Probabilistic graphs and uncertain data analysis represent a rapidly evolving research domain that seeks to reconcile the inherent imprecision of real-world data with robust computational models ...

Graphs are essential because it is impossible to navigate through the ocean of unlike data available for modeling and analysis without some tools to illuminate the process.

Graph-based data models have become central to modern machine learning and artificial intelligence applications, and are now widely used by data analysts in applications as diverse as marketing to ...

What do you get when you combine two of the most up-and-coming paradigms in data processing -- streaming and graphs? Likely a potential game-changer, which DARPA and others are pivoting to invest in.

Siren promises the benefits of knowledge graphs, without the hassle of reinventing your existing data management and infrastructure ...

Here are some other articles you may find of interest on the subject of improving your Excel data analysis, graphs and workflows : Combining Excel and Copilot Pro AI to improve your spreadsheets ...

Offering a versatile approach to data collection and analysis, knowledge graphs have a broad range of applications, from helping ensure regulatory compliance to enhancing customer experiences. But ...

The four pillars of graph adoption This confluence of graph analytics, graph databases, graph data science, machine learning, and knowledge graphs is what makes graph a foundational technology.

While still a bit of an outlier, graph-oriented databases continue to find a role in the modern data stack -- thanks largely to AI.

The terms data analysis and data visualization have become synonymous in everyday language in the wider data community, but the two are quite different. Data analysis is an exploratory process ...