Supercomputing and Computation


Provenance Capture Mining

Provenance system, which was originally designed to collect various metadata information related with events, process, or data to provide data lineage or audit trails, is recently receiving increased attentions in a collaborative multi-user environment, since one can take advantage of collected and accumulated provenance information to extract knowledge of peers by using various data mining and machine learning techniques. Due to the advances in statistical learning techniques in recent years, various machine learning and data mining techniques have been applied in many domains and proven their successes in discovering previously hidden knowledge from massively collected data.

Our research goal is to develop a systematic way to store and index provenance information of data access in scientific applications, and to utilize such collected information for mining to improve data access performance, provide machine-guided parameter selection, etc.

Related Projects

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— The Adaptable IO System (ADIOS) was developed to provides a simple and flexible way to manage IO related tasks in large-scale and data-intensive scientific applications and it has been playing a central role in many real-world scientific applications, such as Gyrokinetic Toroidal Code (GTC), plasma fusion simulation code (XGC), combustion simulation code (S3D), etc.


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