Xplain Data @ML Week 2024 – Breaking Barriers in Smart Manufacturing
At Machine Learning Week 2024 in Munich, Xplain Data’s CEO, Dr. Michel Haft, joined Siemens’ Erik Schwulera to present an…
At Machine Learning Week 2024 in Munich, Xplain Data’s CEO, Dr. Michel Haft, joined Siemens’ Erik Schwulera to present an…
IDC Innovator 2024: Xplain Data has been named an IDC Innovator in: “Industrial AI Applications and Solutions, 2024”. IDC Innovators…
Xplain Data Honored in Seventh Annual German AI Startup Landscape (2024) Xplain Data is proud to announce its inclusion for…
The Gartner AI Hype Cycle ´24 is out, and Causal AI continues its steady ascent! Causal AI is no longer…
Art, agencies, and AI: A vibrant mix at the House of Communication in Munich’s trendy Werksviertel – and a perfect…
How can we support health innovation through data sharing and AI? The future of healthcare in Germany was passionately discussed…
Causality cannot be proven from observational data. However, with comprehensive data, you can get close to proof. The more comprehensive your data, the lower the risk of misinterpreting correlation as causation. Comprehensive data involves a multi-layered data model.
Our ObjectAnalytics Database is designed to store complex data, such as millions of patients with billions of events (diagnoses, prescriptions, genomic data, etc.). Our Causal Discovery algorithms utilize this object-oriented data storage to efficiently search for direct and indirect explanations for a target event, uncovering potential cause-and-effect relationships.
Why not develop next generation intelligent algorithms by operating on entire objects instead of tables, rows, and columns?
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Easily understand causation beyond correlation, based on a holistic view of your business objects.
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