[{Image src='Screenshot from 2022-07-07 12-20-23.png' width=600}] __%%( color: #003399; font-size: 30px;)Exploratory Pattern Analytics:__ An important step in data analysis is data exploration, to achieve a better understanding of the data. The Exploratory Pattern Analytics (EPA) tool works on prepared/preprocessed tabular data. It provides explanatory patterns, i.e., simple rules between some parameters (e.g., temperature, pressure) that are predictive for a certain target parameter (e.g., scrap rate). This provides important insights enhancing data understanding.For example, it could be used to better understand why certain known outliers occur in process data. \\__%%( color: #003399; font-size: 16px;)Type of tool:__ Local browser-based app \\ \\__%%( color: #003399; font-size: 16px;)Short description of the tool: __ (Text)\\ \\__%%( color: #003399; font-size: 16px;)Required skills: __\\ - Process/material knowledge: Knowledge of the data being analysed\\ - Digitalization knowledge: (Basic) knowledge of how to filter/preprocess data for analysis\\ \\__%%( color: #003399; font-size: 16px;)Required programs %%( color: #003000; font-size: 14px;)(step-by-step guide and links provided in user guideline blow): __ \\The EPA tool is integrated into the Data Analytics tool which requires the following programs: \\- Python \\- Java \\- Anaconda \\- Tool files from the GitHub (link below) __%%( color: #003399; font-size: 16px; )Disclaimer:/%__ (Disclaimer Text) \\__%%( color: #003399; font-size: 24px;)This tool supports you to:__ - (Text) \\__%%( color: #003399; font-size: 18x;)Example use case:__ (Text) \\__%%( color: #003399; font-size: 24px;)Tool guideline and access: __ \\ - ⚠️ We recommend to open and save the user guideline before proceeding. The guidline includes a detailed description of how to use the EPA tool with the example of a paper mill production process: [guidelines|Paper mill EPA tool example updated.odt] \\- The tool is available through the Data Analytics tool interface at: [https://github.com/cslab-hub/Data_Analytics_DIPLAST/tree/epa]. Installation instructions are included in the file named "Installation.docx". The python interface for programmers is available at: [https://github.com/cslab-hub/sd4py]. \\__%%( color: #003399; font-size: 16px;)Contact person of the tool: __ Dan Hudson [mailto:daniel.dominic.hudson@uni-osnabrueck.de] the University fo Osnabrück. \\ \\ __%%( color: #003399; font-size: 24px;)Related tools:__ __%%( color: #003399; font-size: 16px;) Before applying this tool:__ \\We recommend also taking a look at the following Di-Plast tools below. They can help you to gather necessary information and data, help to better prepare your data and continue working with it afterwards: \\--> Improve internal information and material flow -> [VSM] \\--> Get guidance to set up a working data infrastructure -> [Data Infrastructure Wiki] \\--> Find the right sensor to survey your process -> [Sensor Tool] \\--> Validate your process data -> [Data Validation] \\ \\__%%( color: #003399; font-size: 16px;)After applying this tool:__ \\-->Analyse and Visualize your process data with data analytics -> [Data Analytics] \\-->Match material requirements with material properties -> [Matrix]