Graph, Data, & AI

Revolutionize Your Enterprise With Graph Visualizations

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Visualize Information

  • Location analysis with geospatial and maps
  • Crucial information visualization dashboards & dependencies
  • KPIs, scoring, & segmentation

Analyze Data

  • Pattern identification
  • Algorithm employment
  • Anomaly & outlier detection
  • Relationship mapping

Simulate Different Scenarios

  • “What if” analysis
  • Simultaneous testing
  • “Digital twin” creation & replication

Optimize Your Process

  • Real time data
  • Intervention operations
  • Workflow enhacement
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Our Insights

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Deep learning, specifically recurrent neural networks, forecast nonlinear time series illness signals.

What Machine Learning Can Learn from Graph

Graphs and graph datasets are rich data structures that can be used uniquely to improve the accuracy and effectiveness of machine learning workflows. Some of the key interactions are graph analytics as features, semi supervised learning, graph based deep learning, and machine learning approaches to hard graph problems.

C360 for Retail

Explore various graph algorithms and community detection styles in this retail use case demonstration for Graph Technology and Customer 360.