Efficient and Fast Pattern Matching in Stream Time Series Image Data

Sethukkarasi, R.; Rajalakshmi, D.; Kannan, A.

doi:10.1109/iciic.2010.49

Cited by 4 publications

(4 citation statements)

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“…The study in Wang et al (2013) compares 8 different time-series representation techniques and compares their performance over several time series data sets. The approaches in Lian et al (2009) and Sethukkarasi et al (2010) represents time series using Multi Segment Mean (MSM) representation and multi scale segment median approximation representation for stream time-series image data, respectively for fast pattern matching in stream time.…”

Section: Representation Of Time-series With Reduced Dimensionalitymentioning

confidence: 99%

A Survey on Effective Pattern Matching in Uncertain Time Series Stream Data

Rajalakshm¹,

Dinakaran²

2015

Asian J. of Applied Sciences

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In real time application such as weather forecasting, coal mine surveillance and privacy preserving data streams arrive at a rate higher than in traditional sensing applications. The processing of these raw data must be as fast as stream speed. These applications convert the raw data into a specified pattern and these patterns vary from application to application. It is then subjected to sophisticated query processing to extract high level information. Uncertainty in stream time series may occur for two reasons such as the inherent imprecision of sensor readings or privacy preserving conversion. Representation of uncertain data over stream time-series, uncertain data management and designing of a data mining algorithm on considering the uncertainty affects the data mining process. The processing of data in the real time application is difficult as it is naturally incomplete and noisy and the observed data pattern is different from the actual pattern required for further processing. A major challenge in processing the stream time-series data with uncertainty is to capture uncertainty as data propagates through query operators until the final result and to process the data at stream speed. The modeling of uncertain time-series without affecting the precision of the system still remains a difficult task. More importantly, it should avoid increased false positives. In this study, a survey of time series data management for pattern matching is provided. The similarity search over uncertain data will be explored. The issues in existing research, limitations and methodology for pattern matching on uncertain time series stream data are examined in this study.

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Section: Representation Of Time-series With Reduced Dimensionalitymentioning

confidence: 99%

A Survey on Effective Pattern Matching in Uncertain Time Series Stream Data

Rajalakshm¹,

Dinakaran²

2015

Asian J. of Applied Sciences

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show abstract

Section: Literature Surveymentioning

confidence: 99%

“…If the original data is represented by the set T after data approximation at level 1 as MSM1 , then MSM 1 = ( MSM 11 where m = length ( T ) / seg_size l ...... (8) The time complexity for the MSM reduction process is O(l*n i ) where l is the number of levels of MSM and n i is the number of segments at each stage. The time complexity is directly proportional to the number of MSM levels.…”

Section: -Illustration Of Msmmentioning

confidence: 99%

“…This approach is not so suitable for image data streams. Sethukkarasi et al, [8] presented a technique for similarity matching between static/dynamic patterns and time-series image data to perform effective retrieval of image data. Keogh et al, [9] have implemented Piecewise Aggregate Approximation, Adaptive Piecewise Constant Approximation, Symbolic Aggregate Approximation and a bit level time series representation techniques for pattern searching.…”

Section: Literature Surveymentioning

confidence: 99%

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