Managing uncertainty in moving objects databases

By Goce Trajcevski, Ouri Wolfson, K. Hinrichs, Sam Chamberlain. TODS, 2004

314 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Geography, Planning and Development, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b79f55d6d86e070b8894274febc2a15988c5259b

Citations in the corpus

• Trajectory Data Mining (2015, unknown): “This is usually achieved by employing geometric objects, such as cylinders [101][100] or beads [99], as trajectory approximations.”
• Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases (2008, unknown): “…the basic assumptions are that each tuple in the table corresponds uniquely to an individual, and that attributes are divided in quasi-identifiers (i.e., attributes that can be linked to external information to reidentify the individual to whom the information refers), and sensitive attributes.”
• A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, unknown): “This uncertainty arises because the objects are moving their location, and due to the network delays [648].”
• The new Casper: query processing for location services without compromising privacy (2006, unknown): “Thus, our approach can be seamlessly integrated with any approaches for probabilistic query processing (e.g., [10, 11, 28, 38, 42, 46]).”