Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
General match: a subsequence matching method in time-series databases based on generalized windows
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Duality-Based Subsequence Matching in Time-Series Databases
Proceedings of the 17th International Conference on Data Engineering
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Proceedings of the 3rd international conference on Embedded networked sensor systems
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Time series compressibility and privacy
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the VLDB Endowment
Managing and Mining Uncertain Data
Managing and Mining Uncertain Data
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
On Unifying Privacy and Uncertain Data Models
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Probabilistic Similarity Search for Uncertain Time Series
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Efficient join processing on uncertain data streams
Proceedings of the 18th ACM conference on Information and knowledge management
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
PODS: a new model and processing algorithms for uncertain data streams
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Uncertain Range Queries for Necklaces
MDM '10 Proceedings of the 2010 Eleventh International Conference on Mobile Data Management
DUST: a generalized notion of similarity between uncertain time series
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
DUST: a generalized notion of similarity between uncertain time series
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
On wavelet decomposition of uncertain time series data sets
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Top-k similarity search on uncertain trajectories
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
ε-Matching: event processing over noisy sequences in real time
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Report on the first workshop on innovative querying of streams
ACM SIGMOD Record
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In the last years there has been a considerable increase in the availability of continuous sensor measurements in a wide range of application domains, such as Location-Based Services (LBS), medical monitoring systems, manufacturing plants and engineering facilities to ensure efficiency, product quality and safety, hydrologic and geologic observing systems, pollution management, and others. Due to the inherent imprecision of sensor observations, many investigations have recently turned into querying, mining and storing uncertain data. Uncertainty can also be due to data aggregation, privacy-preserving transforms, and error-prone mining algorithms. In this study, we survey the techniques that have been proposed specifically for modeling and processing uncertain time series, an important model for temporal data. We provide an analytical evaluation of the alternatives that have been proposed in the literature, highlighting the advantages and disadvantages of each approach, and further compare these alternatives with two additional techniques that were carefully studied before. We conduct an extensive experimental evaluation with 17 real datasets, and discuss some surprising results, which suggest that a fruitful research direction is to take into account the temporal correlations in the time series. Based on our evaluations, we also provide guidelines useful for the practitioners in the field.