This page (revision-31) was last changed on 13-Sep-2022 16:14 by Dan Hudson

This page was created on 22-Mar-2022 15:18 by Dan Hudson

Only authorized users are allowed to rename pages.

Only authorized users are allowed to delete pages.

Page revision history

Version Date Modified Size Author Changes ... Change note
31 13-Sep-2022 16:14 5 KB Dan Hudson to previous
30 13-Sep-2022 16:09 5 KB Dan Hudson to previous | to last
29 13-Sep-2022 16:09 5 KB Dan Hudson to previous | to last
28 13-Sep-2022 16:03 5 KB Dan Hudson to previous | to last
27 13-Sep-2022 16:01 5 KB Dan Hudson to previous | to last
26 13-Sep-2022 15:56 4 KB Dan Hudson to previous | to last
25 13-Sep-2022 15:54 3 KB Dan Hudson to previous | to last
24 13-Sep-2022 15:52 3 KB Dan Hudson to previous | to last
23 06-Sep-2022 16:14 3 KB Sophia Botsch to previous | to last
22 06-Sep-2022 16:12 3 KB Sophia Botsch to previous | to last
21 22-Aug-2022 12:21 3 KB Jonas Umgelter to previous | to last

Page References

Incoming links Outgoing links
Exploratory Pattern Analytics
Exploratory Pattern Analytics

Version management

Difference between version and

At line 3 changed one line
__%%( color: #003399; font-size: 18px; )Type of tool:/%__ Local browser-based app
__%%( color: #003399; font-size: 30px;)Exploratory Pattern Analytics:__
At line 5 changed one line
__%%( color: #003399; font-size: 18px; )Required skills: /%__
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.
At line 7 removed one line
- Process/material knowledge: Knowledge of the data being analysed
At line 9 changed one line
- Digitalization knowledge: (Basic) knowledge of how to filter/preprocess data for analysis
\\__%%( color: #003399; font-size: 16px;)Type of tool:__ Local browser-based app \\
\\__%%( color: #003399; font-size: 16px;)Short description of the tool: __
The tool is meant to guide users into starting with using Data Analytics at their organization.\\
\\__%%( color: #003399; font-size: 16px;)Required skills: __\\
At line 11 changed one line
__%%( color: #003399; font-size: 18px; )Short description of the tool: /%__
- Process/material knowledge: Knowledge of the data being analysed\\
- Digitalization knowledge: (Basic) knowledge of how to filter/preprocess data for analysis\\
At line 13 changed 8 lines
- 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.
\\__%%( color: #003399; font-size: 16px;)Required programs %%( color: #003000; font-size: 14px;)(step-by-step guide and links provided in user guideline blow): __
\\- Python
\\- Java
\\- Anaconda
\\- Tool files from the GitHub (link below)
At line 22 changed 6 lines
- 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:/%__
__%%( color: #003399; font-size: 16px; )Disclaimer:/%__
At line 30 changed one line
__%%( color: #003399; font-size: 18px; )How to use/download/access it:/%__
\\__%%( color: #003399; font-size: 24px;)This tool supports you to:__
At line 32 changed one line
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].
- Visualize process data
At line 34 changed one line
__%%( color: #003399; font-size: 18px; )Description of the problem the tools solves:/%__
- Compare two production runs (one with virgin material and one with recyclate)
At line 36 changed 8 lines
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.
- Explore patterns inside a dataset
At line 45 removed one line
__%%( color: #003399; font-size: 18px; )Contact person of the tool: /%__Dan Hudson
At line 47 removed one line
__%%( color: #003399; font-size: 18px; )Related tools:/%__
At line 49 changed one line
- Analyse and Visualize your process data with data analytics -> [Data Analytics]
\\__%%( color: #003399; font-size: 18x;)Example use case:__
At line 51 changed one line
- Get guidance to set up a working data infrastucture -> [Data Infrastructure Wiki]
After validating your data with help of e.g., the data validation tool, we can begin analyzing to get further insights.
The Data Analytics tool is an interactive dashboard that helps with interpreting the variables in your dataset with help of visuals.
By providing your own data in the 'data' folder, several analytical options are at your disposal.
For example, a correlation plot that gives insights into which variables are behaving similarly, or a PCA analysis that will help determining the most influential variables in your dataset.
At line 53 changed one line
- Find the right sensor to survey your process -> [Sensor Tool]
\\__%%( color: #003399; font-size: 24px;)Tool guideline and access: __
\\ - ⚠️ We recommend to open and save the user guideline before proceeding: [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].
At line 55 changed one line
- Improve internal information and material flow -> [VSM]
\\__%%( color: #003399; font-size: 16px;)Contact person of the tool: __
Dan Hudson [mailto:daniel.dominic.hudson@uni-osnabrueck.de] the University fo Osnabrück.
At line 57 removed one line
- Match material requirements with material properties -> [Matrix]
At line 59 changed one line
 
\\
\\
__%%( color: #003399; font-size: 24px;)Related tools:__
At line 61 changed one line
 
__%%( 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]
This page (revision-31) was last changed on 13-Sep-2022 16:14 by Dan HudsonTop