讲 座 人：PhD. Ziyun Wei
Traditionally, formal languages such as SQL have been used by users for data analysis. However, these interfaces are not easily accessible to lay users without an IT background. This has led to the emergence of novel interfaces such as visual and natural language query interfaces. While these interfaces democratize data access, they can introduce ambiguities in understanding the user's query intent on the frontend. Furthermore, efficiently executing these queries on the backend is not a straightforward task. Traditional systems typically prioritize the most likely query and optimize the query execution by selecting an optimal plan. In contrast, my research focuses on developing a more robust data analysis platform that addresses these issues by introducing a wider range of query and plan candidates.
In this talk, I will primarily introduce two complementary systems that facilitate robust data analysis and processing. The first system, MUVE, enables natural language queries through typed or voice input. It provides users with alternative query interpretations and optimizes visual output to minimize the time required to identify the correct results. The second system, SkinnerMT, parallelizes intra-query learning to improve efficiency and robustness. It utilizes different parallel methods, allocating threads for plan searching or execution on data partitions. Furthermore, I will briefly touch upon my prior work on Khameleon and ROME and outline future directions for the development of the next generation of robust data analysis and processing platforms.
Ziyun Wei graduated from Fudan University in 2018, advised by Prof. Weihua Zhang, focusing on architecture and database systems. Currently, Ziyun is a sixth-year PhD student in the Computer Science Department at Cornell University, under the guidance of Prof. Immanuel Trummer. His research is dedicated to developing data analysis platforms that provides robustness and efficiency. His work primarily revolves around applying optimization models to enhance the accessibility of data analysis and parallelizing adaptive query processing in database systems.
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