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    • Orca

    Metadata Updated: August 22, 2025

    Orca is a data-driven, unsupervised anomaly detection algorithm that uses a distance-based approach. It uses a novel pruning rule that allows it to run in nearly linear time. Orca was co-developed by Stephen Bay of ISLE and Mark Schwabacher of NASA ARC. More information about Orca, including downloadable software, can be found here:

    http://stephenbay.net/orca/

    A conference paper about Orca can be found here:

    https://dashlink.arc.nasa.gov/paper/mining-distance-based-outliers-in-near-linear-time/

    Access & Use Information

    Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

    Downloads & Resources

    Dates

    Metadata Created Date November 12, 2020
    Metadata Updated Date August 22, 2025
    Data Update Frequency irregular

    Metadata Source

    Harvested from NASA Data.json

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date November 12, 2020
    Metadata Updated Date August 22, 2025
    Publisher Dashlink
    Maintainer
    Identifier DASHLINK_121
    Data First Published 2010-09-10
    Data Last Modified 2025-07-17
    Public Access Level public
    Data Update Frequency irregular
    Bureau Code 026:00
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Harvest Object Id abfc0319-7f1e-48e1-aca4-b755e200c1ac
    Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
    Harvest Source Title NASA Data.json
    Homepage URL https://c3.nasa.gov/dashlink/resources/121/
    Program Code 026:029
    Source Datajson Identifier True
    Source Hash 57e39dfcaaceea2ad3f7bccc2002ba8d845083666c5ceb7b2659e646a2050131
    Source Schema Version 1.1

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