摘要：Missing data is ubiquitous. It generally has incompleteness or uncertainty property, and widely exists in many real-life applications such as sensor networks, data integration, society survey, etc. Query processing plays an increasingly important role in these scenarios, especially for obtaining valuable information from complex missing data. However, traditional query techniques on complete data cannot (directly) support querying missing data. I will give a talk with the title of complex missing data query and management. My talk is mainly divided into four parts, including (i) querying incomplete data, (ii) querying uncertain graphs, (iii) optimizing query quality involving data uncertainty, and (iv) an application system with queries on missing data.
Xiaoye Miao is currently a ZJU100 young professor at the Center for Data Science, Zhejiang University (ZJU), Hangzhou, China. She received the Bachelor degree of engineering (in computer science) and Bachelor of economics from Xi’an Jiaotong University (XJTU). She got the Ph.D. degree in computer science from ZJU. Prior to joining ZJU in 2018, she was a research assistant in University of New South Wales (UNSW), Sydney, Australia, and then a postdoctoral fellow in the City University of Hong Kong (CityU), respectively. Her primary research areas are Incomplete/Uncertain Databases, Graph Data Management, Data Cleaning, Data Pricing, etc. She has published over 20 research papers on several premium/leading journals including VLDBJournal, TKDE, and TMC and various prestigious international conferences such as VLDB, ICDE, and DASFAA. She has also co-authored one monograph published in Morgan & Claypool publishers. She was/is serving as a referee/reviewer of several top/important journals/conferences such as VLDB Journal, TKDE, Information Sciences, WWWJournal, DASFAA, APWeb, WISE, etc.
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