Jans Aasman
Want to Join?
Opinions expressed by ICN authors are their own.
Jans Aasman is a Ph.D. psychologist and expert in the Cognitive Science - as well as CEO of Franz, Inc., an early innovator in artificial intelligence and provider of semantic graph databases and analytics. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of artificial intelligence and semantic databases as he works hand-in-hand with organizations such as Montefiore Medical Center, Blue Cross/Blue Shield, Siemens, Merck, Pfizer, Wells Fargo, BAE Systems as well as U.S. and foreign governments.
Dr. Aasman spent a large part of his professional life in telecommunications research, specializing in applied artificial intelligence projects and intelligent user interfaces. He gathered patents in the areas of speech technology, multi-modal user interaction, recommendation engines while developing precursor technology for the iPad and Siri from 1995 to 2004. He was also a part-time professor in the industrial design department of the Technical University of Delft.
The opinions expressed in this blog are those of Jans Aasman and do not necessarily represent those of IDG Communications, Inc., its parent, subsidiary or affiliated companies.
Why dynamically visualizing relationships in data matters
The ability to visualize data, their relationships to one another and connections to business objectives gives organizations the power to uncover insights that would otherwise elude them
Solidifying security analytics with artificial intelligence knowledge graphs
AI knowledge graphs can augment security analytics by linking knowledge together to pinpoint relationships and patterns related to security issues pertinent to an organization.
Navigating time in knowledge graphs
The temporal benefits of cognitive knowledge graphs can affect almost any business problem, including basic issues of data management such as data quality, data cleansing, and integration
The marvels of an event-based schema
When working with various data types at the speed of big data, this method is ideal for integrating and aggregating assorted information for the holistic value it provides
Harmonizing big data with an enterprise knowledge graph
In addition to streamlining how users retrieve diverse data via automation capabilities, a knowledge graph standardizes those data according to relevant business terms and models