Why Airlines Need to Look at Holistic Machine Learning & Optimization Solutions to Improve Scheduling
This blog discusses the decision process for adjusting flight schedules in response to weather events using advanced analytics.
Graph analytics is a field of data analysis that focuses on studying and extracting insights from graph-structured data. Mosaic Data Sciences has strong knowledge of graph analytics techniques and ample experience helping customers understand the patterns, relationships, and properties within these graphs to solve pressing data problems.
This blog discusses the decision process for adjusting flight schedules in response to weather events using advanced analytics.
The nature of problems involving graphs calls for applying techniques tailor-made to work with them. In this post, we will focus on one class of graphs called Knowledge Graphs, describe some of the problems practitioners are trying to solve using them and explain how machine learning is used in this context.
Graph analytics is a classic network science technique that is making waves due to the advancements in Graph Database Technology (GDB) and the integration of machine learning techniques (i.e., neural networks), to solve a wide range of use cases. No need for a map to figure this one out – Principal Data Scientist, Daniel Salazar is giving us the full scoop as our guide.
Given a set of nodes & connections, which can abstract anything from transportation networks, connections between customers, knowledge graphs, or molecular structures to computer data, graph analytics provide a helpful tool to quantify & simplify the many moving parts of dynamic systems.