Davos: a system for interactive data-driven decision making
Name
p2893-shang.pdf
Description
Published version
Size
1.9 MB
Format
Adobe PDF
Checksum (MD5)
84dd82e4dc4c2ad50e38c5100fcfb128
Author(s)
Shang, Zeyuan
Zgraggen, Emanuel
Buratti, Benedetto
Eichmann, Philipp
Karimeddiny, Navid
Meyer, Charlie
Runnels, Wesley
Kraska, Tim
Date Issued
2021
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Citation
Shang, Zeyuan, Zgraggen, Emanuel, Buratti, Benedetto, Eichmann, Philipp, Karimeddiny, Navid et al. 2021. "Davos: a system for interactive data-driven decision making." Proceedings of the VLDB Endowment, 14 (12).
Version
Final published version
Abstract
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 significant challenge for people without technical expertise. In this regard, existing tools for data analytics which were designed decades ago still present a high bar for domain experts, and removing this bar requires a fundamental rethinking of both interface and backend.
At Einblick, an MIT/Brown spin-off based on the Northstar project, we have been building the next generation analytics tool in the last few years. To overcome the shortcomings of existing processing engines, we propose Davos , Einblick's novel backend. Davos combines aspects of progressive computation, approximate query processing and sampling, with a specific focus on supporting user-defined operations. Moreover, Davos optimizes multi-tenant scenarios to promote collaboration. Both empirical evaluation and user study verify that Davos can greatly empower data analytics for new needs.
At Einblick, an MIT/Brown spin-off based on the Northstar project, we have been building the next generation analytics tool in the last few years. To overcome the shortcomings of existing processing engines, we propose Davos , Einblick's novel backend. Davos combines aspects of progressive computation, approximate query processing and sampling, with a specific focus on supporting user-defined operations. Moreover, Davos optimizes multi-tenant scenarios to promote collaboration. Both empirical evaluation and user study verify that Davos can greatly empower data analytics for new needs.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.14778/3476311.3476370
