Ziyu Liu, Ph.D. Student

I received my B.S in 2016 from Nanjing University of Aeronautics and Astronautics. I received my M.S. degree in Computer Science in 2018 from Illinois Institute of Technology. I started my Ph.D. at the IIT DBGroup in Fall 2018.

Teaching

• 2018 Fall: CS525 - Advanced Database Organization
• 2018 Fall: -

Research Projects

• GProM - A database-independent middleware for computing the provenance of queries, updates, and transactions

Collaborators

• Dieter Gawlick - Oracle
• Oliver Kennedy - SUNY Buffalo
• Vasudha Krishnaswamy - Oracle
• Venkatesh Radhakrishnan
• Zhen Hua Liu - Oracle

Publications

1. 1 R Techreport Self-tuning Database Operations by Assessing the Importance of Data
   Boris Glavic, Pengyuan Li and Ziyu Liu
   Technical Report #IIT/CS-DB-2023-01
   Illinois Institute of Technology.
   • BibTex
   • pdf
   • details

   @techreport{GL23,
     author = {Glavic, Boris and Li, Pengyuan and Liu, Ziyu},
     title = {Self-tuning Database Operations by Assessing the Importance of Data},
     institution = {Illinois Institute of Technology},
     year = {2023},
     number = {IIT/CS-DB-2023-01},
     pdfurl = {http://cs.iit.edu/%7edbgroup/assets/pdfpubls/GL23.pdf},
     projects = {Relevance-based Data Management},
     keywords = {Provenance, Relevance-based Data Management},
     venueshort = {Techreport}
   }
   

   
   
   details
2. 2 R Techreport Oracle PBDS Experiments
   Boris Glavic, Xing Niu, Pengyuan Li and Ziyu Liu
   Technical Report #IIT/Cs-db-2022-01
   Illinois Institute of Technology.
   • BibTex
   • pdf
   • details

   @techreport{GN22,
     author = {Glavic, Boris and Niu, Xing and Li, Pengyuan and Liu, Ziyu},
     title = {Oracle PBDS Experiments},
     institution = {Illinois Institute of Technology},
     year = {2022},
     number = {IIT/Cs-db-2022-01},
     pdfurl = {http://cs.iit.edu/%7edbgroup/assets/pdfpubls/GN22.pdf},
     projects = {Relevance-based Data Management},
     keywords = {Provenance, Relevance-based Data Management},
     venueshort = {Techreport}
   }
   

   
   
   details
3. 3 J PVLDB Provenance-based Data Skipping
   Xing Niu, Ziyu Liu, Pengyuan Li, Boris Glavic, Dieter Gawlick, Vasudha Krishnaswamy, Zhen Hua Liu and Danica Porobic
   Proceedings of the VLDB Endowment. 15, 3 (2021) , 451–464.
   • BibTex
   • doi
   • pdf
   • extended version
   • Abstract
   • details

   @article{NL21,
     author = {Niu, Xing and Liu, Ziyu and Li, Pengyuan and Glavic, Boris and Gawlick, Dieter and Krishnaswamy, Vasudha and Liu, Zhen Hua and Porobic, Danica},
     keywords = {Provenance, Data Skipping, Relevance-based Data Management},
     title = {Provenance-based Data Skipping},
     journal = {Proceedings of the VLDB Endowment},
     projects = {Relevance-based Data Management},
     pages = {451 - 464},
     volume = {15},
     issue = {3},
     year = {2021},
     doi = {10.14778/3494124.3494130},
     venueshort = {{PVLDB}},
     pdfurl = {https://vldb.org/pvldb/vol15/p451-niu.pdf},
     longversionurl = {https://arxiv.org/pdf/2104.12815}
   }
   

   Database systems use static analysis to determine upfront which data is needed for answering a query and use indexes and other physical design techniques to speed-up access to that data. However, for important classes of queries, e.g., HAVING and top-k queries, it is impossible to determine up-front what data is relevant. To overcome this limitation, we develop provenance-based data skipping (PBDS), a novel approach that generates provenance sketches to concisely encode what data is relevant for a query. Once a provenance sketch has been captured it is used to speed up subsequent queries. PBDS can exploit physical design artifacts such as indexes and zone maps.

   
   
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