FogGIS: Fog Computing for Geospatial Big Data Analytics

Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (09-11 December, 2016) Indian Institute of Technology (Banaras Hindu University) Varanasi, Indi

Geographical Information Systems: the past, present and future

The main challenges of the XXI century are caused by the large amount of geospatial information through a GIS. Throughout time there have been many attempts to define Geographic Information Systems (GIS). Yet there is still no consensus on its definition and to restrict it to one is limited. In the acronym - Geographic Information Systems - geographic refers to the Earth's surface and near-surface, therefore, all human production and activity, as well as non-human are possible to spatialize using GIS.info:eu-repo/semantics/publishedVersio

Software y aplicaciones en computación de altas prestaciones para el contexto de la UNdeC

En la actualidad, para abordar problemas de mayor tamaño y complejidad estudios de ciencia básica y aplicada utilizan Computación de Altas Prestaciones (HPC - High Performance Computing). El HPC permite mejorar la capacidad, velocidad y precisión en el procesamiento de datos. Con el proyecto que da origen a este trabajo se propone abordar seis estudios desde la perspectiva del HPC, para explorar los aspectos centrales del paralelismo aplicado desde las Ciencias de la Computación en otras disciplinas. Algunas de los estudios que se abordan ya vienen desarrollándose en la Universidad Nacional de Chilecito, otros se inician a partir de cooperaciones con otras instituciones o para formalizar trabajos finales de postgrado. En todos los casos, el HPC será abordado a través de un proceso metodológico organizado para: Consolidar una infraestructura de experimentación, desarrollo y producción de soluciones a problemas de HPC. Desarrollar las capacidades científico-tecnológicas del equipo. Fomentar la vinculación y transferencia con los sectores académico, social y productivo. Cada problema abordado reúne entre sus integrantes, investigadores especialistas en la disciplina del estudio, investigadores de Ciencias de la Computación y estudiantes en sus últimos años de formación de grado. Se espera consolidar a corto plazo un grupo de investigación, desarrollo y transferencia que generará oportunidades de formación de recursos humanos, proveerá de servicios a la comunidad en el área de estudio y potenciará los vínculos de cooperación con otras instituciones.Eje: Procesamiento Distribuido y Paralelo.Red de Universidades con Carreras en Informátic

Geographic Information Systems

One of the main challenges of the 21st century are caused by the large amount of geospatial information through a GIS. Throughout time there have been many attempts to define Geographic Information Systems (GIS). Yet there is no consensus on define it and restrict it to one definition is limited. In the acronym - Geographic Information Systems - the geographic refers to the Earth’s surface and near-surface, therefore, all human production and activity, and non-human are possible patialization in GIS.info:eu-repo/semantics/publishedVersio

Tecnología HPC como motor de ciencia de la UNdeC

En la actualidad, para abordar problemas de mayor tamaño y complejidad, los estudios de ciencia básica y aplicada utilizan Computación de Altas Prestaciones (HPC - High Performance Computing). El HPC permite mejorar la capacidad, velocidad y precisión en el procesamiento de datos. Con el proyecto que da origen a este trabajo se abordan seis estudios desde la perspectiva del HPC, para explorar los aspectos centrales del paralelismo aplicado desde las Ciencias de la Computación en otras disciplinas. Algunos de estos estudios se realizan exclusivamente en la Universidad Nacional de Chilecito, mientras que otros son en cooperación con otras instituciones nacionales y extranjeras. Entre estos, tres formalizan trabajos finales de postgrado. En todos los casos, el HPC será abordado a través de un proceso metodológico organizado para: ● Consolidar una infraestructura de experimentación, desarrollo y producción de soluciones a problemas de HPC ● Desarrollar las capacidades científico-tecnológicas del equipo ● Fomentar la vinculación y transferencia con los sectores académico, social y productivo Cada problema abordado reúne entre sus integrantes investigadores especialistas en la disciplina del estudio, investigadores de Ciencias de la Computación y estudiantes en sus últimos años de formación de grado. Con esto, se está consolidando un grupo de investigación, desarrollo y transferencia que generará oportunidades de formación de recursos humanos, proveerá de servicios a la comunidad en el área de estudio y potenciará los vínculos de cooperación con otras instituciones.Eje: Procesamiento distribuido y paralelo.Red de Universidades con Carreras en Informátic

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing led to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded computers are employed to increase the throughput and reduce latency at the edge of the network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built prototypes using Intel Edison and Raspberry Pi for studying the comparative performance. We conducted a case study on Malaria vector-borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression for reduced data transfer. Also, overlay analysis of geospatial data was implemented. In addition, we discussed energy savings, cost analysis and scalability of the proposed framework with respect to efficient data processing. We compared the performance of the proposed framework with the state-of-the-art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of the proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks

A review of the internet of floods : near real-time detection of a flood event and its impact

Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future

An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities

Rapid urbanization in developing countries has been observed to be relatively high in the last two decades, especially in the Asian and African regions. Although many researchers have made efforts to improve the understanding of the urbanization trends of various cities in Asia and Africa, the absence of platforms where local stakeholders can visualize and obtain processed urbanization data for their specific needs or analysis, still remains a gap. In this paper, we present an Internet-based GIS platform called MEGA-WEB. The Platform was developed in view of the urban planning and management challenges in developing countries of Asia and Africa due to the limited availability of data resources, effective tools, and proficiency in data analysis. MEGA-WEB provides online access, visualization, spatial analysis, and data sharing services following a mashup framework of the MEGA-WEB Geo Web Services (GWS), with the third-party map services using HTML5/JavaScript techniques. Through the integration of GIS, remote sensing, geo-modelling, and Internet GIS, several indicators for analyzing urbanization are provided in MEGA-WEB to give diverse perspectives on the urbanization of not only the physical land surface condition, but also the relationships of population, energy use, and the environment. The design, architecture, system functions, and uses of MEGA-WEB are discussed in the paper. The MEGA-WEB project is aimed at contributing to sustainable urban development in developing countries of Asia and Africa