Real-time collaborative analysis with (almost) pure SQL

Halperin, Daniel; Ribalet, François; Weitz, Konstantin; Saito, Mak A.; Howe, Bill; Armbrust, E. Virginia

doi:10.1145/2484838.2484880

Cited by 5 publications

(5 citation statements)

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“…( 50 ). In the future, late integration techniques ( 51 ) might be used to facilitate this process. A tool called Data Manager Library ( 26 ) might also be used for this purpose since it can use metadata describing the columnar attributes of a dataset to extract its contents and store it in a relational database.…”

Section: Resultsmentioning

confidence: 99%

BaMBa: towards the integrated management of Brazilian marine environmental data

et al. 2015

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A new open access database, Brazilian Marine Biodiversity (BaMBa) (https://marinebiodiversity.lncc.br), was developed in order to maintain large datasets from the Brazilian marine environment. Essentially, any environmental information can be added to BaMBa. Certified datasets obtained from integrated holistic studies, comprising physical–chemical parameters, -omics, microbiology, benthic and fish surveys can be deposited in the new database, enabling scientific, industrial and governmental policies and actions to be undertaken on marine resources. There is a significant number of databases, however BaMBa is the only integrated database resource both supported by a government initiative and exclusive for marine data. BaMBa is linked to the Information System on Brazilian Biodiversity (SiBBr, http://www.sibbr.gov.br/) and will offer opportunities for improved governance of marine resources and scientists’ integration.Database URL: http://marinebiodiversity.lncc.br

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Section: Resultsmentioning

confidence: 99%

BaMBa: towards the integrated management of Brazilian marine environmental data

et al. 2015

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“…Previous studies reported improved skills, higher achievements, more success, self-confidence, satisfaction, and better performance among online learners (Zhang et al, 2006;Chen and Jones, 2007;Al-Qahtani and Higgins, 2013;Kay and McKlin, 2014;Wilkowski et al, 2014;Thai et al, 2017;Tratnik et al, 2019;Al-Balas et al, 2020;Shahzad et al, 2021). SQL is a learning technology declarative computer language course to perform and solve different problems by updating and retrieving the data (Halperin et al, 2013;Ahadi et al, 2015). Due to the computer-based learning of SQL courses, it is significant that online learners be more satisfied and independent.…”

Section: Discussionmentioning

confidence: 99%

“…It describes what to perform and what not to while solving the problem. In SQL, various options allow retrieving and updating the data, focusing on essence rather than technique (Halperin et al, 2013). Previously, research revealed that a deep understanding of novices' common semantic mistakes when writing SQL queries would improve teaching and learning outcomes (Ahadi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Students’ Perception and Performance Regarding Structured Query Language Through Online and Face-to-Face Learning

et al. 2022

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This study explores the Structured Query Language (SQL) learners’ perceptions in online and face-to-face learning regarding the role of the instructor, clarity in lesson delivery and understanding, and concerns about the shift in learning mode. In parallel, we evaluate the performance of online and face-to-face SQL learners in the final examination. The COVID-19 pandemic has forced educational institutes to shift their activities online. Thus, online learning has been accepted during the pandemic and gradually evolving. The literature on online and face-to-face learning has evaluated limited variables. Yet, in online and face-to-face learning, critical parameters concerning the SQL learners’ perceptions about the role of instructors have not been explored. The present study surveyed the final-year students learning medium-level SQL courses at Bar-Ilan University Israel and the College of Management Academic Studies Israel. Survey questionnaires included demographic information, online learning experience, online learning sources, and ten questions about the learners’ concerns of shifting, effectiveness, adequate instructions, the lecturer’s clarity during instruction, clear understanding of the lesson, instructor’s tools, instructor’s availability, satisfactory response, learning independence, and spending extra time in online and face-to-face learning, separately. This study included 102 online learners and 95 face-to-face learners. All the online learners used Zoom and WhatsApp, and the face-to-face learners used Gmail and WhatsApp. Both online and face-to-face learners were significantly satisfied with the lecturer’s performance, especially with the clarity in lecture delivery, instructor availability, and satisfactory response from the lecturer. In addition, online learners agreed upon the effective way of learning, clear understanding of the lesson, independence, and spending extra time. In contrast, face-to-face learners were more satisfied with the tools of the lecturer and dissatisfied with the dependence on the lecturer. Female students attending face-to-face learning were more concerned about the shift in the mode of learning. Further, online learners performed better in written examinations and face-to-face learners in oral examinations. Notwithstanding, advancements are still required to redesign the online learning environment for critical thinking in higher education.

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“…In one experiment, we participated in a workshop between 40+ oceanographers from various subfields, using SQLShare to perform "SQL stenography": writing queries in real time to integrate data, test hypotheses, and populate visualizations in response to the live scientific discussion. We found that that ability to write queries in real-time significantly improved the productivity of the meeting, changing it from a "planning" meeting to a science meeting [7].…”

Section: User-facing Toolsmentioning

confidence: 99%

The database group at the University of Washington

2014

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The database group at the University of Washington (UW) was founded in 1998 when the department hired Alon Halevy (now at Google). The group currently consists of about twenty researchers: three faculty members (the authors), four postdocs, and fifteen students. Alumni include faculty members at Computer Science Departments at British Columbia, Michigan, Pennsylvania, Stanford, UMass, Wisconsin, one faculty member at the CMU Tepper School of Business, and several researchers and engineers at Facebook, Google, Microsoft, Nokia, Twitter, and other technology companies. The group has funding from NSF, the Gordon and Betty Moore Foundation, the Alfred P. Sloan Foundation, and several companies including Amazon, EMC, Google, HP, Intel, Microsoft, NEC, and Yahoo. The group has been recognized through several best paper awards and two ACM SIGMOD Best Dissertation Awards.We conduct research mostly in small groups and tackle a diverse set of data management challenges. Some of our projects result from collaborations with domain scientists on the UW campus; others are sparked by novel theoretical breakthroughs that lead to new approaches to data management challenges; many are the results of both. We give here a short overview of the recent research themes in our group; more details are available on our website: http://db.cs.washington.edu/ SCIENTIFIC DATA MANAGEMENTOur research agenda is partially derived from collaborations with scientists across the University of Washington and beyond, leveraging our close connection with the University of Washington eScience Institute [6].The eScience Institute was founded in 2005 with the goal of advancing the research and practice of dataintensive discovery across all fields of science. With the advent of new, high-bandwidth data sources (survey telescopes, high-throughput sequencers, ubiquitous sensor networks, planetary-scale simulations), data management research became recognized as a critical driver of scientific discovery. As a result, the database group and the eScience Institute became close partners, and were able to initiate and maintain multiple long-term collaborations with scientists.In 2008, we founded an inter-disciplinary research group called AstroDB [1]. This group brings together faculty, research scientists, postdocs, and students from the Astronomy department and our database group. In 2009, we initiated an independent collaboration with a marine microbiology lab. Thanks to the sustained nature of these partnerships, both have led to a series of joint research projects. We give examples in the following sections.Our inter-disciplinary collaborations have also allowed us to collect a curated repository of datasets and use cases that anyone can use in their research: A repository of MapReduce applications [15], a public repository of scientific datasets equipped with a SQL interface [19], and a number of parallel analytics use cases that go beyond MapReduce [14]. We are continuously working on expanding these collections of applications. BIG DATA SYSTEMSMotivated b...

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Real-time collaborative analysis with (almost) pure SQL

Cited by 5 publications

References 5 publications

BaMBa: towards the integrated management of Brazilian marine environmental data

BaMBa: towards the integrated management of Brazilian marine environmental data

Students’ Perception and Performance Regarding Structured Query Language Through Online and Face-to-Face Learning

The database group at the University of Washington

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