A new visualization system for entity resolution and clustering that allows us to investigate different match and clustering techniques for multi-source entity resolution. SIMG-VIZ offers the following key features:
- SIMG-VIZ allows a user to analyze precomputed similarity graphs and clusterings from existing ER tools and also supports executing and analyzing ER match tasks directly with FAMER.
- Different graph and ER cluster visualization techniques and layouts can be applied to choose the best visualiza- tions.
- To increase performance, some layouts can be precom- puted on the server with either parallel or serial computa- tion. This provides a significant optimization potential in particular for force-directed layouts.
- To support visualization of large graphs, preprocessing techniques such as sampling (also executed in parallel on the server) can be selected to obtain a fast overview of large similarity graphs and their clustering results.
- Clusters and their overlaps as well as edges annotated with their type and similarity are visualized by using a simple but useful cake-like visual metaphor. Users can interact with clusters and select individual clusters for investigation.
Based on Flink, this software extracts and visualizes a cluster or a cluster together with is neighboring clusters from a large graph. The input graph for this software is similar to input graph of Gradoop. An assumption is that each vertex has the property "ClusterId" which shows to which cluster that vertex belongs. This property can contain several cluster ids separated by a comma. To use the software, you need first to copy your data into the directory "src/main/resources/data" under an existing folder (called category) or a new folder. After updating the maven, the software can be run just by running the Server class.
The corresponding Paper in EDBT 2018: https://dbs.uni-leipzig.de/file/edbt-demopaper.pdf
We are working to extend the software for the visualization of incremental clustering for subgraphs as well the whole graph which can be very large.