Supporting fast search in time series for movement patterns in multiple scales

Qu, Yunyao; Wang, Changzhou; Wang, X. Sean

doi:10.1145/288627.288664

Cited by 55 publications

(30 citation statements)

References 7 publications

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“…Other techniques to define time series similarity are based on extracting certain features (Landmarks (Perng et al, 2000) or signatures (Faloutsos et al, 1997)) from each time-series and then using these features to define the similarity. Another approach to represent a time series using the direction of the sequence at regular time intervals is presented in Qu, Wang, and Wang (1998). Ge and Smyth (2000) present an alternative sequence similarity model which is based on probabilistic matching, using Hidden Markov Models.…”

Section: Related Workmentioning

confidence: 99%

Elastic Translation Invariant Matching of Trajectories

Vlachos¹,

Kollios

Gunopulos

2005

Mach Learn

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Abstract.We investigate techniques for analysis and retrieval of object trajectories. We assume that a trajectory is a sequence of two or three dimensional points. Trajectory datasets are very common in environmental applications, mobility experiments, video surveillance and are especially important for the discovery of certain biological patterns. Such kind of data usually contain a great amount of noise, that makes all previously used metrics fail. Therefore, here we formalize non-metric similarity functions based on the Longest Common Subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to the similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translating of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and Dynamic Time Warping distance functions (for real and synthetic data) and show the superiority of our approach, especially under the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach.

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Section: Related Workmentioning

confidence: 99%

Elastic Translation Invariant Matching of Trajectories

Vlachos¹,

Kollios

Gunopulos

2005

Mach Learn

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“…An interesting approach to represent a time series using the direction of the sequence at regular time intervals is presented in [23]. Ge and Smyth [11] present an interesting alternative approach for sequence similarity that is based on probabilistic matching.…”

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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“…. , s4 are merged in three steps to produce the PTA result tuple z1, stating that in the interval [1,10] the average salary and working hours for project A are 840 and 29, respectively.…”

Section: Introductionmentioning

confidence: 99%

Parsimonious temporal aggregation

2011

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Temporal aggregation is a crucial operator in temporal databases and has been studied in various flavors, including instant temporal aggregation (ITA) and span temporal aggregation (STA), each having its strengths and weaknesses. In this paper we define a new temporal aggregation operator, called parsimonious temporal aggregation (PTA), which comprises two main steps: (i) it computes the ITA result over the input relation and (ii) it compresses this intermediate result to a user-specified size c by merging adjacent tuples and keeping the induced total error minimal; the compressed ITA result is returned as the final result. By considering the distribution of the input data and allowing to control the result size, PTA combines the best features of ITA and STA. We provide two evaluation algorithms for PTA queries. First, the oPTA algorithm computes an exact solution, by applying dynamic programming to explore all possibilities to compress the ITA result and selecting the compression with the minimal total error. It runs in O(n2pc) time and O(n2) space, where n is the size of the input relation and p is the number of aggregation functions in the query. Second, the more efficient gPTA algorithm computes an approximate solution by greedily merging the most similar ITA result tuples, which, however, does not guarantee a compression with a minimal total error. gPTA intermingles the two steps of PTA and avoids large intermediate results. The compression step of gPTA runs in O(np log(c + ?)) time and O(c + ?) space, where ? is a small buffer for ""look ahead"". An empirical evaluation shows good results: considerable reductions of the result size introduce only small errors, and gPTA scales to large data sets and is only slightly worse than the exact solution of PTA. Parsimonious Temporal Aggregation Juozas Gordevičius Johann Gamper Michael BöhlenFree University of Bozen-Bolzano, Italy {gordevicius,gamper,boehlen}@inf.unibz.it ABSTRACTTemporal aggregation is a crucial operator in temporal databases and has been studied in various flavors, including instant temporal aggregation (ITA) and span temporal aggregation (STA), each having its strengths and weaknesses. In this paper we define a new temporal aggregation operator, called parsimonious temporal aggregation (PTA), which comprises two main steps: (i) it computes the ITA result over the input relation and (ii) it compresses this intermediate result to a user-specified size c by merging adjacent tuples and keeping the induced total error minimal; the compressed ITA result is returned as the final result. By considering the distribution of the input data and allowing to control the result size, PTA combines the best features of ITA and STA. We provide two evaluation algorithms for PTA queries. First, the oPTA algorithm computes an exact solution, by applying dynamic programming to explore all possibilities to compress the ITA result and selecting the compression with the minimal total error. It runs in O(n 2 pc) time and O(n 2 ) space, where n is the size of th...

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Supporting fast search in time series for movement patterns in multiple scales

Cited by 55 publications

References 7 publications

Elastic Translation Invariant Matching of Trajectories

Elastic Translation Invariant Matching of Trajectories

Aggregation and comparison of trajectories

Parsimonious temporal aggregation

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