Web-Based GIS Through a Big Data Open Source Computer Architecture for Real Time Monitoring Sensors of a Seaport

Fernández, Pablo; Santana, J.J.; Ortega, Sebastián; Trujillo, Agustín; Suárez, José P.; Santana, Jaisiel; Sánchez, Alejandro; Domínguez, Conrado

doi:10.1007/978-3-319-45123-7_4

Cited by 5 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A solution for real time monitoring of sensor data in a seaport infrastructure implemented in the Puerto de La Luz seaport in the Canary Islands is described in [9]. The system integrates data from AIS, various sensors and external sources, and provides a 3D map showing the ingoing and outgoing vessels, as well as the environmental conditions.…”

Section: Safety Improvement / Unapređenje Sigurnostisupporting

confidence: 46%

Veliki skupovi podataka u pomorskoj industriji

Mirović¹,

Miličević²,

Obradovic³

2018

Naše more

View full text Add to dashboard Cite

SummaryMaritime industry is a complex system that requires a quick adaptation to changing conditions and in which decision-making needs to take into account a large number of parameters. As navigation systems become more advanced, there is a significant amount of ship performance and navigation data generated. Big Data analytics tools make it possible to analyze these large quantities of data in order to gain the insight that supports decision-making. This paper gives an overview of the applications of Big Data in maritime industry, specifically in logistics optimization, safety and energy efficiency improvement, as well as the challenges that systems involving Big Data face. SažetakPomorska industrija je složeni sustav koji zahtijeva brzu prilagodbu u promjenjivim uvjetima u kojima je potrebno uzeti u obzir velik broj parametara prilikom donošenja odluka. Napretkom navigacijskih sustava, generira se znatna količina podataka o performansama broda i navigaciji. Analitički alati za velike skupove podataka omogućuju analizu tih podataka kako bi se dobilo razumijevanje potrebno za podršku donošenju odlukâ. Ovaj članak daje pregled primjene velikih skupova podataka u pomorstvu, posebno u optimizaciji logistike, sigurnosti i poboljšanju energetske učinkovitosti, kao i izazove s kojima se suočavaju sustavi koji koriste velike skupove podataka. KEY

show abstract

Section: Safety Improvement / Unapređenje Sigurnostisupporting

confidence: 46%

Veliki skupovi podataka u pomorskoj industriji

Mirović¹,

Miličević²,

Obradovic³

2018

Naše more

View full text Add to dashboard Cite

show abstract

“…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%

“…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%

“…Yu, Yao, Tianrong, Li, and Ma (2019) present a 3D visualization platform for transmission cables based on massive spatiotemporal data, supporting the use of virtual reality glasses. The implementation of SmartPort, a system for management and 3D visualization of heterogeneous sensor data from the port of Las Palmas in Gran Canaria, is described in some of the mapped works (Fernández et al, 2017(Fernández et al, , 2018Suárez et al, 2015). Lwin et al (2019) explore the creation of geospatial dashboards to collect, manipulate, and share data from geographically dispersed smart city objects, and visualize it in rich user interfaces.…”

Section: G06 Geovisualizationmentioning

confidence: 99%

See 2 more Smart Citations

Applications of geospatial big data in the Internet of Things

Silva

Holanda

2021

Transactions in GIS

View full text Add to dashboard Cite

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,

show abstract