Associate Teaching Professor Jia Zhang receives $1M NSF award

 

April 14, 2015

ECE’s Jia Zhang, an associate teaching professor at Carnegie Mellon’s Silicon Valley campus, has received a National Science Foundation (NSF) award to support her project, entitled “CIF21 DIBBs: An Infrastructure Supporting Collaborative Data Analytics Workflow Design and Management.”

Zhang’s recent research interests center on service oriented computing, with a focus on scientific workflows, net-centric collaboration, Internet of Things, cloud computing, and big data management. She has co-authored one textbook titled "Services Computing" and has published over 120 refereed journal papers, book chapters, and conference papers. She is now an Associate Editor of IEEE Transactions on Services Computing (TSC) and of International Journal of Web Services Research (JWSR), and Editor-in-Chief of International Journal of Services Computing (IJSC). Zhang also has nine years of software architect experience in industry. Zhang earned her Ph.D. in computer science from the University of Illinois at Chicago, and her M.S. and B.S. in computer science from Nanjing University, China.

Project overview

The need for collaborative data analysis increases significantly when confronted with the challenges of big data. Although workflow tools offer a formal way to define, automate, and repeat multi-step computational procedures, designing complex data process workflow requires collaboration from multiple people with complementary expertise. Existing tools are not suitable to support collaborative design of comprehensive workflows. To address such a challenge, this project aims to design and develop a software infrastructure with the capability of supporting collaborative data-oriented workflow composition and management, adding a key component to existing NSF cyberinfrastructure that will support big data collaboration through the Internet. Reproducibility and scalability are two major targets. The project extends an existing open-source workflow tool, by adding system-level facilities to support human interaction and cooperation that are essential for an effective and efficient scientific collaboration.


This project will produce five outcomes:

  1. A collaborative provenance data model equipped with a graph-level provenance querying formalism
  2. A type-theoretic approach for addressing format transformations
  3. Hypergraph theory-based algorithms for provenance management and mining
  4. A software tool supporting (a)synchronous collaborative scientific workflow design, composition, reproduction, and visualization
  5. Principles, methodologies, experiences, and lessons that support the development of a generically applicable collaborative scientific workflow composition tool

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Jia Zhang