Research

Layer 1: Big Data Repository

Goals: Share data and analysis results for community building

Tasks: Standardize, categorize, and benchmark datasets

Layer 2: Big Data Technological Infrastructure

Goals: Provide generic and special big-data enabling solutions

Tasks: Investigate, design, develop, implement, and test big data-oriented analytics, visualization, computing, networking, workflow, storage, and retrieval solutions

Layer 3: Big Data Applications

Goals: Advances sciences in various domains

Tasks: Adapt, customize, and refine application-specific solutions

Recent Publications:

H. Alquwaiee and C.Q. Wu. "On Performance Modeling and Prediction for Spark-HBase Applications in Big Data Systems”. In Proc. of IEEE International Conference on Communications, Seoul, South Korea, May 16-20, 2022 (ICC22).

C.Q. Wu (guest editor). Special Issue on "Workflows in Support of Large-Scale Science", Concurrency and Computation: Practice and Experience (CCPE), Wiley, March 2022.

Dongliang Chu, Chase Q. Wu, "Generalizing the Over Operator for Parallelization and Order-independency," Journal of Parallel and Distributed Computing, vol. 151, pp. 52-60, May 2021 (JPDC21).

Daqing Yun, Wuji Liu, Chase Q. Wu, Nageswara S.V. Rao, and Rajkumar Kettimuthu, "Exploratory Analysis and Performance Prediction of Big Data Transfer in High-performance Networks," Engineering Applications of Artificial Intelligence, vol. 102, 104285, June 2021 (EAAI21).

Qianwen Ye, Wuji Liu, Chase Q. Wu, "NoStop: A Novel Configuration Optimization Scheme for Spark Streaming," In Proceedings of the 50th International Conference on Parallel Processing, Argonne National Laboratory in Chicago, IL, USA, August 9-12, 2021 (ICPP21).