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

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Page References

Incoming links Outgoing links
Exploratory Pattern Analytics
Exploratory Pattern Analytics

Version management

Difference between version and

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[{Image src='Screenshot from 2022-07-07 12-20-23.png' width=600}]
[{Image src='Screenshot from 2022-07-07 12-20-23.png' width=600}]
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__%%( color: #003399; font-size: 18px; )Type of tool:/%__ Local browser-based app
__%%( color: #003399; font-size: 30px;)Exploratory Pattern Analytics:__
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__%%( 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.
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- Process/material knowledge: Knowledge of the data being analysed
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- 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: __
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__%%( color: #003399; font-size: 18px; )Short description of the tool: /%__
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.
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 skills: __
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- Process/material knowledge: Knowledge of the data being analysed\\
- Digitalization knowledge: (Basic) knowledge of how to filter/preprocess data for analysis\\
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- 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: 16px;)Required programs %%( color: #003000; font-size: 14px;)(step-by-step guide and links provided in user guideline blow): __
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__%%( color: #003399; font-size: 18px; )Disclaimer:/%__
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)
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(Disclaimer Text)
\\
__%%( color: #003399; font-size: 16px; )Disclaimer:/%__
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__%%( color: #003399; font-size: 18px; )How to use/download/access it:/%__
Disclaimer of Warranty
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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].
There is no warranty for the program, to the extent
permitted by applicable law. Except when otherwise stated in writing the
copyright holders and/or other parties provide the program “as is”
without warranty of any kind, either expressed or implied, including,
but not limited to, the implied warranties of merchantability and
fitness for a particular purpose. The entire risk as to the quality and
performance of
the program is with you. should the program prove defective, you assume
the cost of all necessary servicing, repair or correction.
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__%%( color: #003399; font-size: 18px; )Description of the problem the tools solves:/%__
Limitation of Liability
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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.
In no event unless required by applicable law or agreed to
in writing will any copyright holder, or any other party who modifies
and/or conveys the program as permitted above, be liable to you for
damages, including any general, special, incidental or consequential
damages arising out of the use or inability to use the program
(including but not limited to loss of data or data being rendered
inaccurate or losses sustained by you or third parties or a failure of
the program to operate with any other programs), even if such holder or
other party has been advised of the possibility of such damages.
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__%%( color: #003399; font-size: 18px; )Contact person of the tool: /%__Dan Hudson
\\
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__%%( color: #003399; font-size: 18px; )Related tools:/%__
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- Analyse and Visualize your process data with data analytics -> [Data Analytics]
\\__%%( color: #003399; font-size: 24px;)This tool supports you to:__
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- Get guidance to set up a working data infrastucture -> [Data Infrastructure Wiki]
Explore your dataset, by finding interesting groups of data points within your data, which are described through simple patterns.
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- Find the right sensor to survey your process -> [Sensor Tool]
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- Improve internal information and material flow -> [VSM]
\\__%%( color: #003399; font-size: 18x;)Example use case:__
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- Match material requirements with material properties -> [Matrix]
For
example, the EPA tool could be used to better understand why certain known
outliers occur in process data. The Exploratory Pattern Analytics
tool makes it possible to find patterns that explain the data, in the
form of rules that associate a certain combination of properties to a
high probability of an outlier occurring. This can be an exploratory
process, using input from business experts to refine the patterns.
Ultimately, the rules found by the Exploratory Pattern Analytics tool
make it possible to identify conditions under which an anomaly is
more likely, making prediction easier and offering possible causal
explanations – so that the anomalous behavior can be managed more
effectively.
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\\__%%( color: #003399; font-size: 18x;)Example screenshots:__
%%columns
[{Image src='Screenshot from 2022-09-13 13-13-30.png' width=400}]
----
[{Image src='Screenshot from 2022-09-13 13-14-12.png' width=400}]
----
[{Image src='Screenshot from 2022-09-13 13-16-05.png' width=400}]
/%
\\__%%( 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 updates.pdf]
\\- The tool is available through the Data Analytics tool interface at: [https://github.com/cslab-hub/DataAnalytics_Diplast]. 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.
\\
\\
__%%( 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]
\\
\\__%%( 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