Spatiotemporal Big Data Analytics

Dear Colleagues,

In many fields, data contains both space and time information. However, jointly considering both dimensions in data analysis raises new challenges that many traditional data mining and machine learning techniques are unable to cope with. Mining spatiotemporal databases can provide useful insights for many real-world applications such as e-commerce, Internet of Things, agriculture, healthcare, intelligent transportation systems, meteorology, and astronomy. For instance, in the intelligent transportation systems, spatiotemporal big data analytics can help to detect, control, and monitor the set of road segments in which congestion may regularly happen in a transportation network. In meteorology, spatiotemporal big data analytics can help to detect the geographical regions which are regularly prone to droughts. In the Internet of Things, spatiotemporal big data analytics can help to detect, control, and monitor the nearby areas where people are regularly exposed to harmful levels of air pollution.  

This Special Issue is intended to report high-quality research on recent advances on spatiotemporal big data analytics, more specifically the state-of-the-art algorithms, models, methodologies, and systems for handling spatiotemporal data. Authors are solicited to submit unpublished or extended version of conference papers, related but not limited to the following topics of interest:

• Visionary papers on Society 5.0/Industry 4.0 applications
• Mining spatiotemporal databases
• Mining spatiotemporal data streams
• Mining uncertain spatiotemporal data
• Spatiotemporal trajectory analytics
• Spatiotemporal multimedia analytics
• Machine learning/deep learning of spatiotemporal data
• Advanced data analytical methods on spatiotemporal data
• Analytics of meteorological datasets
• Analytics of astronomical big data
• Mining lifelong data
• Optimizing machine learning algorithms for spatiotemporal big data
• Energy efficient mining of spatiotemporal big data
• User interfaces for spatiotemporal applications
• Multi-core and distributed mining algorithms for spatiotemporal big data analytics
• Decision support systems
• Intelligent transportation systems
• Case studies

Prof. Rage Uday Kiran
Prof. Philippe Fournier-Viger
Prof. José María Luna
Prof. Agarwal Sonali
Guest Editors

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