{"links":{},"id":193638,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00193638","sets":["6164:6165:6640:9657"]},"path":["9657"],"owner":"44499","recid":"193638","title":["RNNによるネットワークトラフィック変動の予測"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-06-27"},"_buckets":{"deposit":"f052f147-865f-44e6-8582-26c8e5c4cbe5"},"_deposit":{"id":"193638","pid":{"type":"depid","value":"193638","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"RNNによるネットワークトラフィック変動の予測","author_link":["454169","454166","454168","454167","454165"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"RNNによるネットワークトラフィック変動の予測"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"AI","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2018-06-27","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"お茶の水女子大学"},{"subitem_text_value":"東京大学"},{"subitem_text_value":"東京大学"},{"subitem_text_value":"工学院大学"},{"subitem_text_value":"お茶の水女子大学"}]},"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/193638/files/IPSJ-DICOMO2018211.pdf","label":"IPSJ-DICOMO2018211.pdf"},"date":[{"dateType":"Available","dateValue":"2020-06-27"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2018211.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"b755838d-dd8f-4382-803c-6feadad79be8","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"小山内, 遥香"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中尾, 彰宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山本, 周"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山口, 実靖"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"通信障害は，大規模災害による被災地内外の通信過多による輻輳，同時に起こる OS アップデートや DDoS 攻撃など，様々な原因で引き起こされる．これらの通信障害は起こってからでは，対応が手遅れである場合が多く，確度の高い予測をし，事前に輻輳を抑制することが重要である．網内における機械学習により，トラフィック集中を早期に検知し，効率的に対応するための技術に期待が集まっている．本論文では，深層学習のモデルの一種である Recurrent Neural Network (RNN) を用いてトラフィック異状の情報を抽出し，トラフィック変動の兆候を掴むための手法を提案する．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1425","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散協調とモバイルシンポジウム2018論文集"}],"bibliographicPageStart":"1419","bibliographicIssueDates":{"bibliographicIssueDate":"2018-06-27","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2018"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T00:58:51.148500+00:00","updated":"2025-01-19T23:51:21.210347+00:00"}