Spatio-Temporal Data Mining and Management
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 270
Special Issue Editor
Interests: spatiotemporal data management; streaming data analytics, route planning and recommendations; location-based social media
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the proliferation of location-based equipment (e.g., smartphones, dashcams, wearable devices), spatiotemporal data, which contain both location and time information, are being generated at an unprecedented scale. Spatiotemporal data are ubiquitous in modern life applications, including location-based social media (e.g., Twitter, Facebook), online map services (e.g., Google Maps), online ride-hailing services (e.g., Uber), and smart sensors. The big spatiotemporal data generated by these services and devices contain much timely and useful information. As such, it is important to develop location-based query processing and optimization techniques to support efficient spatiotemporal information retrieval. Meanwhile, it is of equal importance to propose spatiotemporal data mining algorithms to enable timely and effective knowledge discovery from big spatiotemporal data.
However, the problem of effective and efficient management, mining, and analysis of big spatiotemporal data has long been an open challenge for the data science community. Apart from having generic characteristics of big data (e.g., big volume, high velocity, variety), big spatiotemporal data additionally have more challenging characteristics, including but not limited to high variability, low veracity, and difficulty in validation and in ensuring data security.
The purpose of this Special Issue is therefore to disseminate the results of advanced data management, data mining, and data analysis approaches to addressing the aforementioned challenges of processing big spatiotemporal data.
Prof. Dr. Lisi Chen
Guest Editor
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Keywords
- spatial data
- temporal data
- data privacy
- query processing
- indexing
- stream data
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