[{Image src='Screenshot from 2022-07-07 12-20-23.png' width=600}] __%%( color: #003399; font-size: 18px; )Type of tool:/%__ Local browser-based app __%%( color: #003399; font-size: 18px; )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: 18px; )Short description of the tool: /%__ - Description: 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. - Link to the guideline: Before you get started, take a look at the [guidelines|Paper mill EPA tool example updated.odt] and make yourself familiar with how to use the tool. __%%( color: #003399; font-size: 18px; )Disclaimer:/%__ (Disclaimer Text) __%%( color: #003399; font-size: 18px; )How to use/download/access it:/%__ 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: 18px; )Description of the problem the tools solves:/%__ 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: 18px; )Contact person of the tool: /%__Dan Hudson __%%( color: #003399; font-size: 18px; )Related tools:/%__ - Analyse and Visualize your process data with data analytics -> [Data Analytics] - Get guidance to set up a working data infrastucture -> [Data Infrastructure Wiki] - Find the right sensor to survey your process -> [Sensor Tool] - Improve internal information and material flow -> [VSM] - Match material requirements with material properties -> [Matrix]