research-article

The case for data visualization management systems: vision paper

Authors:

Leilani Battle, and

Samuel R. MaddenAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 7, Issue 10

Pages 903 - 906

https://doi.org/10.14778/2732951.2732964

Published: 01 June 2014 Publication History

Abstract

Most visualizations today are produced by retrieving data from a database and using a specialized visualization tool to render it. This decoupled approach results in significant duplication of functionality, such as aggregation and filters, and misses tremendous opportunities for cross-layer optimizations. In this paper, we present the case for an integrated Data Visualization Management System (DVMS) based on a declarative visualization language that fully compiles the end-to-end visualization pipeline into a set of relational algebra queries. Thus the DVMS can be both expressive via the visualization language, and performant by lever-aging traditional and visualization-specific optimizations to scale interactive visualizations to massive datasets.

References

[1]

ggplot2. ggplot2.org.

[2]

CIDR 2013, Sixth Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 6-9, 2013, Online Proceedings, 2013.

[3]

The lyra visualization design environment (vde), February 2014. http://idl.cs.washington.edu/projects/lyra/.

[4]

S. Agarwal, B. Mozafari, A. Panda, H. Milner, S. Madden, and I. Stoica. Blinkdb: queries with bounded errors and bounded response times on very large data. 2013.

Digital Library

[5]

L. Battle, R. Chang, and M. Stonebraker. Dynamic reduction of query result sets for interactive visualization. 2013.

[6]

M. Bostock and J. Heer. Protovis: A graphical toolkit for visualization. InfoVis, 2009.

Digital Library

[7]

M. Bostock, V. Ogievetsky, and J. Heer. D3: Data-driven documents. InfoVis, 2011.

Digital Library

[8]

S. Chaudhuri, V. Narasayya, and R. Ramamurthy. Estimating progress of execution for sql queries. In SIGMOD, 2004.

Digital Library

[9]

S. Chaudhuri and V. R. Narasayya. Autoadmin 'what-if' index analysis utility. In SIGMOD, 1998.

Digital Library

[10]

D. Cohen-Or, Y. L. Chrysanthou, C. T. Silva, and F. Durand. A survey of visibility for walkthrough applications. IEEE Transactions on Visualization and Computers, 2003.

Digital Library

[11]

J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. KDD, 1997.

[12]

J. D. Hunter. Matplotlib: A 2d graphics environment. Computing In Science & Engineering, 2007.

Digital Library

[13]

R. Ikeda and J. Widom. Panda: A system for provenance and data. IEEE Data Eng. Bull., 2010.

[14]

J.-F. Im, F. G. Villegas, and M. J. McGuffin. Visreduce: Fast and responsive incremental information visualization of large datasets. In BigData Conference, 2013.

[15]

N. Kamat, P. Jayachandran, K. Tunga, and A. Nandi. Distributed and Interactive Cube Exploration. In ICDE, 2014.

[16]

S. Kandel, A. Paepcke, J. Hellerstein, and H. Jeffrey. Enterprise data analysis and visualization: An interview study. VAST, 2012.

Digital Library

[17]

A. Kemper and G. Moerkotte. Advanced query processing in object bases using access support relations. In VLDB, 1990.

Digital Library

[18]

A. Key, B. Howe, D. Perry, and C. R. Aragon. Vizdeck: self-organizing dashboards for visual analytics. SIGMOD, 2012.

Digital Library

[19]

L. D. Lins, J. T. Klosowski, and C. E. Scheidegger. Nanocubes for real-time exploration of spatiotemporal datasets. IEEE Transactions on Visualization and Computer Graphics, 2013.

Digital Library

[20]

Z. Liu, B. Jiang, and J. Heer. immens: Real-time visual querying of big data. EuroVis, 2013.

Digital Library

[21]

J. Mackinlay, P. Hanrahan, and C. Stolte. Show me: Automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics, 2007.

Digital Library

[22]

A. Meliou, W. Gatterbauer, and D. Suciu. Reverse data management. PVLDB, 2011.

Digital Library

[23]

A. Oliva and A. Torralba. Building the gist of a scene: the role of global image features in recognition. In Progress in Brain Research, 2006.

[24]

A. Parameswaran, N. Polyzotis, and H. Garcia-Molina. Seedb: Visualizing database queries efficiently. PVLDB, 2014.

Digital Library

[25]

S. Sarawagi and G. Sathe. i3: Intelligent, interactive investigaton of olap data cubes. In SIGMOD, 2000.

Digital Library

[26]

P. G. Selinger, M. M. Astrahan, D. D. Chamberlin, R. A. Lorie, and T. G. Price. Access path selection in a relational database management system. In SIGMOD, 1979.

Digital Library

[27]

C. Stolte and P. Hanrahan. Polaris: A system for query, analysis and visualization of multi-dimensional relational databases. InfoVis, 2002.

Digital Library

[28]

R. Wesley, M. Eldridge, and P. T. Terlecki. An analytic data engine for visualization in tableau. In SIGMOD, 2011.

Digital Library

[29]

H. Wickham. Bin-summarise-smooth: a framework for visualising large data. Technical report, had.co.nz, 2013.

[30]

L. Wilkinson. The Grammar of Graphics (Statistics and Computing). Springer-Verlag New York, Inc., 2005.

Digital Library

[31]

W. Willett, J. Heer, and M. Agrawala. Strategies for crowdsourcing social data analysis. In CHI, 2012.

Digital Library

[32]

E. Wu and S. Madden. Scorpion: Explaining away outliers in aggregate queries. PVLDB, 2013.

Digital Library

[33]

E. Wu, S. Madden, and M. Stonebraker. Subzero: A fine-grained lineage system for scientific databases. In ICDE, 2013.

Digital Library

Cited By

Heer JMoritz D(2024)Mosaic: An Architecture for Scalable & Interoperable Data ViewsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332718930:1(436-446)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327189
Siddiqui TChaudhuri SNarasayya V(2021)COMPAREProceedings of the VLDB Endowment10.14778/3476249.347629114:11(2419-2431)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476291
Eldawy AHristidis VGhosh SSaeedan MSevim ASiddique ASingla SSivaram GVu TZhang YDemartini GZuccon GCulpepper JHuang ZTong H(2021)BeastProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481897(3796-3807)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481897
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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 7, Issue 10

June 2014

146 pages

ISSN:2150-8097

Editors:
H. V. Jagadish
University of Michigan
,
Aoying Zhou
East Normal University, China

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VLDB Endowment

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Published: 01 June 2014

Published in PVLDB Volume 7, Issue 10

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Cited By

Heer JMoritz D(2024)Mosaic: An Architecture for Scalable & Interoperable Data ViewsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332718930:1(436-446)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327189
Siddiqui TChaudhuri SNarasayya V(2021)COMPAREProceedings of the VLDB Endowment10.14778/3476249.347629114:11(2419-2431)Online publication date: 27-Oct-2021
https://dl.acm.org/doi/10.14778/3476249.3476291
Eldawy AHristidis VGhosh SSaeedan MSevim ASiddique ASingla SSivaram GVu TZhang YDemartini GZuccon GCulpepper JHuang ZTong H(2021)BeastProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481897(3796-3807)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481897
Yu JSarwat M(2021)GeoSparkViz: a cluster computing system for visualizing massive-scale geospatial dataThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-020-00645-230:2(237-258)Online publication date: 7-Jan-2021
https://dl.acm.org/doi/10.1007/s00778-020-00645-2
Espinosa-Oviedo J(2021)Streaming and Visualising Neuronal Signals for Understanding PainService-Oriented Computing – ICSOC 2021 Workshops10.1007/978-3-031-14135-5_18(228-235)Online publication date: 22-Nov-2021
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Budiu MGopalan PSuresh LWieder UKruiger HAguilera M(2019)HillviewProceedings of the VLDB Endowment10.14778/3342263.334227912:11(1442-1457)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.14778/3342263.3342279
Omidvar-Tehrani BAmer-Yahia SSimon EZegarra FComba JMoreira V(2019)UserDEVProceedings of the Workshop on Human-In-the-Loop Data Analytics10.1145/3328519.3329128(1-8)Online publication date: 5-Jul-2019
https://dl.acm.org/doi/10.1145/3328519.3329128
Mottin DLissandrini MVelegrakis YPalpanas TBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Exploring the Data Wilderness through ExamplesProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3314031(2031-2035)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3314031
Omidvar-Tehrani BAmer-Yahia SBoncz PManegold SAilamaki ADeshpande AKraska T(2019)Data Pipelines for User Group AnalyticsProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3314028(2048-2053)Online publication date: 25-Jun-2019
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Qin XLuo YTang NLi G(2019)Making data visualization more efficient and effective: a surveyThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00588-329:1(93-117)Online publication date: 19-Nov-2019
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