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KEVKUS, Kevyn Aucoin, KEXIN Store, KEXWAXX, KEY, Key Enterprises Inc., Rondo, Roner, Rong, RONG HOME, RONG TABLE, RONGJI, rongji jewelry, 

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P. Bailis, E. Gan, K. Rong, and S. Suri. Prioritizing Attention in Fast Data: Challenges and Oppor-tunities. CIDR, 2017. Demonstrations and Workshops

of ?? records. dismiss all constraints. view refined list in 2021-01-19 Approximate Partition Selection for Big-Data Workloads using Summary Statistics Kexin Rongy, Yao Luy, Peter Bailis, Srikanth Kandula y, Philip Levis Microsofty, Stanford fkrong,pbailis,pal g@cs.stanford.edu, yflu.yao,srikanth @microsoft.com ABSTRACT Many big-data clusters store data in large partitions that De senaste tweetarna från @kexinrong Kexin Rong, Clara E. Yoon, Karianne J. Bergen, Hashem Elezabi,Peter Bailis, Philip Levis, Gregory C. Beroza. VLDB 2018 LSH VLDB Case Study.

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Edward Gan. Kexin Rong. Sahaana Suri. CIDR 2017. Page 2. Sahaana. Kexin. Edward. Page 3. Deepak. Firas. Tony. John. Matei. Sahaana. Kexin. Edward 

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Sanjiv Kumar Kexin Zhang. GU. Doktorand / PhD student.

Kexin rong

student in Computer Science at Stanford University, co-advised by Professor Peter Bailis and Professor Philip Levis. She designs and builds systems to enable data analytics at scale, supporting applications including scientific analysis, infrastructure monitoring, and analytical queries on big data clusters. Kexin Rong (Stanford University) Peter Bailis (Stanford University) Moses Charikar (Stanford University) Philip Levis (Stanford University) Related Events (a corresponding poster, oral, or spotlight) 2019 Poster: Rehashing Kernel Evaluation in High Dimensions DOI: 10.1145/3035918.3035928 Corpus ID: 6441281. MacroBase: Prioritizing Attention in Fast Data @article{Bailis2017MacroBasePA, title={MacroBase: Prioritizing Attention in Fast Data}, author={Peter Bailis and Edward Gan and S. Madden and D. Narayanan and Kexin Rong and Sahaana Suri}, journal={Proceedings of the 2017 ACM International Conference on Management of Data}, year={2017} } Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, and Philip Levis.
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June 2019DEEM'19:   Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Levis (Stanford and Microsoft) VLDB 2020, Saehan Jo and Junxiong Wang. November 6, CrossFS: A   Kexin Rong, Peter Bailis. An automatic smoothing algorithm for time series visualization that removes short-term fluctuations while preserving large-scale  Thomas W. Giambelluca · Barbara Guieb · Catherine Kexin Rong · Jonalyn Mokiao · Publications Office. Catherine/Kexin Rong Research Support, WRRC.

PVLDB  16 Oct 2020 Sun, Kexin and Zheng, Rong-Shou and Zhang, Siwei and Zeng, Hongmei and Zou, Xiaonong and Wang, Shaoming and Chen, Ru and Wei,  MacroBase: Prioritizing Attention in Fast Data. F Abuzaid, P Bailis, J Ding, E Gan, S Madden, D Narayanan, K Rong, ACM Transactions on Database Systems  Kexin Rong.
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Resource management for network-assisted D2D communication. Du, Rong. Wireless Liu, Kexin. Theoretical Investigation on Propagation and. Coupling of 

A Machine-Compiled Database of Genome-Wide Association Studies. Volodymyr Kuleshov, Jialin Ding, Braden Hancock, … Kexin Rong is a Ph.D. student in Computer Science at Stanford University, co-advised by Professor Peter Bailis and Professor Philip Levis.


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KEVKUS, Kevyn Aucoin, KEXIN Store, KEXWAXX, KEY, Key Enterprises Inc., Rondo, Roner, Rong, RONG HOME, RONG TABLE, RONGJI, rongji jewelry, 

zoomed in on ?? of ?? records. dismiss all constraints. view refined list in View Kexin Rong's business profile as Research Support at University of Hawaii. Find contact's direct phone number, email address, work history, and more. 2019-06-07 · CURIS Coordinators (Griffin Dietz and Kexin Rong) Office: Gates B02 .

Department of Computer Science, Stanford University, 353 Serra Mall, Gates Computer Science Building, Stanford, California 94305 U.S.A. Search for other works by this author on: GSW. Google Scholar. Hashem Elezabi; Hashem Elezabi c. Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri Stanford InfoLab ABSTRACT While data volumes continue to rise, the capacity of human attention remains limited. As a result, users need analytics engines that can assist in prioritizing attention in this fast data that is too large for manual inspection.