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

Barik, Rabindra K.; Dubey, Harishchandra; Mankodiya, Kunal; Sasane, Sapana Ashok; Misra, Chinmaya

doi:10.1007/s12652-018-0702-x

Cited by 65 publications

(37 citation statements)

References 69 publications

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“…Some of the benefits of developing an SDI are improved access to data, reduced duplication of effort in collecting and maintaining data, better availability of data, and interoperability between datasets. Although SDI development is an accepted task for most organizations and nations, and examples exist for various problem domains such as topographic mapping, indoor, health, marine, arctic, and so forth [65][66][67][68][69][70][71][72][73], attempts at developing data models for the improvement of disability rights are new within the literature [74]. As explained by Rajabifard et al [63], an SDI can be an enabling platform for sustainable development challenges.…”

Section: Use Of Geoinformation Tools and Technologies As Enablers Formentioning

confidence: 99%

Improvement of Disability Rights via Geographic Information Science

Kocaman

Özdemir

2020

Sustainability

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Rights, legal regulations, and practices often arise from societal and scientific developments, and societal transformations may originate from new legal regulations as well. Basic rights can be re-defined with advancements in science and technology. In such an evolutional loop, where mutual supply is obvious, combined legal and technological frameworks should be exercised and developed for practicing human rights. The main aim of this article is to propose a conceptual and methodological framework for the improvement of disability rights in the light of recent advancements in geographic information science (GIScience), in particular for those with motor disabilities, for whom questions related to “where” are essential. The concept of disability is discussed, considering different aspects, and a new methodological framework is proposed in which Geographic Information Systems (GIS), volunteered geographic information (VGI) and citizen science are at the core. In order to implement the framework at the national and international levels, a spatial data model should be developed first. The new data collection and interpretation approaches based on VGI, citizen science, and machine learning methods may help to realize equal rights for people with motor disabilities, by enabling improved access to education, health, and travel.

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Section: Use Of Geoinformation Tools and Technologies As Enablers Formentioning

confidence: 99%

Improvement of Disability Rights via Geographic Information Science

Kocaman

Özdemir

2020

Sustainability

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“…Cisco defined fog computing as a distributed paradigm that provides cloud-like services to the edge of the network. They have been applied in various areas like the analysis of geospatial data [32][33][34], designing augmented reality-based gaming applications [35], smart city applications like vehicular network management [36,37] for smart parking, and sensor data analysis [38][39][40][41][42][43].…”

Section: Related Workmentioning

confidence: 99%

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

2018

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The use of wearable and Internet-of-Things (IoT) for smart and affordable healthcare is trending. In traditional setups, the cloud backend receives the healthcare data and performs monitoring and prediction for diseases, diagnosis, and wellness prediction. Fog computing (FC) is a distributed computing paradigm that leverages low-power embedded processors in an intermediary node between the client layer and cloud layer. The diagnosis for wellness and fitness monitoring could be transferred to the fog layer from the cloud layer. Such a paradigm leads to a reduction in latency at an increased throughput. This paper processes a fog-based deep learning model, DeepFog that collects the data from individuals and predicts the wellness stats using a deep neural network model that can handle heterogeneous and multidimensional data. The three important abnormalities in wellness namely, (i) diabetes; (ii) hypertension attacks and (iii) stress type classification were chosen for experimental studies. We performed a detailed analysis of proposed models’ accuracy on standard datasets. The results validated the efficacy of the proposed system and architecture for accurate monitoring of these critical wellness and fitness criteria. We used standard datasets and open source software tools for our experiments.

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“…The term “big geospatial data,” also referred to as “big spatiotemporal data” (Comber & Wulder, 2019) or “big Earth data” (Guo et al, 2017), is an emerging paradigm for the amount of geographic and spatial information becoming available every second around the world (Barik, Dubey, Mankodiya, Sasane, & Misra, 2019; Krämer & Senner, 2015).…”

Section: Introductionmentioning

confidence: 99%

A proposal to minimize the cost of processing big geospatial data in public cloud providers

Bachiega

Holanda

Araújo

2021

Transactions in GIS

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Spatial data represent abstractions of real‐world entities and can be obtained in various ways. They have properties that differentiate them from other types of data, such as a complex structure and dynamism. In recent years with the increasing volume of spatial data, referred to as “big geospatial data,” some tools have been developed to process such data efficiently, such as SpatialHadoop. The use of appropriate data indices based on the data set to be processed, as well as queries and operations to be performed, is essential for the optimal performance of these applications. In particular, since public cloud providers’ charges are based on the resources used, it is imperative to optimize application execution in order to avoid unnecessary expense. This article proposes the use of six conditions that seek to minimize the cost of processing big geospatial data in public cloud providers. The tests performed demonstrate that the use of these conditions and the choice of the lowest‐cost provider can reduce the total processing cost by up to 41%.

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

Cited by 65 publications

References 69 publications

Improvement of Disability Rights via Geographic Information Science

Improvement of Disability Rights via Geographic Information Science

DeepFog: Fog Computing-Based Deep Neural Architecture for Prediction of Stress Types, Diabetes and Hypertension Attacks

A proposal to minimize the cost of processing big geospatial data in public cloud providers

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