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Learning routines over long‐term sensor data using topic models
Author(s) -
Castanedo Federico,
deIpiña Diego López,
Aghajan Hamid K.,
Kleihorst Richard
Publication year - 2014
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12033
Subject(s) - computer science , timeline , a priori and a posteriori , process (computing) , term (time) , wireless sensor network , data mining , machine learning , data science , streaming data , artificial intelligence , computer network , philosophy , physics , archaeology , epistemology , quantum mechanics , history , operating system
Recent advances on sensor network technology provide the infrastructure to create intelligent environments on physical places. One of the main issues of sensor networks is the large amount of data they generate. Therefore, it is necessary to have good data analysis techniques with the aim of learning and discovering what is happening on the monitored environment. The problem becomes even more challenging if this process is performed following an unsupervised way (without having any a priori information) and applied over a long‐term timeline with many sensors. In this work, topic models are employed to learn the latent structure and dynamics of sensor network data. Experimental results using two realistic datasets, having over 50 weeks of data, have shown the ability to find routines of activity over sensor network data in office environments.