Projects per year
Organization profile
The mission of the Center for Big Data is to synergize expertise in various disciplines across the NJIT campus and build a unified platform that embodies a rich set of big data-enabling technologies and services with optimized performance to facilitate research collaboration and scientific discovery. Current research projects at the center focus on the development of high-performance networking and computing technologies to support big data applications. We are building fast, reliable data-transfer systems to help users in a wide spectrum of scientific domains move big data over long distances for collaborative data analytics. We are also developing high-performance workflow processes to manage the execution and optimize the performance of large-scale scientific workflows in various big data computing environments, including Hadoop/MapReduce and Spark. Furthermore, we are developing new machine-learning, data-mining and data-management techniques to address volume, variety, velocity, variability, and veracity challenges to enable big data analytics and predictive modeling in real-life applications. For example, we are developing a platform for analyzing user-contributed social media data to discover adverse drug effects, a leading cause of death. We are also developing data-driven methods to analyze web-page browsing behaviors to better understand user needs as well as the economics that sustain the free Web. These projects have been supported by the Leir Charitable Foundations, the National Science Foundation and Google.
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Profiles
Projects
- 3 Finished
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Mri Collaborative: Development Of Esprit - Emerging Systems' Performance And Energy Evaluation Instruments And Testbench
10/1/18 → 9/30/20
Project: Research project
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Csr: Small: Collaborative Research: An Integrated Approach To Performance Modeling And Optimization Of Big-Data Scientific Workflows
8/28/15 → 9/30/17
Project: Research project
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Career: Analyzing And Exploiting Meta-Information For Keyword Search On Semi-Structured Data
12/25/12 → 2/28/15
Project: Research project
Research output
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DI++: A deep learning system for patient condition identification in clinical notes
Shi, J., Gao, X., Kinsman, W. C., Ha, C., Gao, G. G. & Chen, Y., Jan 2022, In: Artificial Intelligence in Medicine. 123, 102224.Research output: Contribution to journal › Article › peer-review
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On a two-stage progressive clustering algorithm with graph-augmented density peak clustering
Niu, X., Zheng, Y., Liu, W. & Wu, C. Q., Feb 2022, In: Engineering Applications of Artificial Intelligence. 108, 104566.Research output: Contribution to journal › Article › peer-review
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XML2HBase: Storing and querying large collections of XML documents using a NoSQL database system
Bao, L., Yang, J., Wu, C. Q., Qi, H., Zhang, X. & Cai, S., Mar 2022, In: Journal of Parallel and Distributed Computing. 161, p. 83-99 17 p.Research output: Contribution to journal › Article › peer-review