Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Relevance feedback retrieval of time series data
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A unifying framework for detecting outliers and change points from non-stationary time series data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Load Shedding for Aggregation Queries over Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
A Unified Framework for Monitoring Data Streams in Real Time
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
MIST: distributed indexing and querying in sensor networks using statistical models
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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Due to recent advances in sensor technology greater quantities of sensor data are being generated and circulated. In these circumstances, sensor data stream processing and management technologies have become important research areas. In the development of mechanical and electrical systems, sensor stream data are a potential medium for sharing information among the engineers who are engaged in the various phases in the system development and operation. This paper proposes an annotation method for a sensor data stream that links the information generated in the development and operational phases of a system. The key techniques of the proposed method are sensor pattern construction using hidden Markov models (HMMs) and an annotation method based on the HMMs constructed. We applied the proposed method to the sensor data stream of a supersmall artificial satellite and showed that the proposed method achieved approximately 95% annotation accuracy for long fragments of the sensor data stream.