--- description: KNIME Analytics Platformを実際に使用したユーザーのレビューから、製品の機能や価格、メリットデメリットをご覧いただけます。類似製品との比較も簡単、ぴったりのSaaSが見つかるはず！ image: https://gdm-localsites-assets-gfprod.imgix.net/images/capterra/og_logo-e5a8c001ed0bd1bb922639230fcea71a.png?auto=format%2Cenhance%2Ccompress title: KNIME Analytics Platformとは？ | 機能や料金、導入事例をご紹介【キャプテラ】 --- 現在地表示: [ホーム](/) > [予測分析ツール](/directory/30945/predictive-analytics/software) > [KNIME Analytics Platform](/software/158739/knime-analytics-platform) # KNIME Analytics Platform Canonical: https://www.capterra.jp/software/158739/knime-analytics-platform ページ：1 / 2\ 次へ: [次ページ](https://www.capterra.jp/software/158739/knime-analytics-platform?page=2) > 予測分析を活用したデータサイエンス・ワークフローを構築できるツールを備えた、クラウドベースのソリューションです。 > > 評価：25人のユーザーによる評価は**4.6/5**。**おすすめ度**で最高の評価。 ----- ## 概要 ### KNIME Analytics Platformの対象ユーザー 予測分析、データベース管理、データのインポートおよびエクスポート、コラボレーションなどを活用した、データサイエンス・ワークフローを構築できるツールを備えたクラウドベースのプラットフォームです。あらゆる規模の企業向けに設計されています。 ## 簡単な統計と評価 | 測定基準 | 評価 | 詳細 | | **総合評価** | **4.6/5** | 25 レビュー | | 使いやすさ | 4.5/5 | レビュー全体に基づく | | カスタマー・サポート | 3.9/5 | レビュー全体に基づく | | 価格の妥当性 | 4.7/5 | レビュー全体に基づく | | 機能 | 4.4/5 | レビュー全体に基づく | | おすすめ率 | 90% | (9/10 おすすめ度) | ## 企業情報 - **キャプテラについて**: KNIME.COM - **ロケーション**: Zurich, スイス ## ビジネスコンテキスト - **価格プラン**: €0.00 - **価格モデル**: (無料版が利用可能) - **対象となる企業**: 自営業, 2～10, 11～50, 51～200, 201～500, 501～1,000, 1,001～5,000, 5,001～10,000, 10,000+ - **デプロイとプラットフォーム**: クラウド、SaaS、ウェブベース, Mac（デスクトップ）, Windows（デスクトップ）, Linux（デスクトップ）, Linux（オンプレミス） - **サポートされる言語**: 英語 - **利用可能な国**: アイスランド, アイルランド, アゼルバイジャン, アフガニスタン, アメリカ合衆国, アラブ首長国連邦, アルジェリア, アルゼンチン, アルバ, アルバニア, アルメニア, アンギラ, アンゴラ, アンティグア・バーブーダ, アンドラ, イエメン, イギリス, イスラエル, イタリア, イラク さらに208件 ## 機能 - Publishing/Sharing - アドホック・レポート - コラボレーションツール - ダッシュボード - ディープラーニング - データのインポート／エクスポート - データの可視化 - データ・コネクタ - データ検出 - パフォーマンス測定基準 - ビジュアル分析 - モデリング、シミュレーション - モデルトレーニング - ワークフロー管理 - 予測モデリング - 予測分析 - 事業予測 - 機械学習アルゴリズム・ライブラリ - 自然言語処理 - 複数データソース ## 統合 (合計26件) - Amazon Aurora - Amazon Comprehend - Amazon DynamoDB - Amazon EMR - Amazon S3 - Apache Hive - Azure Blob Storage - Azure Data Lake Storage - Azure Databricks - Azure Synapse Analytics - ChatGPT - Cloudera Enterprise - Databricks - Google Cloud Storage - Google Sheets ... さらに11件の統合 ## サポートのオプション - メール/ヘルプデスク - FAQ/フォーラム - ナレッジベース - 電話サポート ## Category - [予測分析ツール](https://www.capterra.jp/directory/30945/predictive-analytics/software) ## 関連カテゴリー - [予測分析ツール](https://www.capterra.jp/directory/30945/predictive-analytics/software) - [機械学習ツール](https://www.capterra.jp/directory/31103/machine-learning/software) - [BIツール](https://www.capterra.jp/directory/23/business-intelligence/software) ## 代替製品 1. [Tableau](https://www.capterra.jp/software/77260/tableau) — 4.6/5 (2351 reviews) 2. [Domo](https://www.capterra.jp/software/119119/domo) — 4.3/5 (330 reviews) 3. [JMP](https://www.capterra.jp/software/151815/jmp-statistical-software) — 4.5/5 (53 reviews) 4. [Google Cloud](https://www.capterra.jp/software/170983/google-cloud-platform) — 4.7/5 (2281 reviews) 5. [Adverity](https://www.capterra.jp/software/162524/datatap) — 4.5/5 (26 reviews) ## レビュー ### "Well created open source for data analysis\!" — 5.0/5 > **Rochelle** | *2023年1月28日* | 情報技術、情報サービス | おすすめ評価：10.0/10 > > **良いポイント**: One of the pros is of course doesn't require license fee. It is also an open source that can connect to Python and R that is capable of customization. Need to mention also the good community support. > > **改善点**: It took time to understand the functionalities and familiarize the user interface. ----- ### "Data Science 101 Platform for non-IT people" — 4.0/5 > **Ferhat** | *2020年1月25日* | 情報技術、情報サービス | おすすめ評価：7.0/10 > > **良いポイント**: - Its ease of use makes it possible for non-IT, non-developer, non-CS background people to make data manipulation, preprocessing, mining, visualization and modelling. - It has a graphical interface with nodes and connections so that you don't need to know Python/R to make predictive models or association rules/recommendation systems. - There's a vast library of functions - Even more functions are created by the community so non-existing customized functions are created by the community, via existing functions. - The visual flow of data makes it easy to understand and interpret it. - It teaches the CRISP-DM methodology in an intuitive way thanks to its graphical user interface - It can connect to SQL and similar servers so that the data can be read directly. - It is possible to write own Python/R script for custom needs. > > **改善点**: - Custom needs are hard to carry out. - Functions have limited abilities and parameters - Data visualization is weak and relatively primitive - Model development is easy but deployment is hard - It is very slow unfortunately and I think this is KNIME's most important drawback > > It was the tool I learned the Data Science in the first place. So it is really good and intuitive with its graphical interface. For example you understand train-test split very well because you literally see the split as you work on it. As I progressed and needed more functions and more custom solutions, I started using Python scripts and solved it like that. So it gave me all these abilities. ----- ### "Solid Platform for Small Datasets and Broad Data Connectivity" — 4.0/5 > **認証済みレビュアー** | *2020年5月1日* | 健康、ウェルネス、フィットネス | おすすめ評価：6.0/10 > > **良いポイント**: There is a wide range of tools to process and prep data in the platform natively and additional tools that can be download within the platform. The ability to customize the settings for most of the tools allows the user to adjust the output. Even more technical settings, like hyperparameter tuning, can be done in the tool UI. There are numerous input and output options and types. > > **改善点**: Pulling in very basic files, like Excel spreadsheets can be a bit challenging where other platforms handle files with ease. Also, database connections are not seamless. The Java memory errors also limit the size of data that can be processed without making manual adjustments to settings. Lastly, not being a cloud-based platform, processing big data is very time-consuming. > > The two main reasons we used KNIME were to process and prep data, then to conduct machine learning by training models and processing predictions. KNIME is great with data prep and blend as long as the data set is small to medium in size (\< 4GB). There were areas where we struggled and that was when models were more complex (\> 50 variables) and being able to deploy and schedule jobs. We had to download JDBC drivers for our database connections, which was not something we had to do with other platforms. ----- ### "Great for all types of data scientists" — 5.0/5 > **認証済みレビュアー** | *2020年9月26日* | 高等教育 | おすすめ評価：9.0/10 > > **良いポイント**: Some drag and drop tools for machine learning are really limited, but KNIME is not. There are a ton of capabilities of the tool that are built in, and there are even more that are available online, like AutoML. It gives citizen data scientists the ability to create good models without knowing a programming language, and it increases the bandwidth of actual data scientists by allowing them to easily create more models and experiments. > > **改善点**: Of course, it is more limited than a programming language, and if you're familiar with building models programmatically, there is a learning curve that will slow you down and limit you at first. > > I have had a very positive experience with KNIME and like it a lot more than other drag and drop machine learning tools I have tried out. ----- ### "Using KNIME for reporting" — 5.0/5 > **Sasha** | *2024年1月12日* | 情報技術、情報サービス | おすすめ評価：8.0/10 > > **良いポイント**: KNIME allowed me to pull data from large google sheets and manipulate them in a clear and easy way. The visual representation of each node makes it really easy to use and understand even for people without a background in data analytics. The KNIME website also provides a lot of resources on using the platform > > **改善点**: Very large google sheets containing a lot of data cannot always be extracted due to the size. > > Good and would recommend to non technical professionals as well ----- ページ：1 / 2\ 次へ: [次ページ](https://www.capterra.jp/software/158739/knime-analytics-platform?page=2) ## リンク - [Capterraで表示する](https://www.capterra.jp/software/158739/knime-analytics-platform) ## このページは以下の言語で利用できます | ロケール | URL | | de | | | de-AT | | | de-CH | | | en | | | en-AE | | | en-AU | | | en-CA | | | en-GB | | | en-IE | | | en-IL | | | en-IN | | | en-NZ | | | en-SG | | | en-ZA | | | es | | | es-AR | | | es-CL | | | es-CO | | | es-CR | | | es-DO | | | es-EC | | | es-MX | | | es-PA | | | es-PE | | | fr | | | fr-BE | | | fr-CA | | | fr-LU | | | ja | | ----- ## 構造化データ