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__%%( 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: __
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\\__%%( 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:/%__
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\\__%%( color: #003399; font-size: 24px;)This tool supports you to:__ 

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\\__%%( color: #003399; font-size: 18x;)Example use case:__ 

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\\__%%( 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.


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__%%( 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]
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\\__%%( 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]