Selective Wander Join: Fast Progressive Visualizations for Data Joins Article Swipe
Marianne Procopio
,
Carlos Scheidegger
,
Eugene Wu
,
Remco Chang
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/informatics6010014
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/informatics6010014
Progressive visualization offers a great deal of promise for big data visualization; however, current progressive visualization systems do not allow for continuous interaction. What if users want to see more confident results on a subset of the visualization? This can happen when users are in exploratory analysis mode but want to ask some directed questions of the data as well. In a progressive visualization system, the online aggregation algorithm determines the database sampling rate and resulting convergence rate, not the user. In this paper, we extend a recent method in online aggregation, called Wander Join, that is optimized for queries that join tables, one of the most computationally expensive operations. This extension leverages importance sampling to enable user-driven sampling when data joins are in the query. We applied user interaction techniques that allow the user to view and adjust the convergence rate, providing more transparency and control over the online aggregation process. By leveraging importance sampling, our extension of Wander Join also allows for stratified sampling of groups when there is data distribution skew. We also improve the convergence rate of filtering queries, but with additional overhead costs not needed in the original Wander Join algorithm.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/informatics6010014
- https://www.mdpi.com/2227-9709/6/1/14/pdf?version=1553499356
- OA Status
- gold
- Cited By
- 9
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2924001750
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2924001750Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/informatics6010014Digital Object Identifier
- Title
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Selective Wander Join: Fast Progressive Visualizations for Data JoinsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-03-25Full publication date if available
- Authors
-
Marianne Procopio, Carlos Scheidegger, Eugene Wu, Remco ChangList of authors in order
- Landing page
-
https://doi.org/10.3390/informatics6010014Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-9709/6/1/14/pdf?version=1553499356Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2227-9709/6/1/14/pdf?version=1553499356Direct OA link when available
- Concepts
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Computer science, Joins, Visualization, Join (topology), Data mining, Rate of convergence, Sampling (signal processing), Theoretical computer science, Key (lock), Programming language, Filter (signal processing), Computer vision, Mathematics, Combinatorics, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 3, 2022: 1, 2021: 1, 2020: 3Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2227-9709/6/1/14/pdf?version=1553499356 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Informatics |
| primary_location.landing_page_url | https://doi.org/10.3390/informatics6010014 |
| publication_date | 2019-03-25 |
| publication_year | 2019 |
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