{"created":"2025-01-19T01:15:57.624230+00:00","updated":"2025-01-19T16:16:56.979407+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00215183","sets":["6504:10735:10807"]},"path":["10807"],"owner":"44499","recid":"215183","title":["分散ストリーム処理フレームワークを用いた動作識別処理性能の調査"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"7dc499e5-2d81-4e29-9c6c-7b4216871a56"},"_deposit":{"id":"215183","pid":{"type":"depid","value":"215183","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"分散ストリーム処理フレームワークを用いた動作識別処理性能の調査","author_link":["553929","553927","553928","553926"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"分散ストリーム処理フレームワークを用いた動作識別処理性能の調査"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2021-03-04","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"お茶の水女子大"},{"subitem_text_value":"NII"},{"subitem_text_value":"産総研"},{"subitem_text_value":"お茶の水女子大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/215183/files/IPSJ-Z83-4U-02.pdf","label":"IPSJ-Z83-4U-02.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-29"}],"format":"application/pdf","filename":"IPSJ-Z83-4U-02.pdf","filesize":[{"value":"539.2 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"547f0ace-3993-4cf4-be7b-e9f86ffb8957","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"高崎, 智香子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹房, あつ子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中田, 秀基"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小口, 正人"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"センサ機器やクラウドコンピューティングの普及により，一般家庭で取得，蓄積した動画像が子供やお年寄りの見守りサービスや防犯対策，セキュリティに活用されるようになってきた.家庭のセンサで取得した動画像をリアルタイムに機械学習を用いて解析するには，データ転送量と解析計算量が課題となる.我々は，センサ側で姿勢推定ライブラリOpenPoseを使用して動画像から関節の特徴量データを抽出して転送し，クラウドでその特徴量データのみを用いて機械学習による動作識別を行うことで，処理遅延やプライバシの問題に対処するセンサとクラウドでの分散処理手法を提案している.しかし，複数家庭のセンサから連続的に送られる大量のデータをクラウドで処理するには，急激なデータの増加によるシステム負荷上昇に耐えうる処理基盤が必要である.本研究では，大量のデータを効率よく処理可能な分散ストリーム処理基盤の構築を目指して，エッジで抽出した関節の特徴量データをApache Kafkaを用いて収集し，クラウドにおいてApache Flinkの分散ストリーム処理機能を用いて機械学習処理を行うシステムを構築し，解析スループットを調査する.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"168","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"167","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":215183,"links":{}}