research-article

Towards Visualization Recommendation Systems

Authors:

Tarique Siddiqui,

Aditya ParameswaranAuthors Info & Claims

ACM SIGMOD Record, Volume 45, Issue 4

Pages 34 - 39

https://doi.org/10.1145/3092931.3092937

Published: 11 May 2017 Publication History

Abstract

Data visualization is often used as the first step while performing a variety of analytical tasks. With the advent of large, high-dimensional datasets and significant interest in data science, there is a need for tools that can support rapid visual analysis. In this paper we describe our vision for a new class of visualization systems, namely visualization recommendation systems, that can automatically identify and interactively recommend visualizations relevant to an analytical task. We detail the key requirements and design considerations for a visualization recommendation system. We also identify a number of challenges in realizing this vision and describe some approaches to address them.

References

[1]

Spotfire, http://www.tibco.com/company/news/releases/2015/tibcoannounces-recommendations-for-spotfire-cloud. {Online; accessed 17-Aug-2015}.

[2]

Tableau showme. {Online; accessed 17-Aug-2015}.

[3]

S. Acharya et al. The aqua approximate query answering system. SIGMOD '99, pages 574--576, New York, NY, USA, 1999. ACM.

Digital Library

[4]

G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. TKDE, 17(6):734--749, 2005.

Digital Library

[5]

S. Agarwal et al. Blinkdb: Queries with bounded errors and bounded response times on very large data. EuroSys '13, 2013.

Digital Library

[6]

Y. Benjamini and D. Yekutieli. The control of the false discovery rate in multiple testing under dependency. Annals of statistics, pages 1165--1188, 2001.

[7]

J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez. Recommender systems survey. Knowledge-Based Systems, 46:109--132, 2013.

Digital Library

[8]

C. E. Bonferroni. Teoria statistica delle classi e calcolo delle probabilit`a. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8:3--62, 1936.

[9]

J. Boy, R. A. Rensink, E. Bertini, and J.-D. Fekete. A principled way of assessing visualization literacy. IEEE transactions on visualization and computer graphics, 20(12):1963--1972, 2014.

[10]

D. Feldman, M. Schmidt, and C. Sohler. Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering. SODA '13, pages 1434--1453, 2013.

Digital Library

[11]

J. B. Garfield. Assessing statistical reasoning. Statistics Education Research Journal, 2(1):22--38, 2003.

[12]

D. Gotz and Z. Wen. Behavior-driven visualization recommendation. IUI '09, pages 315--324, New York, NY, USA, 2009. ACM.

Digital Library

[13]

T. M. Green, W. Ribarsky, and B. Fisher. Building and applying a human cognition model for visual analytics. Information Visualization, 8(1):1--13, Jan. 2009.

Digital Library

[14]

P. Hanrahan. Analytic database technologies for a new kind of user: the data enthusiast. In SIGMOD Conference, pages 577--578, 2012.

Digital Library

[15]

I. Herman, G. Melancón, and M. S. Marshall. Graph visualization and navigation in information visualization: A survey. TVCG, 6(1):24--43, 2000.

Digital Library

[16]

S. Holland, M. Ester, and W. Kießling. Preference mining: A novel approach on mining user preferences for personalized applications. In PKDD 2003, pages 204--216. Springer, 2003.

[17]

G. Holmes, A. Donkin, and I. H. Witten. Weka: A machine learning workbench. In Conf. on Intelligent Information Systems '94, pages 357--361. IEEE, 1994.

[18]

E. Horvitz. Principles of mixed-initiative user interfaces. CHI'99, pages 159--166. ACM, 1999.

Digital Library

[19]

S. Kandel et al. Profiler: integrated statistical analysis and visualization for data quality assessment. In AVI, pages 547--554, 2012.

Digital Library

[20]

D. Keim et al. Information visualization and visual data mining. TVCG, 8(1):1--8, 2002.

Digital Library

[21]

A. Key, B. Howe, D. Perry, and C. Aragon. Vizdeck: Self-organizing dashboards for visual analytics. SIGMOD '12, pages 681--684, 2012.

Digital Library

[22]

A. Kim, E. Blais, A. Parameswaran, P. Indyk, S. Madden, and R. Rubinfeld. Rapid sampling for visualizations with ordering guarantees. Proc. VLDB Endow., 8(5):521--532, Jan. 2015.

Digital Library

[23]

M. Kreuseler, N. Lopez, and H. Schumann. A scalable framework for information visualization. INFOVIS '00, pages 27--, Washington, DC, USA, 2000. IEEE Computer Society.

Digital Library

[24]

G. Linden, B. Smith, and J. York. Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1):76--80, 2003.

Digital Library

[25]

L. Lins, J. T. Klosowski, and C. Scheidegger. Nanocubes for real--time exploration of spatiotemporal datasets. IEEE Transactions on Visualization and Computer Graphics, 19(12):2456--2465, 2013.

Digital Library

[26]

S. Liu, W. Cui, Y. Wu, and M. Liu. A survey on information visualization: recent advances and challenges. The Visual Computer, 30(12):1373--1393, 2014.

Digital Library

[27]

J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Trans. Graph., 5(2):110--141, Apr. 1986.

Digital Library

[28]

J. D. Mackinlay et al. Show me: Automatic presentation for visual analysis. IEEE Trans. Vis. Comput. Graph., 13(6):1137--1144, 2007.

Digital Library

[29]

B. Mobasher, R. Cooley, and J. Srivastava. Automatic personalization based on web usage mining. Commun. ACM, 43(8):142--151, Aug. 2000.

Digital Library

[30]

K. Morton et al. Support the data enthusiast: Challenges for next-generation data--analysis systems. PVLDB, 7(6):453--456, 2014.

Digital Library

[31]

C. H. Papadimitriou and M. Yannakakis. Multiobjective query optimization. In P. Buneman, editor, PODS. ACM, 2001.

Digital Library

[32]

A. Parameswaran, N. Polyzotis, and H. Garcia-Molina. Seedb: Visualizing database queries efficiently. PVLDB, 7(4), 2013.

Digital Library

[33]

Pedregosa et al. Scikit-learn: Machine learning in python. The Journal of Machine Learning Research, 12:2825--2830, 2011.

Digital Library

[34]

T. K. Sellis. Multiple-query optimization. ACM Trans. Database Syst., 13(1):23--52, Mar. 1988.

Digital Library

[35]

J. Seo and B. Shneiderman. A rank-by-feature framework for interactive exploration of multidimensional data. Information Visualization, 4(2):96--113, 2005.

Digital Library

[36]

T. Siddiqui et al. Fast--forwarding to desired visualizations with zenvisage. In CIDR, 2017.

[37]

T. Siddiqui, A. Kim, J. Lee, K. Karahalios, and A. Parameswaran. Effortless visual data exploration with zenvisage: An expressive and interactive visual analytics system. In PVLDB, 2016.

Digital Library

[38]

S. T. Tokdar and R. E. Kass. Importance sampling: a review. Wiley Interdisciplinary Reviews: Computational Statistics, 2(1):54--60, 2010.

Digital Library

[39]

S. Trewin. Knowledge-based recommender systems. Encyclopedia of Library and Information Science: Volume 69-Supplement 32, page 180, 2000.

[40]

M. Vartak, S. Rahman, S. Madden, A. Parameswaran, and N. Polyzotis. Seedb: efficient data-driven visualization recommendations to support visual analytics. VLDB, 8(13):2182--2193, 2015.

Digital Library

[41]

J. Vermorel and M. Mohri. Multi-armed bandit algorithms and empirical evaluation. In ECML, pages 437--448, 2005.

Digital Library

[42]

M. Voigt et al. Context-aware recommendation of visualization components. In eKNOW'12, pages 101--109, 2012.

[43]

L. Wasserman. All of Statistics. Springer, 2003.

[44]

L. Wilkinson, A. Anand, and R. L. Grossman. Graph-theoretic scagnostics. In INFOVIS, volume 5, page 21, 2005.

Digital Library

[45]

G. Wills and L. Wilkinson. Autovis: automatic visualization. Information Visualization, 9(1):47--69, 2010.

Digital Library

[46]

K. Wongsuphasawat et al. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. IEEE Trans. Visualization & Comp. Graphics, 2015.

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Sharaf MHelal HZaki NAlketbi WAlkaabi LAlshamsi SAlhefeiti F(2024)Automated Recommendation of Aggregate Visualizations for Crowdfunding DataAlgorithms10.3390/a1706024417:6(244)Online publication date: 6-Jun-2024
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Information & Contributors

Information

Published In

cover image ACM SIGMOD Record

ACM SIGMOD Record Volume 45, Issue 4

December 2016

48 pages

ISSN:0163-5808

DOI:10.1145/3092931

Editors:
Yanlei Diao
University of Massachusetts Amherst
,
Vanessa Braganholo
Universidade Federal Fluminense
,
Marco Brambilla
Politecnico di Milano
,
Chee Yong Chan
National University of Singapore
,
Rada Chirkova
North Carolina State University
,
Zackary Ives
University of Pennsylvania
,
Anastasios Kementsietsidis
Google Research
,
Jeffrey Naughton
University of Wisconsin-Madison
,
Frank Neven
Hasselt University
,
Olga Papaemmanoui
Brandeis Univesity
,
Aditya Parameswaran
University of Illinois
,
Anish Das Sarma
Google Research
,
Alkis Simitsis
HP Labs
,
Wang-Chiew Tan
University of California Santa Cruz
,
Nesime Tatbul
MIT CSAIL
,
Marianne Winslett
University of Illinois
,
Jun Yang
Duke University

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Published: 11 May 2017

Published in SIGMOD Volume 45, Issue 4

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Cited By

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https://doi.org/10.4018/979-8-3693-6537-3.ch016
Coelho DGhai BKrishna AVelez-Rojas MGreenspan SMankovski SMueller K(2024)TaskFinder: A Semantics-Based Methodology for Visualization Task RecommendationAnalytics10.3390/analytics30300153:3(255-275)Online publication date: 4-Jul-2024
https://doi.org/10.3390/analytics3030015
Sharaf MHelal HZaki NAlketbi WAlkaabi LAlshamsi SAlhefeiti F(2024)Automated Recommendation of Aggregate Visualizations for Crowdfunding DataAlgorithms10.3390/a1706024417:6(244)Online publication date: 6-Jun-2024
https://doi.org/10.3390/a17060244
Song YZhao XWong RBaeza-Yates RBonchi F(2024)Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language ConversationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671935(2733-2744)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671935
Schmidt JPointner BMiksch S(2024)Visual Analytics for Understanding Draco's Knowledge BaseIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332691230:1(392-402)Online publication date: 1-Jan-2024
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Song YLu JZhao XWong RZhang H(2024)Demonstration of FeVisQA: Free-Form Question Answering over Data Visualization2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00417(5417-5420)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00417
Chen HJiang SYu XYin HWang XHu YWang CLi C(2024)GeoVis: a data-driven geographic visualization recommendation system via latent space encodingJournal of Visualization10.1007/s12650-024-00986-y27:4(603-622)Online publication date: 16-Apr-2024
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Ahmad GAgarwal SMitra SRossi RDoshi MPorwal VPaila S(2024)ScaleViz: Scaling Visualization Recommendation Models on Large DataAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2262-4_8(93-104)Online publication date: 7-May-2024
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Islam MAkter SIslam LRazzak IWang XXu G(2023)Strategies for evaluating visual analytics systems: A systematic review and new perspectivesInformation Visualization10.1177/1473871623121256823:1(84-101)Online publication date: 28-Dec-2023
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Bako HVarma AFaboro AHaider MNerrise FKenah BDickerson JBattle L(2023)User-Driven Support for Visualization Prototyping in D3Proceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584041(958-972)Online publication date: 27-Mar-2023
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