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Find out how analytics, from data mining to cognitive computing, is changing the way we do business. COVID related lockdowns around the world resulted in changes to the way that we work, study and live. Some of these changes were positive: better access to remote learning and working for those who want it, for example. However, there were other less positive developments, including a huge rise in. SAS has been extending technological boundaries for many years.
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Codw modern statistical techniques incorporate randomness: simulation, bootstrapping, random forests, and so forth. To use the technique, you need to specify a seed value, which determines pseudorandom numbers that are used in the algorithm.
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I have previously blogged about ways to perform balanced bootstrap resampling in SAS. SAS ViyaではCData JDBC Driverを使って下記のソーシャルメディア・ファイルストレージサービスにシームレスにかつ、素早く連結できます。 ・Facebook ・Google Analytics ・Google Drive ・Microsoft OneDrive ・Odata ・Twitter ・YouTube Analytics /42193.txt JDBCドライバーを使ってTwitterと連携し、「天気」に関するツイートを取得してみたいと思います。順番通り説明しますので、最後までお読みいただき、皆さんも是非ご活用ください。 1.
Twitter API利用申請 Twitter Developer PlatformにてTwitter APIの利用申請を行います。申請にあたり、名前と住んでいる地域、利用目的などの情報を提供する必要がありますので、事前に用意しておいてください。また、利用申請の承認はTwitter側で数日かかる場合がありますのでご了承ください。 Twitter APIの利用申請が終わったら、申請完了のメールが届きます。 また、申請の検討が終わり、Twitter APIが利用できる状態になりましたら、「Account Application Approved」というメールが届きます。 2. CData Twitter JDBC Driverインストール インストールにはSASの契約とは別途、CData社との契約が必要ですが、30日間トライアルで使うことも可能ですので、ご紹介します。 まず、CData Twitter Ckltured Driverインストールページにアクセスします。 次に、Downloadクリックします。 Download Trialをクリックします。 適切なOSを選択してDownloadをクリックします。今回はWindowsを選択しました。 ダウンロードされたTwitterJDBCDriver.
exeファイルを開き、画面に表示されるステップに従ってインストールを完了します。 3. Connection String生成. 医者の診断に例えて学ぶ AIを用いたセンサデータ分析システムに関するよくある誤解について 製造業で盛んに導入されているセンサ。そのセンサデータを分析してビジネスインパクトのある結果を出すには、どのようにしたら良いのでしょうか？ データ分析を成功させるためには、様々な要素が考えられますが、ここではセンサデータの質に注目したいと思います。いくら高度なデータ分析手法を用いても、分析対象のセンサデータが正しく取得できていない場合は、結果が出ないことは容易に想像できますが、あまり議論されることはありません。 これは、センサ計測とデータ分析の両方を視野に入れた幅広いノウハウが必要となり、Information Technology IT と Operational Technology OT との融合という課題に行き着くためです。 本ブログでは、このマニアックな話題を、医者の診断に例えながら、わかりやすく解説していきます。 今回から、「自覚症状が無いセンサデータの品質問題」に関連した話題をお伝えしていきます。 結果が出ないPoC（Proof of Concept：概念実証） SASは世界各国に支社を持ち、製造業DXの実現に向けた数多くのデータ分析案件を取り扱っています。 よく頂く御相談内容としては、生産品の品質管理と設備保全系に関連するデータ分析システムの導入検討です。（図１） 図１.
生産ライン向けDXとしてよくある御相談 ところが、PoCとしてセンサデータを用いてデータ分析をしているが、思うような結果が得られていないというケースが市場で発生しています。多くの方がデータ分析手法に問題があるのではないかと考え、データ分析のスペシャリストである弊社に御連絡を頂きます。たしかに分析手法の問題もあり、原因は様々ですが、意外と盲点になっているのが分析対象となるセンサデータ自体の品質問題です。 センサデータの品質問題とは何か？ データ分析はデータ収集から始まります。そして、そのデータの質が分析結果に影響を与えることは容易に想像できます。図２はセンサデータ分析システムの構築の流れを示しています。システム構築は、データ収集からスタートし、データ蓄積、そしてデータ分析という順番で実施され、手動でデータ分析の結果が出るようになった段階で自動化するという流れが一般的です。 図２. AIを用いたセンサデータ分析システムの開発の流れ 自覚症状が無いセンサデータの品質問題 この問題の恐ろしい点は、システム開発に携わっている関係者の皆様にとって自覚症状が表れない場合が多いことです。 そもそも、データ分析の結果が出ない原因が、上述のセンサデータの質に関係していることを、どうやって判断すれば良いのでしょうか？ 当然、他の原因も考えられます。 先日、お医者様と健康診断の検査結果のお話をした際に気がついたのですが、お医者様は検査データの意味や限界、誤差要因をよく御存知のようでした。そして総合的に私の健康状態を判断しておられるようでした。思わず、その秘密を知りたいと思い質問してしまったのですが、お医者様の回答は「過去の事例と経験即かなぁ～～？？」と、お答えいただきました。 ということで、次回以降、私の経験即に基づいたチェックポイントを御紹介していきます。 次回に続く.
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