@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix org: <http://www.w3.org/ns/org#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://data.nasa.gov/dataset/dec052d6-b9f0-4ae2-acb0-b1808894ba2d> a dcat:Dataset ;
    dct:description "This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed inferencing, data compaction, data modeling and classification tasks in many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Computing a global regression model from data available at the different peer-nodes using a traditional centralized algorithm for regression can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. This paper proposes a two-step approach to deal with this problem. First, it offers an efficient local distributed algorithm that monitors the “quality ” of the current regression model. If the model is outdated, it uses this algorithm as a feedback mechanism for rebuilding the model. The local nature of the monitoring algorithm guarantees low monitoring cost. Experimental results presented in this paper strongly support the theoretical claims." ;
    dct:identifier "DASHLINK_176" ;
    dct:issued "2010-09-22"^^xsd:date ;
    dct:modified "2020-01-29"^^xsd:date ;
    dct:publisher <https://data.nasa.gov/organization/dbb294e4-8597-44f5-aaf4-c0baac12cdbc> ;
    dct:title "An Efficient Local Algorithm for Distributed Multivariate Regression" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "Kanishka Bhaduri" ;
            vcard:hasEmail <mailto:kanishka.bhaduri-1@nasa.gov> ] ;
    dcat:distribution <https://data.nasa.gov/dataset/dec052d6-b9f0-4ae2-acb0-b1808894ba2d/resource/e048bca4-0580-4ea4-93b4-a6f9189916b5> ;
    dcat:keyword "ames",
        "dashlink",
        "nasa" .

<https://data.nasa.gov/dataset/dec052d6-b9f0-4ae2-acb0-b1808894ba2d/resource/e048bca4-0580-4ea4-93b4-a6f9189916b5> a dcat:Distribution ;
    dct:description "Paper" ;
    dct:format "PDF" ;
    dct:identifier "e048bca4-0580-4ea4-93b4-a6f9189916b5" ;
    dct:issued "2025-03-31T22:54:00.232995"^^xsd:dateTime ;
    dct:modified "2025-03-31T22:54:00.220788"^^xsd:dateTime ;
    dct:title "Regression.pdf" ;
    dcat:accessURL <https://c3.nasa.gov/dashlink/static/media/publication/Regression.pdf> ;
    dcat:mediaType "application/pdf" .

<https://data.nasa.gov/organization/dbb294e4-8597-44f5-aaf4-c0baac12cdbc> a org:Organization ;
    skos:prefLabel "NASA" ;
    foaf:name "NASA" .