Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
On the Discovery of Weak Periodicities in Large Time Series
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Hierarchical Clustering Algorithms for Document Datasets
Data Mining and Knowledge Discovery
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
A methodology for extracting temporal properties from sensor network data streams
Proceedings of the 7th international conference on Mobile systems, applications, and services
The BehaviorScope framework for enabling ambient assisted living
Personal and Ubiquitous Computing
Designing smart environments: a paradigm based on learning and prediction
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Towards macroscopic human behavior based authentication for mobile transactions
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Semantic trajectories: Mobility data computation and annotation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction
Discovering periodic patterns of nodal encounters in mobile networks
Pervasive and Mobile Computing
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This paper describes an algorithm for determining if an event occurs persistently within an interval where the interval is periodic but the event is not. The goal of the algorithm is to identify events with this property and also determine the minimum interval in which they occur. This solution is geared towards discovering human routines by considering the triggering of simple sensors over a diverse set of spatial and temporal scales. After describing the problem and the proposed solution, in this paper we demonstrate using testbed data and simulations that this approach uncovers components of routines by identifying which events are parts of the same routine through their temporal properties.