The single machine early/tardy problem
Management Science
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Optimal manufacturing batch size with rework process at a single-stage production system
Computers and Industrial Engineering
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
Adaptive cleaning for RFID data streams
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Proceedings of the VLDB Endowment
PROUD: a probabilistic approach to processing similarity queries over uncertain data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
A Framework for Clustering Uncertain Data Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Representing uncertain data: models, properties, and algorithms
The VLDB Journal — The International Journal on Very Large Data Bases
Bidirectional data aggregation scheme for wireless sensor networks
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
Similarity matching for uncertain time series: analytical and experimental comparison
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data
Online windowed subsequence matching over probabilistic sequences
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Uncertain time-series similarity: return to the basics
Proceedings of the VLDB Endowment
A probabilistic approach to correlation queries in uncertain time series data
Proceedings of the 21st ACM international conference on Information and knowledge management
Computers and Industrial Engineering
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Large-scale sensor deployments and an increased use of privacy-preserving transformations have led to an increasing interest in mining uncertain time series data. Traditional distance measures such as Euclidean distance or dynamic time warping are not always effective for analyzing uncertain time series data. Recently, some measures have been proposed to account for uncertainty in time series data. However, we show in this paper that their applicability is limited. In specific, these approaches do not provide an intuitive way to compare two uncertain time series and do not easily accommodate multiple error functions. In this paper, we provide a theoretical framework that generalizes the notion of similarity between uncertain time series. Secondly, we propose DUST, a novel distance measure that accommodates uncertainty and degenerates to the Euclidean distance when the distance is large compared to the error. We provide an extensive experimental validation of our approach for the following applications: classification, top-k motif search, and top-k nearest-neighbor queries.