[Dmbu-l] A Generic Framework for Efficient and Effective Subsequence Retrieval [Thursday 04/26 @ 12:00 pm in MCS 148]

Charalampos Mavroforakis cmav at bu.edu
Thu Apr 26 11:16:44 EDT 2012


A reminder for today's talk, 12pm in MCS 148

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> *
> *
> *Data Mining and Database Group Seminar*
> *A Generic Framework for Efficient and Effective Subsequence Retrieval*
> *Speaker: *Haohan Zhu,* *Boston University
>
>
> *Abstract:*
> This paper proposes a general framework for matching similar subsequences
> in both time series and string databases. The matching results are pairs of
> query subsequences and database subsequences. The framework finds all
> possible pairs of similar subsequences if the distance measure satisfies
> the "consistency" property, which is a property introduced in this paper.
> We show that most popular distance functions, such as the Euclidean
> distance, DTW, ERP, the Frechet distance for time series, and the Hamming
> distance and Levenshtein distance for strings, are all "consistent". We
> also propose an index structure for metric spaces named "reference net".
> The reference net is an unsupervised index which costs O(n) space, where n
> is the size of the dataset. The experiments demonstrate the ability of our
> method to improve retrieval performance when combined with diverse distance
> measures. The experiments also illustrate that the reference net has a
> better running time than cover trees and the maximum variance method, while
> all three methods have similar costs in terms of space.
>
> Joint work with George Kollios and Vassilis Athitsos
>
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