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Years Active: 2010-2012
Title: The Innovations of Christopher Re
Introduction
Christopher Re is an accomplished inventor based in Seattle, WA. He holds two patents that showcase his expertise in data management and optimization. His work has significantly contributed to the fields of data deduplication and probabilistic databases.
Latest Patents
One of Christopher Re's latest patents is titled "Declarative framework for deduplication." This patent describes a system, framework, and algorithms for data deduplication. It introduces a declarative language, such as a Datalog-type logic language, which allows programs to describe data to be deduplicated along with the constraints that must be satisfied. The patent also details algorithms for performing graph clustering to execute these programs effectively.
Another notable patent is "Efficient top-K query evaluation on probabilistic data." This invention presents a novel approach to compute and efficiently rank the top-k answers to a query on a probabilistic database. The algorithm runs several Monte Carlo simulations in parallel for each candidate answer, approximating the probability of each answer as needed to determine the top-k results. This algorithm is optimal and scalable, making it applicable to large databases.
Career Highlights
Christopher Re has worked with prominent organizations, including Microsoft Technology Licensing, LLC and the University of Washington. His experience in these institutions has allowed him to refine his skills and contribute to significant advancements in technology.
Collaborations
Throughout his career, Christopher has collaborated with notable professionals such as Dan Suciu and Arvind Arasu. These collaborations have further enriched his work and expanded his impact in the field of data management.
Conclusion
Christopher Re is a distinguished inventor whose contributions to data deduplication and probabilistic databases have made a lasting impact. His innovative patents reflect his commitment to advancing technology and improving data management practices.