Similarity searching for multi-attribute sequences

Kahveci, Tamer; Singh, Ambuj K.; Gurel, Ahmet Volkan

doi:10.1109/ssdm.2002.1029718

Cited by 51 publications

(27 citation statements)

References 16 publications

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“…While the design of distance functions between trajectories has been considered in recent work [15,14,7], none of these techniques consider similarity measures that are rotation invariant. In many applications, a similarity measure that is immune to rotation effects is desirable.…”

Section: Introductionmentioning

confidence: 99%

Rotation invariant distance measures for trajectories

Vlachos¹,

Gunopulos²,

Das

2004

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Section: Introductionmentioning

confidence: 99%

Rotation invariant distance measures for trajectories

Vlachos¹,

Gunopulos²,

Das

2004

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…The MBRs are organized in an R*-tree index structure, which is probed by query MBRs to prune irrelevant data sequences. Kahveci et al [19] propose an index structure for shift-and scaleinvariant comparison of time sequences. Cai et al [6] use the coefficients of chebyshef polynomials as features to approximate data sequences.…”

Section: Related Workmentioning

confidence: 99%

“…In cases where d = 2 or 3, a history, often called a trajectory, may describe the position of a moving object in a plane or space, and there are also works that address this problem (e.g. [22,32,19,31]). Since time series and trajectories are usually long sequences, a common approach is to index features extracted from segments using a variant of R-tree structure.…”

Section: Introductionmentioning

confidence: 99%

On efficiently searching trajectories and archival data for historical similarities

Sherkat

Rafiei

2008

Proc. VLDB Endow.

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We study the problem of efficiently evaluating similarity queries on histories, where a history is a d-dimensional time series for d ≥ 1. While there are some solutions for timeseries and spatio-temporal trajectories where typically d ≤ 3, we are not aware of any work that examines the problem for larger values of d. In this paper, we address the problem in its general case and propose a class of summaries for histories with a few interesting properties. First, for commonly used distance functions such as the Lp-norm, LCSS, and DTW, the summaries can be used to efficiently prune some of the histories that cannot be in the answer set of the queries. Second, histories can be indexed based on their summaries, hence the qualifying candidates can be efficiently retrieved. To further reduce the number of unnecessary distance computations for false positives, we propose a finer level approximation of histories, and an algorithm to find an approximation with the least maximum distance estimation error. Experimental results confirm that the combination of our feature extraction approaches and the indexability of our summaries can improve upon existing methods and scales up for larger values of d and database sizes, based on our experiments on real and synthetic datasets of 17-dimensional histories.

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“…Work on multidimensional sequences can be found in Lee et al (2000) and Kahveci, Singh, and Gurel (2002), but only support the use of Euclidean distance, which cannot capture flexible similarities.…”

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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Similarity searching for multi-attribute sequences

Abstract: We

Cited by 51 publications

References 16 publications

Rotation invariant distance measures for trajectories

Rotation invariant distance measures for trajectories

On efficiently searching trajectories and archival data for historical similarities

Elastic Translation Invariant Matching of Trajectories

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