Davos: a system for interactive data-driven decision making
<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...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
VLDB Endowment
2022
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| Online Access: | https://hdl.handle.net/1721.1/143730 |
| _version_ | 1826198221815283712 |
|---|---|
| author | Shang, Zeyuan Zgraggen, Emanuel Buratti, Benedetto Eichmann, Philipp Karimeddiny, Navid Meyer, Charlie Runnels, Wesley Kraska, Tim |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Shang, Zeyuan Zgraggen, Emanuel Buratti, Benedetto Eichmann, Philipp Karimeddiny, Navid Meyer, Charlie Runnels, Wesley Kraska, Tim |
| author_sort | Shang, Zeyuan |
| collection | MIT |
| description | <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> |
| first_indexed | 2024-09-23T11:01:19Z |
| format | Article |
| id | mit-1721.1/143730 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:01:19Z |
| publishDate | 2022 |
| publisher | VLDB Endowment |
| record_format | dspace |
| spelling | mit-1721.1/1437302023-04-10T20:06:24Z Davos: a system for interactive data-driven decision making Shang, Zeyuan Zgraggen, Emanuel Buratti, Benedetto Eichmann, Philipp Karimeddiny, Navid Meyer, Charlie Runnels, Wesley Kraska, Tim Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science <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> 2022-07-14T13:53:00Z 2022-07-14T13:53:00Z 2021 2022-07-14T13:50:31Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/143730 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). en 10.14778/3476311.3476370 Proceedings of the VLDB Endowment Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf VLDB Endowment VLDB Endowment |
| spellingShingle | Shang, Zeyuan Zgraggen, Emanuel Buratti, Benedetto Eichmann, Philipp Karimeddiny, Navid Meyer, Charlie Runnels, Wesley Kraska, Tim Davos: a system for interactive data-driven decision making |
| title | Davos: a system for interactive data-driven decision making |
| title_full | Davos: a system for interactive data-driven decision making |
| title_fullStr | Davos: a system for interactive data-driven decision making |
| title_full_unstemmed | Davos: a system for interactive data-driven decision making |
| title_short | Davos: a system for interactive data-driven decision making |
| title_sort | davos a system for interactive data driven decision making |
| url | https://hdl.handle.net/1721.1/143730 |
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