Fallbrook, CA, United States of America

James Dean Hildreth


Average Co-Inventor Count = 1.8

ph-index = 3

Forward Citations = 40(Granted Patents)


Location History:

  • Fallbrook, CA (US) (2004)
  • Fallbook, CA (US) (2004)

Company Filing History:


Years Active: 2004

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3 patents (USPTO):Explore Patents

Title: James Dean Hildreth: Innovator in Data Mining Algorithms

Introduction

James Dean Hildreth is a notable inventor based in Fallbrook, CA (US). He has made significant contributions to the field of data mining through his innovative algorithms. With a total of 3 patents, Hildreth has developed methods that enhance the efficiency of data analysis in relational database management systems.

Latest Patents

Hildreth's latest patents include a SQL-based analytic algorithm for cluster analysis. This invention provides a method, apparatus, and article of manufacture for performing data mining applications. The algorithm utilizes SQL statements and programmatic iteration to identify groupings in data retrieved from relational databases, effectively creating analytic models within an analytic logical data model. Another significant patent is the SQL-based analytic algorithm for clustering, which also focuses on data mining applications. This algorithm reduces data retrieved in bulk and operates on the reduced data to find clusters, further contributing to the field of data analysis.

Career Highlights

James Dean Hildreth is currently employed at NCR Corporation, where he continues to innovate in the realm of data mining and analytics. His work has been instrumental in advancing the capabilities of relational database management systems.

Collaborations

Hildreth has collaborated with notable colleagues such as Scott Woodroofe Cunningham and Timothy Edward Miller. Their combined expertise has contributed to the development of advanced data mining techniques.

Conclusion

James Dean Hildreth stands out as a key figure in the innovation of data mining algorithms. His contributions have significantly impacted the efficiency of data analysis in relational databases, showcasing his expertise and dedication to the field.

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