Managing and 3D Visualization of Real-time Big Geo-referenced Data from Las Palmas Port through a Flexible Open Source Computer Architecture

Suárez, José P.; Trujillo, Agustín; Domínguez, Conrado; Santana, J.J.; Fernández, Pablo

doi:10.5220/0005371700720082

Cited by 4 publications

(8 citation statements)

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“…Furthermore, there is a significant presence of geospatial big data in the smart environment domain, in disaster monitoring (Fang et al, 2015), air quality management (Chinnaswamy et al, 2019;Xuyao, Hui, Kexin, Yijin, & Jinhang, 2013;Zou et al, 2021), and water and sewage management (Howell, Rezgui, & Beach, 2018). Other domains were also mentioned, such as logistics (Fernández et al, 2017;Fernández, Suárez, Trujillo, Domínguez, & Santana, 2018;Finogeev et al, 2019;Gupta, Sadana, & Gupta, 2020;Kang et al, 2016;Li et al, 2015;Suárez, Trujillo, Domínguez, & José Miguel Santana, 2015), culture and tourism (Benedusi, Chianese, Marulli, & Piccialli, 2015;Chianese, Marulli, Piccialli, Benedusi, & Jung, 2017;Li, Liao, & Huang, 2020;Mello et al, 2019), and smart water (Howell et al, 2018). Some works explained the type of geospatial data analyzed but did not specify the application domain.…”

Section: Decisionmentioning

confidence: 99%

“…The OGC (2000) proposed Geography Markup Language (GML; see also Luan et al, 2015;Cai et al, 2016). There is also applicability for JavaScript Object Notation (JSON; Benedusi et al, 2015;Chen et al, 2015;Chianese et al, 2017;Crockford, 2000;Gupta et al, 2020;Leung, Braun, & Cuzzocrea, 2019;Qader & Hristidis, 2017;Suárez et al, 2015), a format for storage and exchange of data; GeoJSON (Butler et al, 2016;Işikdağ, 2020;Kraft et al, 2019), a JSON variation oriented to the codification of geographic structures; and…”

Section: Rq3 What Are the Main Data Formats Stored?mentioning

confidence: 99%

“…Along with geospatial extensions of relational database management systems, geographic information systems such as ArcGIS For the processing of massive data, cloud computing has proved to be a viable alternative. Wu, Morandini, and Sinnott (2015) propose a generic and highly scalable cloud-based architecture for big data processing and apply it in a Fernández et al, 2017Fernández et al, , 2018Suárez et al, 2015) simplify the writing of MapReduce programs. Spark (Al Jawarneh et al, 2019;Limkar and Jha, 2019;Zaharia, Chowdhury, Franklin, Shenker, & Stoica, 2010) offers an engine for distributed massive data processing.…”

Section: Rq4 Which Artifacts Are Most Used To Deal With Geospatial Bi...mentioning

confidence: 99%

“…• José Pablo Suárez, Pablo Fernández, Agustín Trujillo, Conrado Domínguez, and José Miguel Santana (Fernández et al, 2017(Fernández et al, , 2018Suárez et al, 2015); Li Da Xu, Shifeng Fang, and Yunqiang Zhu (Fang et al, 2014;…”

Section: F I G U R E 3 Distribution Of Papers By Country Of the First...mentioning

confidence: 99%

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Applications of geospatial big data in the Internet of Things

Silva

Holanda

2021

Transactions in GIS

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The Internet of Things (IoT) has emerged as a communication paradigm that allows the interconnection of devices that interact with the environment through sensors and actuators, collect data from it, and transmit these data over a network without requiring human interaction (Armstrong, Wang, & Zhang, 2019). IoT architectures can demand database management systems capable of handling high data generation and transmission rates, bringing the research on the IoT closer to the studies on big data (Ge,

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Section: Decisionmentioning

confidence: 99%

Section: Rq3 What Are the Main Data Formats Stored?mentioning

confidence: 99%

Section: Rq4 Which Artifacts Are Most Used To Deal With Geospatial Bi...mentioning

confidence: 99%

Section: F I G U R E 3 Distribution Of Papers By Country Of the First...mentioning

confidence: 99%

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Applications of geospatial big data in the Internet of Things

Silva

Holanda

2021

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“…In this scenario, we have recently introduced the SmartPort project [ 5 ], a web platform that integrates the tools for the analysis and visualization of the sensor network of Las Palmas de Gran Canaria seaport. In that work, the project was firstly introduced explaining its design and the used technologies.…”

Section: Introductionmentioning

confidence: 99%

SmartPort: A Platform for Sensor Data Monitoring in a Seaport Based on FIWARE

Fernández

Santana

Ortega

et al. 2016

Sensors

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Seaport monitoring and management is a significant research area, in which infrastructure automatically collects big data sets that lead the organization in its multiple activities. Thus, this problem is heavily related to the fields of data acquisition, transfer, storage, big data analysis and information visualization. Las Palmas de Gran Canaria port is a good example of how a seaport generates big data volumes through a network of sensors. They are placed on meteorological stations and maritime buoys, registering environmental parameters. Likewise, the Automatic Identification System (AIS) registers several dynamic parameters about the tracked vessels. However, such an amount of data is useless without a system that enables a meaningful visualization and helps make decisions. In this work, we present SmartPort, a platform that offers a distributed architecture for the collection of the port sensors’ data and a rich Internet application that allows the user to explore the geolocated data. The presented SmartPort tool is a representative, promising and inspiring approach to manage and develop a smart system. It covers a demanding need for big data analysis and visualization utilities for managing complex infrastructures, such as a seaport.

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Design and evaluation of a scalable Internet of Things backend for smart ports

et al. 2021

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Internet of Things (IoT) technologies, when adequately integrated, cater for logistics optimisation and operations' environmental impact monitoring, both key aspects for today's EU ports management. This article presents Obelisk, a scalable and multi‐tenant cloud‐based IoT integration platform used in the EU H2020 PortForward project. The landscape of IoT protocols being particularly fragmented, the first role of Obelisk is to provide uniform access to data originating from a myriad of devices and protocols. Interoperability is achieved through adapters that provide flexibility and evolvability in protocol and format mapping. Additionally, due to ports operating in a hub model with various interacting actors, a second role of Obelisk is to secure access to data. This is achieved through encryption and isolation for data transport and processing, respectively, while user access control is ensured through authentication and authorisation standards. Finally, as ports IoTisation will further evolve, a third need for Obelisk is to scale with the data volumes it must ingest and process. Platform scalability is achieved by means of a reactive micro‐services based design. Those three essential characteristics are detailed in this article with a specific focus on how to achieve IoT data platform scalability. By means of an air quality monitoring use‐case deployed in the city of Antwerp, the scalability of the platform is evaluated. The evaluation shows that the proposed reactive micro‐service based design allows for horizontal scaling of the platform as well as for logarithmic time complexity of its service time.

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Managing and 3D Visualization of Real-time Big Geo-referenced Data from Las Palmas Port through a Flexible Open Source Computer Architecture

Cited by 4 publications

References 13 publications

Applications of geospatial big data in the Internet of Things

Applications of geospatial big data in the Internet of Things

SmartPort: A Platform for Sensor Data Monitoring in a Seaport Based on FIWARE

Design and evaluation of a scalable Internet of Things backend for smart ports

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