This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Kinaxis Inc.. Active years: 2026.
Company Filing History:
Years Active: 2026
Title: Matt Diener: Innovator in Dependency-Based Scheduling
Introduction
Matt Diener is a notable inventor based in Ottawa, Canada. He has made significant contributions to the field of computer science, particularly in the area of analytic engines and concurrent online analytics. His innovative work focuses on optimizing the distribution of work between threads, which is crucial for enhancing the efficiency of analytic processes.
Latest Patents
Matt Diener holds a patent for an "Analytic engine for optimally distributing work between threads for dependency-based scheduling for concurrent online analytics." This invention encompasses a system, method, and non-transitory computer-readable storage medium designed to compute a full dependency graph before obtaining a result of an analytic. The patent outlines a process for constructing a scheduling graph that optimally distributes work among available threads based on the full dependency graph. This includes receiving a request for a result of an algorithm executed on a node, checking for a secondary dependency algorithm, and executing the algorithm on the node.
Career Highlights
Matt Diener is currently employed at Kinaxis Inc., a company known for its innovative supply chain management solutions. His work at Kinaxis has allowed him to apply his expertise in developing advanced analytic tools that improve operational efficiency. With a focus on dependency-based scheduling, Matt has positioned himself as a key player in the field of analytics.
Collaborations
Throughout his career, Matt has collaborated with talented individuals such as Dane Henshall and Nathaniel Stanley. These collaborations have fostered a creative environment that encourages innovation and the development of cutting-edge technologies.
Conclusion
In summary, Matt Diener is a distinguished inventor whose work in dependency-based scheduling has made a significant impact on the field of analytics. His contributions continue to influence the way analytic engines operate, paving the way for more efficient data processing solutions.