Next Location Prediction with Time-Evolving Markov Models over Data Streams

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Andrade, T (Author) Other; Gama, João (Author) FEP

Conference proceedings International

Title: Progress in Artificial Intelligence - 23rd EPIA Conference on Artificial Intelligence, EPIA 2024, Viana do Castelo, Portugal, September 3-6, 2024, Proceedings, Part III

Pages: 115-126

23rd EPIA Conference on Artificial Intelligence, EPIA 2024

Viana do Castelo, 3 September 2024 through 6 September 2024

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-017-CWV

DOI: 10.1007/978-3-031-73503-5_10

Abstract (EN): Various relevant aspects of our lives relate to the places we visit and our daily activities. The movement of individuals between regular places, such as work, school, or other important personal locations is getting increasing attention due to the pervasiveness of geolocation devices and the amount of data they generate. This paper presents an approach for personal location prediction using a probabilistic model and data mining techniques over mobility data streams. We extract the individuals¿ locations from relevant events in a data stream to build and maintain a Markov Chain over the important places. We evaluate the method over 3 real-world datasets. The results show the usefulness of the proposal in comparison with other well-known approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11