A Brief Introduction to Geospatial Big Data Analytics with Apache Asterixdb

• Published in: Proceedings / IEEE International Conference on Mobile Data Management pp. 1 - 3
• Main Authors: Shivakumar, Suryaa Charan, Maxon, Ian, Eldawy, Ahmed, Carey, Michael, Tsotras, Vassilis
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02.06.2025
• Subjects: Big Data; Big data analytics; Complexity theory; Data systems; Distributed databases; Focusing; Geospatial analysis; Geospatial big data; Information storage systems; Information systems; Optimization; Parallel and distributed DBMS(s); Seminars; Spatial analysis; Spatial databases
• ISSN: 2375-0324
• DOI: 10.1109/MDM65600.2025.00049

The potential of geospatial data is vast, and its value increases when combined with temporal, textual, or other nonspatial features. However, managing and analyzing geospatial data at scale is inherently challenging due to the computational and storage requirements, especially when additional optimization is required for combined features. While there are numerous solutions for big spatial data management, many struggle to support non-spatial operations effectively, with limited options in the open-source space that excel at handling both spatial and non-spatial queries comprehensively. This seminar explores scalable geospatial data management and analytics, focusing on approaches and techniques that address these challenges. Participants will gain hands-on experience in processing complex queries involving spatial, temporal, and textual features using a real-world Big Data Management System. Through practical examples and exercises, attendees will learn how to tackle the complexities of scalable geospatial analytics in modern data systems.