Paper Info
Title
TPRED: a Spatio-Temporal Location Predictor Framework.
Abstract
The vast diffusion of devices equipped with a GPS receiver has brought the possibility of collecting data related to massive amounts of moving objects on a scale never seen before. During the latest years, such diffusion instigated the development of many different techniques to deal with location prediction problems. Existing works mainly aim at predicting the next location of moving objects by focusing on information in the spatial domain. In this paper we want to take into account information in the temporal domain as well, both to improve the reliability of predictions and to answer not only where a moving object is going to move, but also when an object is expected to leave its current location. To this end we propose Tpred, a framework based on probabilistic suffix trees which tries to capture typical movement patterns of moving objects, and computes reliable predictions accordingly, by exploiting information both in the spatial and temporal domains. In order to prove the validity of our contribution we conduct an extensive set of experimental evaluations, based on real-world datasets and different performance metrics, where we show the efficiency and effectiveness of our proposal.
Year
DOI
Venue
2016
10.1145/2938503.2938544
IDEAS
Field
DocType
Citations 
Data mining,Suffix,Computer science,Artificial intelligence,Probabilistic logic,Location prediction,Assisted GPS,Database,Machine learning
Conference
1
PageRank 
References 
Authors
0.35
11
7
Name
Order
Citations
PageRank
Cleilton L. Rocha110.35
Igo Ramalho Brilhante21027.83
Francesco Lettich3195.37
José Antônio Fernandes de Macêdo446551.40
Alessandra Raffaetà518716.45
Rossana M. C. Andrade635458.76
Salvatore Orlando71595202.29