Labeling sensing data for mobility modeling. (April 2016)

• Record Type: Journal Article
• Authors: Read, Jesse; Žliobaitė, Indrė; Hollmén, Jaakko
• Is Part Of: Information systems. Volume 57(2016)
• Page Start: 207
• Page End: 222
• Publication Date: 2016-04
• Subjects: Sensory data -- Sensor fusion -- Hidden Markov models -- Multi-label; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2015.09.001
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: In urban environments, sensory data can be used to create personalized models for predicting efficient routes and schedules on a daily basis; and also at the city level to manage and plan more efficient transport, and schedule maintenance and events. Raw sensory data is typically collected as time-stamped sequences of records, with additional activity annotations by a human, but in machine learning, predictive models view data as labeled instances, and depend upon reliable labels for learning. In real-world sensor applications, human annotations are inherently sparse and noisy. This paper presents a methodology for preprocessing sensory data for predictive modeling in particular with respect to creating reliable labeled instances. We analyze real-world scenarios and the specific problems they entail, and experiment with different approaches, showing that a relatively simple framework can ensure quality labeled data for supervised learning. We conclude the study with recommendations to practitioners and a discussion of future challenges.