{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T06:59:21Z","timestamp":1758265161412},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,7]]},"abstract":"<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>\n          <jats:p>\n            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\n            <jats:italic>Davos<\/jats:italic>\n            , Einblick's novel backend.\n            <jats:italic>Davos<\/jats:italic>\n            combines aspects of progressive computation, approximate query processing and sampling, with a specific focus on supporting user-defined operations. Moreover,\n            <jats:italic>Davos<\/jats:italic>\n            optimizes multi-tenant scenarios to promote collaboration. Both empirical evaluation and user study verify that\n            <jats:italic>Davos<\/jats:italic>\n            can greatly empower data analytics for new needs.\n          <\/jats:p>","DOI":"10.14778\/3476311.3476370","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T22:48:56Z","timestamp":1635461336000},"page":"2893-2905","source":"Crossref","is-referenced-by-count":7,"title":["Davos"],"prefix":"10.14778","volume":"14","author":[{"given":"Zeyuan","family":"Shang","sequence":"first","affiliation":[{"name":"Einblick Analytics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emanuel","family":"Zgraggen","sequence":"additional","affiliation":[{"name":"Einblick Analytics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benedetto","family":"Buratti","sequence":"additional","affiliation":[{"name":"Einblick Analytics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp","family":"Eichmann","sequence":"additional","affiliation":[{"name":"Einblick Analytics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Navid","family":"Karimeddiny","sequence":"additional","affiliation":[{"name":"Einblick Analytics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charlie","family":"Meyer","sequence":"additional","affiliation":[{"name":"Einblick Analytics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wesley","family":"Runnels","sequence":"additional","affiliation":[{"name":"Einblick Analytics"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tim","family":"Kraska","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"[n.d.]. Apache Arrow. https:\/\/arrow.apache.org\/.  [n.d.]. Apache Arrow. https:\/\/arrow.apache.org\/."},{"key":"e_1_2_1_2_1","unstructured":"[n.d.]. Apache Flink. https:\/\/flink.apache.org\/.  [n.d.]. Apache Flink. https:\/\/flink.apache.org\/."},{"key":"e_1_2_1_3_1","unstructured":"[n.d.]. Apache Hadoop YARN. https:\/\/hadoop.apache.org\/docs\/current\/hadoop-yarn\/hadoop-yarn-site\/YARN.html.  [n.d.]. Apache Hadoop YARN. https:\/\/hadoop.apache.org\/docs\/current\/hadoop-yarn\/hadoop-yarn-site\/YARN.html."},{"key":"e_1_2_1_4_1","unstructured":"[n.d.]. Apache Storm. https:\/\/storm.apache.org\/.  [n.d.]. Apache Storm. https:\/\/storm.apache.org\/."},{"key":"e_1_2_1_5_1","unstructured":"[n.d.]. The BF Scheduler. https:\/\/en.wikipedia.org\/wiki\/Brain_Fuck_Scheduler.  [n.d.]. The BF Scheduler. https:\/\/en.wikipedia.org\/wiki\/Brain_Fuck_Scheduler."},{"key":"e_1_2_1_6_1","unstructured":"[n.d.]. Descriptive analytics 101: What happened? https:\/\/www.ibm.com\/blogs\/business-analytics\/descriptive-analytics-101-what-happened\/.  [n.d.]. Descriptive analytics 101: What happened? https:\/\/www.ibm.com\/blogs\/business-analytics\/descriptive-analytics-101-what-happened\/."},{"key":"e_1_2_1_7_1","unstructured":"[n.d.]. Einblick demo video. https:\/\/www.youtube.com\/watch?v=4eb_idT4YrM.  [n.d.]. Einblick demo video. https:\/\/www.youtube.com\/watch?v=4eb_idT4YrM."},{"key":"e_1_2_1_8_1","unstructured":"[n.d.]. Google Cloud. https:\/\/cloud.google.com\/.  [n.d.]. Google Cloud. https:\/\/cloud.google.com\/."},{"key":"e_1_2_1_9_1","unstructured":"[n.d.]. Monetdb. https:\/\/www.monetdb.org\/.  [n.d.]. Monetdb. https:\/\/www.monetdb.org\/."},{"key":"e_1_2_1_10_1","unstructured":"[n.d.]. Pandas. https:\/\/pandas.pydata.org\/.  [n.d.]. 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