Fast retrieval of similar subsequences in long sequence databases

Park, S.; Lee, D.; Chu, Wesley W.

doi:10.1109/kdex.1999.836610

Cited by 50 publications

(41 citation statements)

References 6 publications

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“…This technique suffers from performance degradation for long time sequences [21], as it has to scan all the data sequences sequentially to perform a similarity search [18].…”

Section: Gis'02mentioning

confidence: 99%

Aggregation and comparison of trajectories

Meratnia

By²

2002

Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems

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Dealing with moving objects necessitates having available complete geographical traces for determining exact or possible locations that objects have had, have or will have. This is where trajectory determination plays an important role, and on which classification, aggregation and comparison methods must be built. The purpose of aggregation is to identify similar trajectories and to represent them by a single trajectory.Although much work has been done in similarity measurements for time series data, they mainly deal with one dimensional time series. On the other hand, they are good for short time series and in absence of noise, which is definitely not the case for moving objects. This paper describes different approaches to aggregate similar trajectories.

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“…This technique suffers from performance degradation for long time sequences [21], as it has to scan all the data sequences sequentially to perform a similarity search [18].…”

Section: Gis'02mentioning

confidence: 99%

Aggregation and comparison of trajectories

Meratnia

By²

2002

Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems

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“…Many other schemes have been proposed for similarity search in time series databases. As they focus on speeding up search by sacrificing the guarantee of no false dismissals [11,15,19], and/or allowing more flexible distances measures [2,11,15,14,13,23,16, 21] we will not discuss them further.…”

Section: Related Workmentioning

confidence: 99%

A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases

Keogh

Pazzani

2000

Knowledge Discovery and Data Mining. Current Issues and New Applications

178

104

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Abstract.We address the problem of similarity search in large time series databases. We introduce a novel-dimensionality reduction technique that supports an indexing algorithm that is more than an order of magnitude faster than the previous best known method. In addition to being much faster our approach has numerous other advantages. It is simple to understand and implement, allows more flexible distance measures including weighted Euclidean queries and the index can be built in linear time. We call our approach PCA-indexing (Piecewise Constant Approximation) and experimentally validate it on space telemetry, financial, astronomical, medical and synthetic data.

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“…Several high level representations of time series have been proposed, including Fourier Transforms [1,13], Wavelets [4], Symbolic Mappings [2,5,24] and Piecewise Linear Representation (PLR). In this work, we confine our attention to PLR, perhaps the most frequently used representation [8,10,12,14,15,16,17,18,20,21,22,25,27,28,30,31].…”

Section: Introductionmentioning

confidence: 99%

“…• Support novel distance measures for time series, including "fuzzy queries" [27,28], weighted queries [15], multiresolution queries [31,18], dynamic time warping [22] and relevance feedback [14].…”

Section: Introductionmentioning

confidence: 99%