Interactive visual components that accelerate and simplify data analysis to use their maximum value for the business
Modern business organizations operate within processes that generate a large amount of valuable data stored in data catalogs. Data mining, then, is about analyzing, which deals with acquiring insights into the semantics of the actual business process accurately.
The presented visual components support getting maximum value in the shortest possible delivery time by targeting data and process content.
|Client||Data Strategy and Big Data Enablement Company|
|Business need||database integrations via flow diagrams|
|Scope||flow diagrams, database diagrams, interactive dashboard|
The database flow enables the helicopter view on the IT infrastructure. It consists of the central diagram that depicts the databases’ correlations. The graph presents the information flow and dependencies between the databases. Each data flow can be followed through the filters and take actions.
The user can dive through specific information flow anytime. The expandable nodes showcase the details within them. By clicking the node, the user discovers dependencies between databases, filters, and actions. On the other hand, the highlighted arrows enable investigating the input and output correlations along with their origins.
The timeline equips each record to present changes during a specific time. The integrated table component enables the user to look over the particular data sheet within the app.
The two vertical sidebars enrich the graphic user interface. It enables submenu usage with more specific functions. The diagram itself is equipped with intelligent adornments to showcase the alerts within it.
Visual components make reading data easier and almost effortless. Thanks to the interactive dashboard, you can control and maintain your data and analyze how they change during a specific time.
An intuitive graphic user interface helps you navigate through nodes, arrows, and other components and easily find the needed databases with their contents.
Effortless dashboard and diagram maintenance thanks to intuitive and extended graphic user interface
Presenting the information flow and dependencies between the databases
Analyzing changes during specific times thanks to an easy-to-read timeline
Data visualization in data mining refers to the use of visual representations, such as charts, graphs, and maps, to communicate patterns, trends, and insights discovered from large datasets. It helps to present complex information in a more understandable and intuitive manner.
To visualize data mining results, you can employ various techniques such as scatter plots, bar charts, line graphs, heatmaps, treemaps, and network diagrams. These techniques help to showcase relationships, distributions, and patterns within the data.
In Python, data mining involves extracting valuable insights and patterns from large datasets using techniques such as clustering, classification, regression, and association rule mining. Data visualization in Python refers to the use of libraries like Matplotlib, Seaborn, and Plotly to create visual representations of the data mining results.
Data mining visualization techniques include scatter plots, histograms, box plots, parallel coordinates, decision trees, network graphs, and interactive visualizations. These techniques help users understand patterns, correlations, outliers, and other relevant information in the dataset.
Examples of data mining visualization include visualizing customer purchase patterns to identify market segments, creating a network graph to analyze social media connections, generating a geographic heat map to understand regional sales performance, and plotting a time series chart to detect trends in stock market data. These examples demonstrate how data mining and visualization can provide valuable insights in various domains.