Getting Started

BDI-Viz 1.0 is built leveraging Panel. The application is designed to provide a user-friendly interface on jupyter notebooks. Where users can explore the schema matching recommandations, interact with the result, and pass them to the next step of the data integration process.

First, import the BDISchemaMatchingHeatMap class from bdiviz:

from bdiviz import BDISchemaMatchingHeatMap

Prepare the source and target dataframes

The only step before using the BDISchemaMatchingHeatMap class is to prepare the source and target dataframes. The source and target should be in the form of pandas dataframes. For GDC data, we can simply pass "gdc" as target schema. In this example, we will use the GDC schema as the target schema and a sample schema as the source schema.

import pandas as pd

source = pd.read_csv("../../examples/datasets/dou.csv")
target = "gdc"

Generate Visualization

To generate the visualization, we need to pass the source and target dataframes to the BDISchemaMatchingHeatMap class. Then use the plot_heatmap method to generate the schema matching recommendations heatmap.

BDI-Viz will automatically generate the schema matching recommendations and display them in a heatmap. The heatmap will display the schema matching recommendations with the source schema on the y-axis and the target schema on the x-axis. The heatmap will also display the similarity score between the source and target schema.

bdiviz_manager = BDISchemaMatchingHeatMap(source, target)
bdiviz_manager.plot_heatmap()