Special Session on Spatiotemporal Big Data Analytics (SBDA2021)
34th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2021)
Governmental initiatives, such as Society 5.0 and Industry 4.0, aim to promote well-being of the human beings (or society as a whole). These initiatives cover a wide spectrum of real-world applications whose data naturally exist as a spatiotemporal database. Mining spatiotemporal databases can provide useful insights in many real-world applications such as eCommerce, internet of things, agriculture, healthcare, intelligent transportation systems, meteorology and astronomy. 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 were regularly exposed to harmful levels of air pollution.
Special session on Spatiotemporal Big Data Analytics is the 1st Special Session focusing on original research that aim to solve real-world problems using the concepts, tools, and techniques from statistics, data mining, machine learning and artificial intelligence. Papers in this special session are expected to range over a wide spectrum of topics from theoretical results to practical considerations, and from academic research to industrial adoption. Topics of interest include but are not limited to:
Call for paper
We are inviting original research submissions (FULL 12 pages) and work-in-progress (SHORT 6 pages). Submitted papers must be formatted using the Springer LNCS/LNAI style.
Topics of interest include but not limited to:
- Visionary papers on Society 5.0/Industry 4.0 applications
- Mining spatiotemporal databases
- Mining spatiotemporal data streams
- Mining uncertain spatiotemporal data
- Spatiotemporal multimedia analytics
- Machine learning/Deep learning of spatiotemporal data
- Analytics on Meteorological datasets
- Analytics on Astronomical big data
- Mining lifelog 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
- Developing intelligent transportation systems
- Case studies
|Conference Sessions:||July 26 –29, 2021|
*Announcement: Due to the current pandemic of COVID-19, virtual video presentation can be considered and allowed for presenters from affected regions and countries.
Submission Site: https://cmt3.research.microsoft.com/IEAAIE2021/Submission/Index
- R. Uday Kiran, The University of Aizu, Japan, firstname.lastname@example.org
- Ram AVTAR, Hokkaido University, Japan, email@example.com
- Incheon Paik, The University of Aizu, Japan, firstname.lastname@example.org
- Sonali Agarwal, Indian Institute of Information Technology-Allahabad, India, email@example.com
- Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway, firstname.lastname@example.org