Type of tool: Local browser-based app

Required skills:

- Process/material knowledge: Knowledge of the data being analysed

- Digitalization knowledge: (Basic) knowledge of how to filter/preprocess data for analysis

Short description of the tool:

- Detailed description: Link to the guideline:

Disclaimer:

(Disclaimer Text)

How to use/download/access it:

e.g. got the the gitup [link], copythe code into [XY] and start using

Use case/problem: Selecting material (recyclate) for specificproduct requirements

Description of the problem the tools solves:

[General] + [Tool-specific]

Contact person of the tool: Stefan Bloemheuvel

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

Tool Description#

An important step in data analysis is data exploration, to achieve a betterunderstanding of the data. The Exploratory Pattern Analytics (EPA)tool works on prepared/preprocessed tabular data. It providesexplanatory patterns, i.e., simple rules between some parameters(e.g., temperature, pressure) that are predictive for a certaintarget parameter (e.g., scrap rate). This provides important insightsenhancing data understanding.For example, it could be used to better understand why certain known outliers occurin process data.

Guidelines#

Before you get started, take a look at the guidelines and make yourself familiar with how to use the tool.

Getting Started#

The tool is available through the Data Analytics tool interface at: https://github.com/cslab-hub/Data_Analytics_DIPLAST/tree/epa. The python interface for programmers is available at: https://github.com/cslab-hub/sd4py.