Data Analysis Dashboard - Revision history

Admino739mjm7: /* Key Features */

2025-10-20T09:17:41Z

Key Features

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	*Upload datasets via file input or URL.		*Upload datasets via file input or URL.
	*Manage missing values using various imputation methods.		*Manage missing values using various imputation methods.
	*Encode categorical variables and coerce numeric data.		*Encode categorical variables and coerce numeric data.</br>


	[[File:1.data preprocessing.png\|center\|x300px\|Image Caption]]		[[File:1.data preprocessing.png\|center\|x300px\|Image Caption]]
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	*Knowledge Graph Creation:		*Knowledge Graph Creation:
	*Automatically generate RDF graphs representing significant correlations.		*Automatically generate RDF graphs representing significant correlations.
	*Define relationships using user-specified thresholds.		*Define relationships using user-specified thresholds.</br>


	[[File:2.correlation analysis.png\|center\|x300px\|Image Caption]]		[[File:2.correlation analysis.png\|center\|x300px\|Image Caption]]

Admino739mjm7: /* Key Features */

2025-10-20T09:17:24Z

Key Features

Admino739mjm7: /* Usage */

2025-10-20T09:16:43Z

Usage

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	* Spearman Correlation: Set a rank-based similarity threshold to capture monotonic relationships.		* Spearman Correlation: Set a rank-based similarity threshold to capture monotonic relationships.
	* Euclidean Similarity: Define distance-based thresholds for evaluating proximity between data points.		* Euclidean Similarity: Define distance-based thresholds for evaluating proximity between data points.
	This customization allows users to align the analysis with the needs of their domain or study.		This customization allows users to align the analysis with the needs of their domain or study.</br>


	4. Analyze Data and Visualize Results:		4. Analyze Data and Visualize Results:

Admino739mjm7: /* Usage */

2025-10-20T09:16:25Z

Usage

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	2. Select Columns for Analysis:		2. Select Columns for Analysis:
	Choose specific pairs or groups of columns from the dataset to focus on correlation analysis. This step allows users to isolate meaningful relationships and ensures the analysis remains targeted to the relevant data dimensions.		Choose specific pairs or groups of columns from the dataset to focus on correlation analysis. This step allows users to isolate meaningful relationships and ensures the analysis remains targeted to the relevant data dimensions.</br>

	3. Set Thresholds for Similarity Metrics:		3. Set Thresholds for Similarity Metrics:

Admino739mjm7: /* Usage */

2025-10-20T09:15:55Z

Usage

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	<p style="line-height: 1.5em">		<p style="line-height: 1.5em">
	1. Upload a Dataset:		1. Upload a Dataset:
	Begin by uploading a dataset in .csv format. This serves as the primary input for analysis. The system supports datasets of varying sizes, ensuring flexibility for small exploratory analyses or large-scale data studies.		Begin by uploading a dataset in .csv format. This serves as the primary input for analysis. The system supports datasets of varying sizes, ensuring flexibility for small exploratory analyses or large-scale data studies.</br>

	2. Select Columns for Analysis:		2. Select Columns for Analysis:

Admino739mjm7 at 09:15, 20 October 2025

2025-10-20T09:15:17Z

Admino739mjm7: /* Usage */

2025-10-20T05:42:24Z

Usage

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	• Perform complex queries to explore relationships, extract specific subsets of data, or validate inferred connections.		• Perform complex queries to explore relationships, extract specific subsets of data, or validate inferred connections.
	• This feature integrates the power of semantic querying, enabling users to uncover insights that are not immediately apparent in the raw dataset.		• This feature integrates the power of semantic querying, enabling users to uncover insights that are not immediately apparent in the raw dataset.


	~~<p style="line-height: 1.5em">~~
	~~''Note: The asset is under Ongoing Development''~~
	~~</p>~~

Admino739mjm7: /* Usage */

2025-10-20T05:41:57Z

Usage

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	• This feature integrates the power of semantic querying, enabling users to uncover insights that are not immediately apparent in the raw dataset.		• This feature integrates the power of semantic querying, enabling users to uncover insights that are not immediately apparent in the raw dataset.



	* Upload a CSV file containing the data to be analysed
	* Configure the data preprocessing aspects (handling missing values and data types)

	~~Afterwards the user can explore their data and underlying relations , extracting knowledge in a flexible way:~~
	* Select pairs of features and see the visualisations of correlation analysis



	* Build a knowledge graph that represents significant relationships in the data
	* Interact directly with the knowledge graph through a SPARQL query interface

	~~[[File:Data analysis dash 3.png\|center\|x300px\|Image Caption]]~~
	~~<div align="center" style="font-size:88%;line-height: 2em">''Image 3: Visualisation, querying and inference''</div>~~

	~~</p>~~
	~~<br>~~

	<p style="line-height: 1.5em">		<p style="line-height: 1.5em">

Admino739mjm7: /* Key Features */

2025-10-20T05:41:28Z

Key Features

← Older revision		Revision as of 05:41, 20 October 2025
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	*Automatically generate RDF graphs representing significant correlations.		*Automatically generate RDF graphs representing significant correlations.
	*Define relationships using user-specified thresholds.		*Define relationships using user-specified thresholds.

			[[File:2.correlation analysis.png\|center\|x300px\|Image Caption]]
			<div align="center" style="font-size:88%;line-height: 2em">''Image 3: Correlation Analysis''</div>


	<strong>Inference Rules:</strong>		<strong>Inference Rules:</strong>
	*Input custom IF-THEN rules to add inferred relationships to the RDF graph.		*Input custom IF-THEN rules to add inferred relationships to the RDF graph.

			[[File:3.inference rules.png\|center\|x300px\|Image Caption]]
			<div align="center" style="font-size:88%;line-height: 2em">''Image 4: Inference Rules''</div>

	<strong>Visualization:</strong>		<strong>Visualization:</strong>

Admino739mjm7: /* Key Features */

2025-10-20T05:39:32Z

Key Features

← Older revision		Revision as of 05:39, 20 October 2025
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	*Manage missing values using various imputation methods.		*Manage missing values using various imputation methods.
	*Encode categorical variables and coerce numeric data.		*Encode categorical variables and coerce numeric data.

			[[File:1.data preprocessing.png\|center\|x300px\|Image Caption]]
			<div align="center" style="font-size:88%;line-height: 2em">''Image 2: Data Preprocessing''</div>

	<strong>Correlation Analysis:</strong>		<strong>Correlation Analysis:</strong>

← Older revision		Revision as of 09:17, 20 October 2025
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	<strong>Inference Rules:</strong>		<strong>Inference Rules:</strong>
	*Input custom IF-THEN rules to add inferred relationships to the RDF graph.		*Input custom IF-THEN rules to add inferred relationships to the RDF graph.</br>


	[[File:3.inference rules.png\|center\|x300px\|Image Caption]]		[[File:3.inference rules.png\|center\|x300px\|Image Caption]]
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	<strong>Visualization:</strong>		<strong>Visualization:</strong>
	*Visualize correlations using interactive Plotly subplots.		*Visualize correlations using interactive Plotly subplots.
	*Display the knowledge graph as a network with customizable aesthetics.<br>		*Display the knowledge graph as a network with customizable aesthetics.</br>


	[[File:4.visualisation.png\|center\|x300px\|Image Caption]]		[[File:4.visualisation.png\|center\|x300px\|Image Caption]]
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	<strong>SPARQL Querying:</strong>		<strong>SPARQL Querying:</strong>
	*Query the RDF graph using SPARQL with a user-friendly interface.<br>		*Query the RDF graph using SPARQL with a user-friendly interface.</br>

	[[File:5. sparql.png\|center\|x300px\|Image Caption]]		[[File:5. sparql.png\|center\|x300px\|Image Caption]]
	<div align="center" style="font-size:88%;line-height: 2em">''Image 5: SPARQL Querying''</div>		<div align="center" style="font-size:88%;line-height: 2em">''Image 5: SPARQL Querying''</div>

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	<strong>Dashboard that allows human operators monitor and extract knowledge from tabular data through visualization/interpretability, querying, and inference features.</strong> ~~[[File:Data analysis dash 1.jpg\|thumb\|right\|<div style="font-size:88%;line-height: 1.5em">Image 1: The main view of the Data Analysis Dashboard</div>]]~~		<strong>Dashboard that allows human operators monitor and extract knowledge from tabular data through visualization/interpretability, querying, and inference features.</strong>


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	[[File:1.data preprocessing.png\|center\|x300px\|Image Caption]]		[[File:1.data preprocessing.png\|center\|x300px\|Image Caption]]
	<div align="center" style="font-size:88%;line-height: 2em">''Image 2: Data Preprocessing''</div>		<div align="center" style="font-size:88%;line-height: 2em">''Image 1: Data Preprocessing''</div>

	<strong>Correlation Analysis:</strong>		<strong>Correlation Analysis:</strong>
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	[[File:2.correlation analysis.png\|center\|x300px\|Image Caption]]		[[File:2.correlation analysis.png\|center\|x300px\|Image Caption]]
	<div align="center" style="font-size:88%;line-height: 2em">''Image 3: Correlation Analysis''</div>		<div align="center" style="font-size:88%;line-height: 2em">''Image 2: Correlation Analysis''</div>


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	[[File:3.inference rules.png\|center\|x300px\|Image Caption]]		[[File:3.inference rules.png\|center\|x300px\|Image Caption]]
	<div align="center" style="font-size:88%;line-height: 2em">''Image 4: Inference Rules''</div>		<div align="center" style="font-size:88%;line-height: 2em">''Image 3: Inference Rules''</div>

	<strong>Visualization:</strong>		<strong>Visualization:</strong>
	*Visualize correlations using interactive Plotly subplots.		*Visualize correlations using interactive Plotly subplots.
	*Display the knowledge graph as a network with customizable aesthetics.''		*Display the knowledge graph as a network with customizable aesthetics.<br>

			[[File:4.visualisation.png\|center\|x300px\|Image Caption]]
			<div align="center" style="font-size:88%;line-height: 2em">''Image 4: Data Visualisation''</div>

	<strong>SPARQL Querying:</strong>		<strong>SPARQL Querying:</strong>
	*Query the RDF graph using SPARQL with a user-friendly interface.		*Query the RDF graph using SPARQL with a user-friendly interface.<br>
			[[File:5. sparql.png\|center\|x300px\|Image Caption]]
			<div align="center" style="font-size:88%;line-height: 2em">''Image 5: SPARQL Querying''</div>

	</p>		</p>
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	3. Set Thresholds for Similarity Metrics:		3. Set Thresholds for Similarity Metrics:
	Fine-tune the thresholds for multiple similarity measures using an intuitive sidebar interface:		Fine-tune the thresholds for multiple similarity measures using an intuitive sidebar interface:
	• Pearson Correlation: Adjust the threshold to measure linear relationships between variables.		* Pearson Correlation: Adjust the threshold to measure linear relationships between variables.
	• Spearman Correlation: Set a rank-based similarity threshold to capture monotonic relationships.		* Spearman Correlation: Set a rank-based similarity threshold to capture monotonic relationships.
	• Euclidean Similarity: Define distance-based thresholds for evaluating proximity between data points.		* Euclidean Similarity: Define distance-based thresholds for evaluating proximity between data points.
	This customization allows users to align the analysis with the needs of their domain or study.		This customization allows users to align the analysis with the needs of their domain or study.

	4. Analyze Data and Visualize Results:		4. Analyze Data and Visualize Results:
	Gain deeper insights into your data with interactive visual tools. For example:		Gain deeper insights into your data with interactive visual tools. For example:
	• Access detailed visual representations of correlation metrics, including heatmaps and scatter plots. These visuals highlight patterns and relationships in the data, making it easier to interpret findings.		* Access detailed visual representations of correlation metrics, including heatmaps and scatter plots. These visuals highlight patterns and relationships in the data, making it easier to interpret findings.
	• Explore an interactive knowledge graph that visually maps correlations and relationships between selected data columns, enabling intuitive navigation of complex connections.		* Explore an interactive knowledge graph that visually maps correlations and relationships between selected data columns, enabling intuitive navigation of complex connections.

	5. Apply Inference Rules:		5. Apply Inference Rules:
	Enhance the knowledge graph by introducing domain-specific logic:		Enhance the knowledge graph by introducing domain-specific logic:
	• Enter custom rules in an IF-THEN format (e.g., “IF variable A > threshold, THEN infer relationship B”).		* Enter custom rules in an IF-THEN format (e.g., “IF variable A > threshold, THEN infer relationship B”).
	• This step enables users to derive new relationships and enrich the dataset with inferred knowledge, tailoring the analysis to their specific research goals.		* This step enables users to derive new relationships and enrich the dataset with inferred knowledge, tailoring the analysis to their specific research goals.

	6. Execute SPARQL Queries on the RDF Graph:		6. Execute SPARQL Queries on the RDF Graph:
	Use the SPARQL interface to go deeper into the knowledge graph, execute advanced queries, and reveal hidden relationships and insights within the data:		Use the SPARQL interface to go deeper into the knowledge graph, execute advanced queries, and reveal hidden relationships and insights within the data:
	• Dive deeper into the generated knowledge graph using the SPARQL query interface.		* Dive deeper into the generated knowledge graph using the SPARQL query interface.
	• Perform complex queries to explore relationships, extract specific subsets of data, or validate inferred connections.		* Perform complex queries to explore relationships, extract specific subsets of data, or validate inferred connections.
	• This feature integrates the power of semantic querying, enabling users to uncover insights that are not immediately apparent in the raw dataset.		* This feature integrates the power of semantic querying, enabling users to uncover insights that are not immediately apparent in the raw dataset.