Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Nesime Tatbul"'
Autor:
Parimarjan Negi, Ziniu Wu, Andreas Kipf, Nesime Tatbul, Ryan Marcus, Sam Madden, Tim Kraska, Mohammad Alizadeh
Publikováno v:
Proceedings of the VLDB Endowment. 16:1520-1533
Query driven cardinality estimation models learn from a historical log of queries. They are lightweight, having low storage requirements, fast inference and training, and are easily adaptable for any kind of query. Unfortunately, such models can suff
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2231469560db28b57a92af7cb2bda19e
https://doi.org/10.14778/3583140.3583164
https://doi.org/10.14778/3583140.3583164
Publikováno v:
Proceedings of the VLDB Endowment. 15:3754-3757
Machine programming is an emerging research area that improves the software development life cycle from design through deployment. We present a tutorial on machine programming research highlighting aspects relevant to the data systems community. We d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::17153c37ac39304c3af61690dc403136
https://doi.org/10.14778/3554821.3554892
https://doi.org/10.14778/3554821.3554892
Autor:
Manos Athanassoulis, Peter Triantafillou, Raja Appuswamy, Rajesh Bordawekar, Badrish Chandramouli, Xuntao Cheng, Ioana Manolescu, Yannis Papakonstantinou, Nesime Tatbul
Publikováno v:
ACM SIGMOD Record. 51:74-77
In the last few years, SIGMOD and VLDB have intensified efforts to encourage, facilitate, and establish reproducibility as a key process for accepted research papers, awarding them with the Reproducibility badge. In addition, complementary efforts ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fa0d042f0bcadfc2506b4b42cb380274
https://doi.org/10.1145/3552490.3552511
https://doi.org/10.1145/3552490.3552511
Publikováno v:
ACM SIGMOD Record. 51:56-58
As part of the International Conference on Very Large Data Bases (VLDB) 2021 / Proceedings of the VLDB Endowment Volume 14, a new Research Track category named Scalable Data Science (SDS) was launched [2, 6]. The goal of SDS is to attract cutting-edg
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::023f5cbfc04302d8d0819a714aa3fea9
https://doi.org/10.1145/3572751.3572769
https://doi.org/10.1145/3572751.3572769
Publikováno v:
SIGMOD Conference
Recent efforts applying machine learning techniques to query optimization have shown few practical gains due to substantive training overhead, inability to adapt to changes, and poor tail performance. Motivated by these difficulties, we introduce Bao
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::264c6a20e8378e8d8e9be1fea413c014
https://doi.org/10.1145/3542700.3542703
https://doi.org/10.1145/3542700.3542703
Publikováno v:
Proceedings of the VLDB Endowment (PVLDB)
Proceedings of the VLDB Endowment (PVLDB), 2021
Proceedings of the VLDB Endowment (PVLDB), VLDB Endowment, 2021
Proceedings of the VLDB Endowment (PVLDB), 2021
Proceedings of the VLDB Endowment (PVLDB), VLDB Endowment, 2021
International audience; Access to high-quality data repositories and benchmarks have been instrumental in advancing the state of the art in many experimental research domains. While advanced analytics tasks over time series data have been gaining lot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21c42e28d1dae11d5928dc063554a8d7
https://doi.org/10.14778/3476249.3476307
https://doi.org/10.14778/3476249.3476307
Publikováno v:
ACM SIGMOD Record. 49:18-23
Observability has been gaining importance as a key capability in today's large-scale software systems and services. Motivated by current experience in industry exemplified by Slack and as a call to arms for database research, this paper outlines the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5ff102d4d1aae6eb94648a521d251ea
https://doi.org/10.1145/3456859.3456863
https://doi.org/10.1145/3456859.3456863
Autor:
Nesime Tatbul, Timothy G. Mattson, El Kindi Rezig, Michael Stonebraker, Samuel Madden, Ashrita Brahmaroutu, Mourad Ouzzani, Nan Tang
Publikováno v:
VLDB Endowment
Data pipelines are the new code. Consequently, data scientists need new tools to support the often time-consuming process of debugging their pipelines. We introduce Dagger , an end-to-end system to debug and mitigate data-centric errors in data pipel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b527bde4f3690a00293a78e23a6bfc78
https://doi.org/10.14778/3415478.3415527
https://doi.org/10.14778/3415478.3415527
Publikováno v:
Information Systems. 109:102088
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::618491c5a37ebf108be258166c4e5c9a
https://doi.org/10.1016/j.is.2022.102088
https://doi.org/10.1016/j.is.2022.102088
Autor:
Vasimuddin, Darryl Ho, Nesime Tatbul, Jialin Ding, Saurabh Kalikar, Tim Kraska, Sanchit Misra, Heng Li
BackgroundNext-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics. Genomics data analytics at this scale requires overcoming
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b1a198146375f596c7a03cff7996b5f7
https://doi.org/10.1101/2020.12.22.423964
https://doi.org/10.1101/2020.12.22.423964
