A methodology for extracting temporal properties from sensor network data streams

Authors:
Dimitrios Lymberopoulos;Athanasios Bamis;Andreas Savvides
Affiliations:
Microsoft Research, Redmond, WA, USA;Yale University, New Haven, CT, USA;Yale University, New Haven, CT, USA
Venue:
Proceedings of the 7th international conference on Mobile systems, applications, and services
Year:
2009

Citing 10
Cited 6

Discovery of Frequent Episodes in Event Sequences

Data Mining and Knowledge Discovery
Mining Sequential Patterns

ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Approximate Association Rule Mining

Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Hierarchical Clustering Algorithms for Document Datasets

Data Mining and Knowledge Discovery
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
A sensory grammar for inferring behaviors in sensor networks

Proceedings of the 5th international conference on Information processing in sensor networks
Learning and inferring transportation routines

Artificial Intelligence
Detecting Patterns for Assisted Living Using Sensor Networks: A Case Study

SENSORCOMM '07 Proceedings of the 2007 International Conference on Sensor Technologies and Applications
Extracting spatiotemporal human activity patterns in assisted living using a home sensor network

Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments

STFL: a spatio temporal filtering language with applications in assisted living

Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Collaborative signal processing for action recognition in body sensor networks: a distributed classification algorithm using motion transcripts

Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Discovering routine events in sensor streams for macroscopic sensing composition

Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
The BehaviorScope framework for enabling ambient assisted living

Personal and Ubiquitous Computing
A method for discovering components of human rituals from streams of sensor data

CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Delay-bounded utility-based event detection in energy harvesting sensor networks

Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

The extraction of temporal characteristics from sensor data streams can reveal important properties about the sensed events. Knowledge of temporal characteristics in applications where sensed events tend to periodically repeat, can provide a great deal of information towards identifying patterns, building models and using the timing information to actuate and provide services. In this paper we outline a methodology for extracting the temporal properties, in terms of start time and duration, of sensor data streams that can be used in applications such as human, habitat, environmental and traffic monitoring where sensed events repeat over a time window. Its application is demonstrated on a 30-day dataset collected from one of our assisted living sensor network deployments.