TY - GEN
T1 - Data debugging and exploration with vizier
AU - Brachmann, Mike
AU - Bautista, Carlos
AU - Castelo, Sonia
AU - Feng, Su
AU - Freire, Juliana
AU - Glavic, Boris
AU - Kennedy, Oliver
AU - Müller, Heiko
AU - Rampin, Rémi
AU - Spoth, William
AU - Yang, Ying
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2019/6/25
Y1 - 2019/6/25
N2 - We present Vizier, a multi-modal data exploration and debugging tool. The system supports a wide range of operations by seamlessly integrating Python, SQL, and automated data curation and debugging methods. Using Spark as an execution backend, Vizier handles large datasets in multiple formats. Ease-of-use is attained through integration of a notebook with a spreadsheet-style interface and with visualizations that guide and support the user in the loop. In addition, native support for provenance and versioning enable collaboration and uncertainty management. In this demonstration we will illustrate the diverse features of the system using several realistic data science tasks based on real data.
AB - We present Vizier, a multi-modal data exploration and debugging tool. The system supports a wide range of operations by seamlessly integrating Python, SQL, and automated data curation and debugging methods. Using Spark as an execution backend, Vizier handles large datasets in multiple formats. Ease-of-use is attained through integration of a notebook with a spreadsheet-style interface and with visualizations that guide and support the user in the loop. In addition, native support for provenance and versioning enable collaboration and uncertainty management. In this demonstration we will illustrate the diverse features of the system using several realistic data science tasks based on real data.
UR - http://www.scopus.com/inward/record.url?scp=85069450009&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069450009&partnerID=8YFLogxK
U2 - 10.1145/3299869.3320246
DO - 10.1145/3299869.3320246
M3 - Conference contribution
AN - SCOPUS:85069450009
T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data
SP - 1877
EP - 1880
BT - SIGMOD 2019 - Proceedings of the 2019 International Conference on Management of Data
PB - Association for Computing Machinery
T2 - 2019 International Conference on Management of Data, SIGMOD 2019
Y2 - 30 June 2019 through 5 July 2019
ER -