Informing machine learning with time series analysis

Dr. Jans Aasman was interview for this AI Business article:

Organizations can optimize their ability to perform time series analysis with a number of data management staples including data modeling, data visualizations, and knowledge graph technologies. This combination enables businesses to analyze their data so they can “go forwards and backwards in time,” according to Jans Aasman, CEO at Franz. “This is super powerful, as you can imagine. You can look at relationships in your data as they evolve over time.”

When keeping track of various entities for fraud detection in financial services, for example, events—and the relationships between them—might include anything related to companies, people, and their actions. “You owned a company from a start time to an end time, or you worked for a company from a particular start time to an end time, or you were at a particular address, or had a telephone number, or had an email address with a particular start time and end time,” Aasman said. “We have relationships and every relationship has a beginning, and most of the time, an end.”

The advantage of visualizations offered by progressive graph solutions is the capability to “go back and see how the graph got built up over time,” Aasman said. Without such visualizations, users would have to look at the individual dates attributed to nodes, a laborious and complicated process.

Read the full article at AI Business