Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Fast discovery of association rules
Advances in knowledge discovery and data mining
Maintaining knowledge about temporal intervals
Communications of the ACM
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Connecting the Physical World with Pervasive Networks
IEEE Pervasive Computing
Discovering Temporal Patterns for Interval-Based Events
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
From Shell Logs to Shell Scripts
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Discovering Frequent Arrangements of Temporal Intervals
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Multi-Dimensional Relational Sequence Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
IEEE Communications Magazine
Learning pattern graphs for multivariate temporal pattern retrieval
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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Wireless sensor networks (WSNs) represent a typical domain where there are complex temporal sequences of events. In this paper we propose a relational framework to model and analyse the data observed by sensor nodes of a wireless sensor network. In particular, we extend a general purpose relational sequence mining algorithm to take into account temporal interval-based relations. Real-valued time series are discretized into similar subsequences and described by using a relational language. Preliminary experimental results prove the applicability of the relational learning framework to complex real world temporal data.