Big Data & Analytics

• Fraud, especially its dynamic nature, is a major area of concern requiring significant time and resources to isolate from an enormous volume of transaction data.  
• We have developed an innovative new composite AI based solution that combines graph-rule-based with graph-supervised-learning coupled with explainability to address this problem.  
• The talk is based on a real world Graph AI (GAI) project undertaken by Larus, Inc. and Fujitsu Research. The project evaluated the incorporation of GAI technology from Fujitsu (Deep Tensor) into an existing rules-based credit card fraud detection application developed by LARUS. 

• Lane Arthur of John Deere will discuss how AI helps build resilience on the farm. Specifically, he will discuss the following topics:
  • The challenges that agriculture is facing today and how technology can help.
  • How AI & AI-enabled equipment is taking the burden off farmers.
  • How AI and big data is utilized at all stages of the farming cycle.
  • How other industries can follow the agriculture industry in utilizing AI and data in their day-to-day operations.

On-Demand Presentation

• Use cases summary where OLTP and Analytics need to work on same data
• Cloud Independent Big Data Architecture that support OLTP and Analytics Processing on same data
• AWS based Serverless Big Data Architecture that supports OLTP and Analytics Processing on same data

On-Demand Presentation

• In today’s world anyone can create any data from anywhere on any device. While this is going to grow even further with more and more business strategy shifts and technology sophistication, we are left with no choice other than to deal with lots and lots of data.
• Traditionally we have been forced to use big data platforms, analytics but does that really help? Are the leadership able to trust their own data within their organization? Are the data engineers able to steer and proactively manage their data platforms? Are the data scientist who are supposed to build models and refining algorithms able to focus just on those objectives?
• Or are we just mindlessly  building data lakes and clusters with the hope one day we will make some sense of the data we have. This topic is for those who are brave enough to roll up their sleeves and willing to admit enough is enough, and try new approaches to solve the age old question of how to make sense of our own data?

On-Demand Presentation