data users have to become aware of the advantages of web services and be trained how to benefit from them most

Development of Distributed Research Center for analysis of regional climatic and environmental changes

We present an approach and first results of a collaborative project being carried out by a joint team of researchers from the Institute of Monitoring of Climatic and Ecological Systems, Russia and Earth Systems Research Center UNH, USA. Its main objective is development of a hardware and software platform prototype of a Distributed Research Center (DRC) for monitoring and projecting of regional climatic and environmental changes in the Northern extratropical areas. The DRC should provide the specialists working in climate related sciences and decision-makers with accurate and detailed climatic characteristics for the selected area and reliable and affordable tools for their in-depth statistical analysis and studies of the effects of climate change. Within the framework of the project, new approaches to cloud processing and analysis of large geospatial datasets (big geospatial data) inherent to climate change studies are developed and deployed on technical platforms of both institutions. We discuss here the state of the art in this domain, describe web based information-computational systems developed by the partners, justify the methods chosen to reach the project goal, and briefly list the results obtained so far

Development of artificial intelligence models for the enrichment and exploitation of geospatial data in the built environment

Geospatial data treatment is an important task since it is a big part of big data. Nowadays, geospatial data exploitation is lacking in terms of artificial intelligence. In this work, we focus on the usage of a machine learning models to exploit geospatial data. We will follow a complete workflow from the collection and first descriptive analysis of the data to the development and evaluation of the different machine learning algorithms. From download dataset we will predict if the download will lead to civil work, in other words, it is a classification problem. We conclude that combining machine learning and geospatial data we can get a lot out of it

Spatial data science for sustainable mobility

The constant rise of urban mobility and transport has led to a dramatic increase in greenhouse gas emissions. In order to ensure livable environments for future generations and counteract climate change, it will be necessary to reduce our future CO2 footprint. Spatial data science contributes to this effort in major ways, also fuelled by recent progress regarding the availability of spatial big data, computational methods and geospatial technologies. This paper demonstrates important contributions from Spatial data science to mobility pattern analysis and prediction, context integration, and the employment of geospatial technologies for changing people\u27s mobility behavior. Among the interdisciplinary research challenges that lie ahead of us are an enhanced public availability of mobility studies and their data sets, improved privacy protection strategies, spatially-aware machine learning methods, and evaluating the potential for people\u27s long-term behavior change towards sustainable mobility