Zobrazeno 1 - 10
of 17
pro vyhledávání: '"El Kindi Rezig"'
Autor:
Rowan Hart, Brian Hays, Connor McMillin, El Kindi Rezig, Gustavo Rodriguez-Rivera, Jeffrey A. Turkstra
Publikováno v:
Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1.
Autor:
El Kindi Rezig, Vijay Gadepally, Michael Stonebraker, Benjamin Price, Anna Fariha, Allan Vanterpool, Anshul Bhandari
Publikováno v:
Proceedings of the VLDB Endowment. 14:2819-2822
In order to conduct analytical tasks, data scientists often need to find relevant data from an avalanche of sources (e.g., data lakes, large organizational databases). This effort is typically made in an ad hoc, non-systematic manner, which makes it
Autor:
Mourad Ouzzani, Ahmed K. Elmagarmid, El Kindi Rezig, Walid G. Aref, Ahmed R. Mahmood, Michael Stonebraker
Publikováno v:
Proceedings of the VLDB Endowment. 14:2546-2554
A large class of data repair algorithms rely on integrity constraints to detect and repair errors. A well-studied class of constraints is Functional Dependencies (FDs, for short). Although there has been an increased interest in developing general da
Publikováno v:
Heterogeneous Data Management, Polystores, and Analytics for Healthcare ISBN: 9783031239045
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b9c104c79081a837a1a2a56c1e9a0ad
https://doi.org/10.1007/978-3-031-23905-2_2
https://doi.org/10.1007/978-3-031-23905-2_2
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
Autor:
El Kindi Rezig, Allan Vanterpool, Michael Stonebraker, Vijay Gadepally, Michael Cafarella, Benjamin Price
Publikováno v:
Heterogeneous Data Management, Polystores, and Analytics for Healthcare ISBN: 9783030710545
Poly/DMAH@VLDB
Poly/DMAH@VLDB
Data scientists today have to query an avalanche of multi-source data (e.g., data lakes, company databases) for diverse analytical tasks. Data discovery is labor-intensive as users have to find the right tables, and the combination thereof to answer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7206e05f764b3a9490c74314a53d2e88
https://doi.org/10.1007/978-3-030-71055-2_6
https://doi.org/10.1007/978-3-030-71055-2_6
Autor:
El Kindi Rezig, Vijay Gadepally, Timothy Mattson, Michael Stonebraker, Tim Kraska, Jun Kong, Gang Luo, Dejun Teng, Fusheng Wang
This book constitutes revised selected papers from two VLDB workshops: The International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2022, and the 8th International Workshop o
Autor:
El Kindi Rezig, Vijay Gadepally, Timothy Mattson, Michael Stonebraker, Tim Kraska, Fusheng Wang, Gang Luo, Jun Kong, Alevtina Dubovitskaya
This book constitutes revised selected papers from two VLDB workshops: The International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2021, and the 7th International Workshop on
Publikováno v:
HILDA@SIGMOD
Data Cleaning refers to the process of detecting and fixing errors in the data. Human involvement is instrumental at several stages of this process such as providing rules or validating computed repairs. There is a plethora of data cleaning algorithm