Chenjie Li, Ph.D. Student

I recieved my Bachelor Degree in Electrical Engineering from GuiZhou University, China. Now I am studying a master’s degree of Data Science at Illinois Institute of Technology

Research Projects

• Cape - Explaining Outliers in Query Results Beyond Provenance

Collaborators

• Sudeepa Roy - Duke University
• Zhengjie Miao - Duke University

Publications

1. 1 C SIGMOD Putting Things into Context: Rich Explanations for Query Answers using Join Graphs
   Chenjie Li, Zhengjie Miao, Qitian Zeng, Boris Glavic and Sudeepa Roy
   Proceedings of the 45th International Conference on Management of Data (2021).
   • To Appear
   • BibTex
   • extended version
   • details

   @inproceedings{LM21,
     author = {Li, Chenjie and Miao, Zhengjie and Zeng, Qitian and Glavic, Boris and Roy, Sudeepa},
     booktitle = {Proceedings of the 45th International Conference on Management of Data},
     pages = {},
     projects = {Cape},
     title = {Putting Things into Context: Rich Explanations for Query Answers using Join Graphs},
     keywords = {Provenance; Explanations},
     venueshort = {SIGMOD},
     longversionurl = {https://arxiv.org/pdf/2103.15797},
     istoappear = true,
     year = {2021}
   }
   

   
   
   details
2. 2 J PVLDB CAPE: Explaining Outliers by Counterbalancing
   Zhengjie Miao, Qitian Zeng, Chenjie Li, Boris Glavic, Oliver Kennedy and Sudeepa Roy
   Proceedings of the VLDB Endowment (Demonstration Track). 12, 12 (2019) , 1806–1809.
   • BibTex
   • doi
   • pdf
   • Abstract
   • details

   @article{MZ19a,
     author = {Miao, Zhengjie and Zeng, Qitian and Li, Chenjie and Glavic, Boris and Kennedy, Oliver and Roy, Sudeepa},
     date-modified = {2019-08-02 09:14:13 -0500},
     journal = {Proceedings of the VLDB Endowment (Demonstration Track)},
     keywords = {Outliers; Intervention; Cape; Explanations},
     pdfurl = {http://www.vldb.org/pvldb/vol12/p1806-miao.pdf},
     projects = {Cape},
     pages = {1806-1809},
     volume = {12},
     issue = {12},
     doi = {10.14778/3352063.3352071},
     title = {CAPE: Explaining Outliers by Counterbalancing},
     venueshort = {{PVLDB}},
     year = {2019}
   }
   

   In this demonstration we showcase Cape, a system that ex- plains surprising aggregation outcomes. In contrast to previous work which relies exclusively on provenance, Cape applies a novel approach for explaining outliers in aggregation queries through counterbalancing (outliers in the opposite direction). The foundation of our approach are aggregate regression patterns (ARPs) based on which we defined outliers, and an efficient explanation generation algorithm that utilizes these patterns. In the demonstration, the audience can run aggregation queries over real world datasets, and browse the patterns and explanations returned by Cape for outliers in the result of such queries.

   
   
   details