Paper Info
Title
Predicting future locations using prediction-by-partial-match
Abstract
We implemented the Prediction-by-Partial-Match data compression algorithm as a predictor of future locations. Positioning was done using IEEE 802.11 wireless access logs. Several experiments were run to determine how to divide the data for training and testing and how to best represent the data as a string of symbols. Our test data consisted of 198 datasets containing over 28,000 pairs, obtained from the UCSD Wireless Topology Discovery project. Tests of a first-order PPM model revealed a 90% success rate in predicting a user's location given the time. The third-order model, which is given the previous time and location and asked to predict the location at a given time, is correct 92% of the time.
Year
DOI
Venue
2008
10.1145/1410012.1410014
MELT
Keywords
Field
DocType
third-order model,compression algorithm,future location,previous time,wireless access log,ucsd wireless topology discovery,test data,success rate,first-order ppm model,prediction-by-partial-match data,supervised learning,first order,prediction,data consistency,data compression
Data mining,Wireless,Computer science,Partial match,Supervised learning,Test data,Artificial intelligence,Data compression,Machine learning
Conference
Citations 
PageRank 
References 
14
0.80
14
Authors
2
Name
Order
Citations
PageRank
Ingrid Burbey1242.50
Thomas L. Martin220124.17