i4sea: a big data platform for sea area monitoring and analysis of fishing vessels activity

Tampakis, Panagiotis; Chondrodima, Eva; Tritsarolis, Andreas; Pikrakis, Aggelos; Theodoridis, Yannis; Pristouris, Kostis; Nakos, Harry; Kalampokis, Panagiotis; Dalamagas, Theodore

doi:10.1080/10095020.2021.1971055

Cited by 6 publications

(1 citation statement)

References 46 publications

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“…Some scholars have built a big data platform based on the lambda architecture and applied it in the energy field to realize the visual monitoring of residential power consumption and effectively improve energy utilization efficiency [ 8 ]. Not only that, but the big data platform based on the lambda architecture can also dynamically monitor and analyze marine activities and integrate various parameters [ 9 ]. In addition to it, cloud computing technology provides new solutions and computing potential for the storage and management of large-scale data, relying on the scientific Earth big data platform [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

Gao

Qing

et al. 2023

Computational Intelligence and Neuroscience

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Objective. This study attempts to build a big data platform for disease burden that can realize the deep coupling of artificial intelligence and public health. This is a highly open and shared intelligent platform, including big data collection, analysis, and result visualization. Methods. Based on data mining theory and technology, the current situation of multisource data on disease burden was analyzed. Putting forward the disease burden big data management model, functional modules, and technical framework, Kafka technology is used to optimize the transmission efficiency of the underlying data. This will be an efficient and highly scalable data analysis platform through embedding embedded Sparkmlib in the Hadoop ecosystem. Results. With the concept of “Internet + medical integration,” the overall architecture design of the big data platform for disease burden management was proposed based on the Spark engine and Python language. The main system composition and application scenarios are given at four levels: multisource data collection, data processing, data analysis, and the application layer, according to application scenarios and use requirements. Conclusion. The big data platform of disease burden management helps to promote the multisource convergence of disease burden data and provides a new path for the standardized paradigm of disease burden measurement. Provide methods and ideas for the deep integration of medical big data and the formation of a broader standard paradigm.

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Section: Introductionmentioning

confidence: 99%

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

Gao

Qing

et al. 2023

Computational Intelligence and Neuroscience

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Predicting Co-movement patterns in mobility data

et al. 2022

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Predictive analytics over mobility data is of great importance since it can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example is anticipated location prediction, where the goal is to predict the future location of a moving object, given a look-ahead time. What is even more challenging is to be able to accurately predict collective behavioural patterns of movement, such as co-movement patterns as well as their course over time. In this paper, we address the problem of Online Prediction of Co-movement Patterns. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates the comparison between the predicted clusters and the actual ones. Finally, we calculate the clusters’ evolution through time (survive, split, etc.) and compare the cluster evolution predicted by our framework with the actual one. Our experimental study uses two real-world mobility datasets from the maritime and urban domain, respectively, and demonstrates the effectiveness of the proposed framework.

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Rural Ecological Environment Monitoring and VR Visualization Analysis of Jilin Province Supported By Big Data

Ju,

Huang

2024

Procedia Computer Science

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i4sea: a big data platform for sea area monitoring and analysis of fishing vessels activity

Cited by 6 publications

References 46 publications

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

Design and Development of a Big Data Platform for Disease Burden Based on the Spark Engine

Predicting Co-movement patterns in mobility data

Rural Ecological Environment Monitoring and VR Visualization Analysis of Jilin Province Supported By Big Data

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