Clustering-Based Predictive Analytics to Improve Scientific Data Discovery
Given the sheer volume of scientific data archived within the data-intensive projects at the US Department of Energy's Oak Ridge National Laboratory, finding precisely what data we are looking for may not be a trivial task; conversely, we may also miss a more prominent data product. To address such issues, we propose improving the data discovery system and using data analytics methods to comprehend what specific users might be interested in based on their physiological state, search patterns, and past data usage history. This work's primary goal is to prune the complexity, increase the visibility of popular data products, and direct users toward the data that best meet their needs. The proposed algorithm constructs a user profile based on the user's explicit or implicit interactions with the system, such as items they are currently looking at on-site and the key metadata mappings related to the data set. The pattern is then used to build a training data set, which will help find relevant data to recommend to the user.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1777750
- Resource Relation:
- Conference: IEEE International Conference on Big Data - Atlanta, Georgia, United States of America - 12/10/2020 3:00:00 PM-12/13/2020 3:00:00 PM
- Country of Publication:
- United States
- Language:
- English
Similar Records
THE NEW ONLINE METADATA EDITOR FOR GENERATING STRUCTURED METADATA
Data Locality Enhancement of Dynamic Simulations for Exascale Computing (Final Report)