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                2022年6月23日學術報○告——Hua Lu

                來源: 計算機◤學院 | 發表時間: 2022-06-20 | 瀏覽次數: 675

                報告題目:Indoor Location-based Services for Safety against COVID-19

                報告人:Hua Lu

                時間:20226月23 1600—17:30

                地點:線上騰訊ω會議:592-839-922

                報告人簡介↑:


                Hua Lu is a Professor of Computer Science, the Department of People and Technology, Roskilde University, Denmark. From 2007 to 2020, he worked in the Department of Computer Science, Aalborg University, Denmark. He received the BSc and MSc degrees from Peking University, China, and the PhD degree in computer science from National University of Singapore. His research interests span databases, data science, spatial data, and geographic information systems. He has published more than 100 peer-reviewed papers, with 50+ in CCF-A outlets (e.g., SIGMOD, PVLDB, ICDE, WWW, and TKDE). He received the Best Vision Paper Award at SSTD 2019. He has served as PC co-chair or vice-chair for ISA 2011, MUE 2011, MDM 2012, and NDBC 2019, demo chair for SSDBM 2014, and PhD forum co-chair for MDM 2016 and 2022. He has also served on the program committee of many conferences including VLDB, ICDE, WWW, KDD, CIKM, SSTD, ACM SIGSPATIAL GIS and so on. He is a senior member of IEEE.

                報告內容:

                The world is entering a post-COVID-19 era where most countries and societies are reopening. As a result, people are gradually recovering pre-pandemic life styles, e.g., spending considerable amounts of time in public indoor venues (e.g., airports, malls and office buildings), despite the still existing threat of COVID-19 virus variants. To help people enjoy indoor safety against COVID-19, indoor location-based services (LBS) can play an important role, enabled by technologies of moving objects databases and smartphones. This talk will introduce two novel types of indoor LBS for safety against COVID-19. The first LBS is motivated by the fact that people in an indoor venue can collectively form crowds, which in turn influence people’s routing choices. For instance, people may prefer to avoid crowded rooms in their walks when COVID-19 is a concern. To this end, we propose two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. The second LBS focuses on indoor social distance monitoring, which alerts people when they are getting too close to each other in terms of safe distance concerning COVID-19. For each type of LBS, this talk will formulate the research problem, present the principles and design of the solution, and report on the experimental studies. Finally, the talk will end with brief discussion of future directions for COVID-19 related research on LBS and spatial databases.


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