Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Approximate Queries and Representations for Large Data Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Online Amnesic Approximation of Streaming Time Series
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
ACM SIGMOD Record
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Time delay is a general phenomenon in industrial process. Accurate evaluation on delay is important in data preprocessing when mine manufactory process data. As typical streaming time series, sensors’ data of industrial process have attracted much attention recently. A new concept , trend similarity search, is proposed based on raw monotony between two industrial process variables. The new concept is for those two time series which are similar only in trend but dissimilar in shape, whereas similarity search may not do well in such condition. An algorithm DelayMine is also proposed to mine delay between two interrelated time series by trend similarity search. Moreover, the DelayMine is extended to online algorithm for processing streaming time series. The properties and performance of DelayMine is demonstrated through experiments both on systems with steady and time-varying delay.