Company Filing History:
Years Active: 2019
Title: Ralph Hollinshead: Innovator in Data Lineage and Change Impact Prediction
Introduction
Ralph Hollinshead is a notable inventor based in Cary, NC (US). He has made significant contributions to the field of data management and computing. His innovative work focuses on methods and systems for data lineage identification and change impact prediction in distributed computing environments.
Latest Patents
Hollinshead holds a patent for "Data lineage identification and change impact prediction in a distributed computing environment." This patent describes methods and systems that enable servers to capture metadata defining data objects associated with data sources. The servers determine direct relationships between these data sources based on the captured metadata and identify indirect relationships as well. They generate a data lineage across the data sources for the data objects and extract unstructured text from database incident tickets, matching it to the metadata. Furthermore, the servers generate a multidimensional vector for the data objects based on the data lineage and unstructured text. A classification model is trained using these vectors to predict a change impact score for each data object. When a request to change a data object is received, the servers determine the change impact score, and if it is below a certain threshold, they execute the change.
Career Highlights
Hollinshead is currently employed at FMR Corp., where he applies his expertise in data management and computing. His work has been instrumental in advancing the understanding of data lineage and its implications for change impact prediction.
Collaborations
Some of his notable coworkers include Gopalakrishnan Subramanian and Srinivas Gururaja Rau. Their collaborative efforts contribute to the innovative projects at FMR Corp.
Conclusion
Ralph Hollinshead's contributions to data lineage and change impact prediction highlight his role as an influential inventor in the field of computing. His patent reflects a significant advancement in understanding data relationships and managing changes effectively.