[{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]