Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Emanuel Zgraggen"'
Autor:
Charlie Meyer, Wesley Runnels, Philipp Eichmann, Emanuel Zgraggen, Benedetto Buratti, Zeyuan Shang, Navid Karimeddiny, Tim Kraska
Publikováno v:
Proceedings of the VLDB Endowment. 14:2893-2905
Recently, a new horizon in data analytics, prescriptive analytics, is becoming more and more important to make data-driven decisions. As opposed to the progress of democratizing data acquisition and access, making data-driven decisions remains a sign
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7a66c943b765c03a8ca51f45839cc68f
https://doi.org/10.14778/3476311.3476370
https://doi.org/10.14778/3476311.3476370
Publikováno v:
ICDM
We present PROSECCO, an algorithm for the progressive mining of frequent sequences from large transactional datasets: it processes the dataset in blocks and outputs, after having analyzed each block, a high-quality approximation of the collection of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f72368a3078d25c0595eb6981bb172f2
https://doi.org/10.1007/s10115-019-01393-8
https://doi.org/10.1007/s10115-019-01393-8
Publikováno v:
SIGMOD Conference
Existing benchmarks for analytical database systems such as TPC-DS and TPC-H are designed for static reporting scenarios. The main metric of these benchmarks is the performance of running individual SQL queries over a synthetic database. In this pape
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21f10ae5b9a2a067ee25f97b2224c5cf
https://doi.org/10.1145/3318464.3380574
https://doi.org/10.1145/3318464.3380574
Publikováno v:
arXiv
Automatic machine learning (AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible. While many recent works have focused on aspects of the mac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a2c2995c0388669716af527b8d20aa9
http://arxiv.org/abs/2003.09758
http://arxiv.org/abs/2003.09758
Publikováno v:
Proceedings of the VLDB Endowment. 10:1142-1153
Visual data exploration tools allow users to quickly gather insights from new datasets. As dataset sizes continue to increase, though, new techniques will be necessary to maintain the interactivity guarantees that these tools require. Approximate que
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::639593e2f59d336fce6f42af6c8277e3
https://doi.org/10.14778/3115404.3115418
https://doi.org/10.14778/3115404.3115418
Autor:
Philipp Eichmann, Ferdinand Kossmann, Eli Upfal, Benedetto Buratti, Tim Kraska, Carsten Binnig, Yeounoh Chung, Emanuel Zgraggen, Zeyuan Shang
Publikováno v:
SIGMOD Conference
Other repository
Other repository
© 2019 Association for Computing Machinery. Statistical knowledge and domain expertise are key to extract actionable insights out of data, yet such skills rarely coexist together. In Machine Learning, high-quality results are only attainable via min
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55d1e4e59dd6b372e829e4ccb9bd75db
https://doi.org/10.1145/3299869.3319863
https://doi.org/10.1145/3299869.3319863
Autor:
Michiel A. Bakker, Emanuel Zgraggen, Madelon Hulsebos, Kevin Hu, Tim Kraska, Çağatay Demiralp, César A. Hidalgo, Arvind Satyanarayan
Publikováno v:
SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining
SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, Association for Computing Machinery (ACM), 2019, ⟨10.1145/3292500.3330993⟩
arXiv
KDD
SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, Association for Computing Machinery (ACM), 2019, ⟨10.1145/3292500.3330993⟩
arXiv
KDD
Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery. Existing data preparation and analysis systems rely on dictionary lookups and regular expres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b12b20f3af42055a027e2affadf88c44
https://hal.archives-ouvertes.fr/hal-03159582
https://hal.archives-ouvertes.fr/hal-03159582
Autor:
Tim Kraska, Madelon Hulsebos, Çağatay Demiralp, César A. Hidalgo, Kevin Hu, Arvind Satyanarayan, Guoliang Li, Snehalkumar (Neil) S. Gaikwad, Michiel A. Bakker, Emanuel Zgraggen
Publikováno v:
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
2019 CHI Conference on Human Factors in Computing Systems
2019 CHI Conference on Human Factors in Computing Systems, May 2019, Glasgow, United Kingdom. ⟨10.1145/3290605.3300892⟩
MIT web domain
CHI
CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
2019 CHI Conference on Human Factors in Computing Systems
2019 CHI Conference on Human Factors in Computing Systems, May 2019, Glasgow, United Kingdom. ⟨10.1145/3290605.3300892⟩
MIT web domain
CHI
Researchers currently rely on ad hoc datasets to train automated visualization tools and evaluate the effectiveness of visualization designs. These exemplars often lack the characteristics of real-world datasets, and their one-off nature makes it dif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0391a95cb2fd61d15d1629a327b0c568
https://hal.archives-ouvertes.fr/hal-03159766
https://hal.archives-ouvertes.fr/hal-03159766
Autor:
Emanuel Zgraggen, Tim Kraska, Eli Upfal, Nathaniel Weir, Philipp Eichmann, Arif Usta, Cyrus Cousins, Zeyuan Shang, Isabella Tromba, Prasetya Ajie Utama, Fuat Basik, Alex Galakatos, Carsten Binnig, Dylan Ebert, Robert C. Zeleznik, Yeounoh Chung, Linnan Wang, Andrew Crotty, Uğur Çetintemel, Amir Ilkhechi, Benedetto Buratti, Benjamin Hättasch
Publikováno v:
Lecture Notes in Business Information Processing
Real-Time Business Intelligence and Analytics ISBN: 9783030241230
Real-Time Business Intelligence and Analytics ISBN: 9783030241230
Date of Conference: 28 August-1 September 2017 Conference Name: 11th International Workshop on Real-Time Business Intelligence and Analytics, BIRTE 2017 Enabling interactive visualization over new datasets at “human speed” is key to democratizing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9291d29fce65caf715a69790c7349a57
https://hdl.handle.net/11693/53434
https://hdl.handle.net/11693/53434
Autor:
Tim Kraska, Emanuel Zgraggen, Robert C. Zeleznik, Zeyuan Shang, Yeounoh Chung, Cyrus Cousins, Eli Upfal, Carsten Binnig, Benedetto Buratti
Publikováno v:
DEEM@SIGMOD
Democratizing Data Science requires a fundamental rethinking of the way data analytics and model discovery is done. Available tools for analyzing massive data sets and curating machine learning models are limited in a number of fundamental ways. Firs
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b70ab16585a8a74b438e1de1f463c9da
https://doi.org/10.1145/3209889.3209891
https://doi.org/10.1145/3209889.3209891