Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.

With the outbreak of COVID-19 pandemic, several educational and business organizations have adopted online video conferencing platforms to facilitate their smooth functioning. These platforms have largely favored educational institutions for remotely conducting virtual classes over various electronic learning (e-learning) platforms. This has imposed challenges on existing Internet platforms for efficiently managing network performance generated from the extensive use of these platforms. In this paper, a framework for monitoring the effect of different network parameters, while using some well-known e-learning platforms is provided. The different LTE network performance parameters like uplink speed, downlink speed, network latency, and jitter are considered. We observed that the LTE network performance parameters obtained while using Microsoft Teams is optimum. Since, we observed that Microsoft Teams provided an optimal performance, we further perform analysis on Microsoft Teams. We further obtain the empirical distribution pertaining to the LTE network communication features by employing Gamma distribution. From our analysis, it is observed that the network throughput, latency, and jitter can be optimized to enhance the users' quality of experience by employing the proposed strategy.

To visualize high-dimensional geospatial data has achieved much importance in last decades. But to analyze it, the technologies used of machine learning are not so convincing and thus it is high time to switch to a sub-domain of machine learning called deep learning, which has gained popularity because of its accuracy in processing and analyzing high-dimensional data. The convergence of deep learning with geospatial data analytics shall prove to be a boon to those who actually has a need to predict specific outputs over geospatial data. In this paper, we have presented some geospatial data generated using the GIS (Geographic Information Systems) technology and proposed ways to implement deep learning over these data. GIS technology is a mapping technology which signifies high-dimensional geospatial data and our aim is to propose a model where GIS converges with highly efficient deep neural networks such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network) and LSTM (Long Short-Term Memory Network). We have provided geospatial as well as statistical results in this paper to visualize practically the GIS technology. This paper further provides future scope of the proposed model which shall present the challenges that needs to be tackled in future and its applicability in many relevant domains.