Distance measure for querying sequences of temporal intervals

Orestis Kostakis*, Panagiotis Papapetrou, Jaakko Hollmén

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    4 Citations (Scopus)

    Abstract

    Time series representations are not always rich enough to describe the temporal activity, for instance, when the context and the relations of the observed elements are of interest. Sequences of temporal intervals use such intervals as primitives in their representation, and allow focusing on the temporal relations of these elements. This is a useful representation of data across many domains. Searching, indexing, and mining such sequences is essential for domain experts in order to discover useful information out of them. In this paper, we formulate the problem of comparing sequences of temporal intervals and propose a novel distance measure. We discuss the properties of the measure and study its robustness in the domain of sign language. Experiments on real data show that the measure is robust in terms of retrieval accuracy even for high levels of artificially introduced distortion.

    Original languageEnglish
    Title of host publication4th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2011
    Publication statusPublished - 2011
    MoE publication typeA4 Article in a conference publication
    EventInternational Conference on Pervasive Technologies Related to Assistive Environments - Heraklion, Greece
    Duration: 25 May 201127 May 2011
    Conference number: 4

    Conference

    ConferenceInternational Conference on Pervasive Technologies Related to Assistive Environments
    Abbreviated titlePETRA
    CountryGreece
    CityHeraklion
    Period25/05/201127/05/2011

    Keywords

    • American sign language
    • Distance measure
    • Sequence
    • Temporal intervals

    Fingerprint Dive into the research topics of 'Distance measure for querying sequences of temporal intervals'. Together they form a unique fingerprint.

    Cite this