This inventor holds 1 USPTO granted patent. Top assignee: Oracle International Corporation. Active years: 2013.
Company Filing History:
Years Active: 2013
Title: Martin Hoyes - Innovator in Data Reduction Technologies
Introduction
Martin Hoyes is a notable inventor based in San Francisco, CA. He has made significant contributions to the field of data optimization and testing. His innovative approach has led to the development of a patent that addresses the challenges of managing large production databases.
Latest Patents
Martin Hoyes holds a patent titled "Data reduction for optimizing and testing." This invention allows for the efficient replication and maintenance of a reasonably-sized testing database instance for a very large production database. The patent ensures that the performance characteristics are preserved, enabling proper testing of the production database for various application programs. By obtaining statistics on the type of data distribution for customer data, parameters can be determined to store data near the endpoints of the distribution. This method retains a substantial amount of data skew in a much smaller instance of the production database, facilitating easier performance and upgrade testing.
Career Highlights
Martin Hoyes is currently employed at Oracle International Corporation, where he applies his expertise in data management and optimization. His work has been instrumental in enhancing the efficiency of database systems. With a focus on innovative solutions, he continues to contribute to advancements in the field.
Collaborations
Some of Martin's coworkers include Nagaraj M Hunur and Peter Murphy. Their collaborative efforts contribute to the innovative environment at Oracle International Corporation.
Conclusion
Martin Hoyes is a distinguished inventor whose work in data reduction technologies has made a significant impact on database management. His patent reflects a deep understanding of the complexities involved in handling large datasets, and his contributions continue to shape the future of data optimization.
