Rawassizadeh, RezaDobbins, ChelseaAkbari, MohammadPazzani, Michael2020-05-062020-05-062019-01-22Reza Rawassizadeh, Chelsea Dobbins, Mohammad Akbari, Michael Pazzani. 2019. "Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering." Sensors (Basel), Volume 19, Issue 3, 25 pages. https://doi.org/10.3390/s190304481424-8220https://hdl.handle.net/2144/40628Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering approach can be used as an annotation on raw sensor data. It improves information retrieval by reducing the search space and is based on searching only the related clusters. To further improve behavior quantification, the third algorithm identifies contrasting events withina cluster content. Two large real-world smartphone datasets have been used to evaluate our algorithms and demonstrate the utility and resource efficiency of our approach to search.25 pagesen-US© 2019 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.http://creativecommons.org/licenses/by/4.0/ClusteringEvent detectionHuman behaviorMobile sensing: contrast behavior miningSpatio-temporalAlgorithmsCluster analysisHumansInformation storage and retrievalSmartphoneAnalytical chemistryElectrical and electronic engineeringEnvironmental science and managementEcologyAnalytical hemistryArticle10.3390/s19030448479151