Davos: a system for interactive data-driven decision making
Author(s)
Shang, Zeyuan; Zgraggen, Emanuel; Buratti, Benedetto; Eichmann, Philipp; Karimeddiny, Navid; Meyer, Charlie; Runnels, Wesley; Kraska, Tim; ... Show more Show less
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<jats:p>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.</jats:p>
<jats:p>
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
<jats:italic>Davos</jats:italic>
, Einblick's novel backend.
<jats:italic>Davos</jats:italic>
combines aspects of progressive computation, approximate query processing and sampling, with a specific focus on supporting user-defined operations. Moreover,
<jats:italic>Davos</jats:italic>
optimizes multi-tenant scenarios to promote collaboration. Both empirical evaluation and user study verify that
<jats:italic>Davos</jats:italic>
can greatly empower data analytics for new needs.
</jats:p>
Date issued
2021Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
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