HMaViz: Human-machine analytics for visual recommendation
Article No.: 32, Pages 1 - 9
Abstract
Visualizations are context-specific. Understanding the context of visualizations before deciding to use them is a daunting task since users have various backgrounds, and there are thousands of available visual representations (and their variances). To this end, this paper proposes a visual analytics framework to achieve the following research goals: (1) to automatically generate a number of suitable representations for visualizing the input data and present it to users as a catalog of visualizations with different levels of abstractions and data characteristics on one/two/multi-dimensional spaces (2) to infer aspects of the user’s interest based on their interactions (3) to narrow down a smaller set of visualizations that suit users analysis intention. The results of this process give our analytics system the means to better understand the user’s analysis process and enable it to better provide timely recommendations.
References
[1]
Robert Amar, James Eagan, and John Stasko. 2005. Low-Level Components of Analytic Activity in Information Visualization. In Proc. of the IEEE Symposium on Information Visualization. 15–24.
[2]
Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. 2002. Finite-time Analysis of the Multiarmed Bandit Problem. Mach. Learn. 47, 2-3 (May 2002), 235–256.
[3]
M. Behrisch, M. Blumenschein, N. W. Kim, L. Shao, M. El-Assady, J. Fuchs, D. Seebacher, A. Diehl, U. Brandes, H. Pfister, T. Schreck, D. Weiskopf, and D. A. Keim. 2018. Quality Metrics for Information Visualization. Computer Graphics Forum 37, 3 (2018), 625–662. https://doi.org/10.1111/cgf.13446 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.13446
[4]
Enrico Bertini, Andrada Tatu, and Daniel Keim. 2011. Quality metrics in high-dimensional data visualization: An overview and systematization. IEEE Transactions on Visualization and Computer Graphics 17, 12(2011), 2203–2212.
[5]
Michael Bostock, Vadim Ogievetsky, and Jeffrey Heer. 2011. D3 data-driven documents. IEEE Transactions on Visualization & Computer Graphics12 (2011), 2301–2309.
[6]
Gerhard Brewka, Thomas Eiter, and Miroslaw Truszczynski. 2011. Answer set programming at a glance. Commun. ACM 54, 12 (2011), 92–103.
[7]
David R Brillinger, Luisa T Fernholz, and Stephan Morgenthaler. 2014. The practice of data analysis: Essays in honor of John W. Tukey. Vol. 401. Princeton University Press.
[8]
E. T. Brown, A. Ottley, H. Zhao, Q. Lin, R. Souvenir, A. Endert, and R. Chang. 2014. Finding Waldo: Learning about Users from their Interactions. IEEE Transactions on Visualization and Computer Graphics 20, 12 (Dec 2014), 1663–1672. https://doi.org/10.1109/TVCG.2014.2346575
[9]
Christopher Collins, Natalia Andrienko, Tobias Schreck, Jing Yang, Jaegul Choo, Ulrich Engelke, Amit Jena, and Tim Dwyer. 2018. Guidance in the human–machine analytics process. Visual Informatics 2, 3 (2018), 166 – 180. https://doi.org/10.1016/j.visinf.2018.09.003
[10]
Tuan Nhon Dang, A. Anand, and L. Wilkinson. 2013. TimeSeer: Scagnostics for High-Dimensional Time Series. Visualization and Computer Graphics, IEEE Transactions on 19, 3(2013), 470–483. https://doi.org/10.1109/TVCG.2012.128
[11]
Tuan Nhon Dang and Leland Wilkinson. 2013. TimeExplorer: Similarity search time series by their signatures. In International Symposium on Visual Computing. Springer, 280–289.
[12]
T. N. Dang and L. Wilkinson. 2014. Transforming Scagnostics to Reveal Hidden Features. IEEE Transactions on Visualization and Computer Graphics 20, 12 (Dec 2014), 1624–1632. https://doi.org/10.1109/TVCG.2014.2346572
[13]
Aritra Dasgupta and Robert Kosara. 2010. Pargnostics: Screen-space metrics for parallel coordinates. IEEE Transactions on Visualization & Computer Graphics6 (2010), 1017–1026.
[14]
Victor Dibia and Çağatay Demiralp. 2018. Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks. arXiv preprint arXiv:1804.03126(2018).
[15]
Fabian Fischer, Johannes Fuchs, and Florian Mansmann. 2012. ClockMap: Enhancing Circular Treemaps with Temporal Glyphs for Time-Series Data. In EuroVis - Short Papers, Miriah Meyer and Tino Weinkaufs (Eds.). https://doi.org/10.2312/PE/EuroVisShort/EuroVisShort2012/097-101
[16]
Lijie Fu. 2009. Implementation of three-dimensional scagnostics. Master’s thesis, Dept. of Math., Univ. of Waterloo (2009).
[17]
Karl Ruben Gabriel. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika 58, 3 (1971), 453–467.
[18]
GeyserTimes. 2017. Eruptions of Old Faithful Geyser, May 2014 [online database]. https://geysertimes.org.
[19]
Nathaniel Good, J Ben Schafer, Joseph A Konstan, Al Borchers, Badrul Sarwar, Jon Herlocker, John Riedl, 1999. Combining collaborative filtering with personal agents for better recommendations. In AAAI/IAAI. 439–446.
[20]
David Gotz and Zhen Wen. 2009. Behavior-driven visualization recommendation. In Proceedings of the 14th international conference on Intelligent user interfaces. 315–324.
[21]
Isabelle Guyon and André Elisseeff. 2003. An Introduction to Variable and Feature Selection. J. Mach. Learn. Res. 3 (March 2003), 1157–1182. http://dl.acm.org/citation.cfm?id=944919.944968
[22]
J.A. Hartigan. 1975. Clustering Algorithms. John Wiley & Sons, New York.
[23]
D. M. Hawkins. 1980. Identification of outliers. Chapman and Hall, London [u.a.]. http://gso.gbv.de/DB=2.1/CMD?ACT=SRCHA&SRT=YOP&IKT=1016&TRM=ppn+02435757X&sourceid=fbw_bibsonomy
[24]
Harry Hochheiser and Ben Shneiderman. 2004. Dynamic Query Tools for Time Series Data Sets: Timebox Widgets for Interactive Exploration. Information Visualization 3, 1 (March 2004), 1–18. https://doi.org/10.1145/993176.993177
[25]
Kevin Hu, Michiel A Bakker, Stephen Li, Tim Kraska, and César Hidalgo. 2019. Vizml: A machine learning approach to visualization recommendation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–12.
[26]
Plotly Technologies Inc.2015. Collaborative data science. https://plot.ly
[27]
J. Karim. 2014. Hybrid system for personalized recommendations. In 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS). 1–6. https://doi.org/10.1109/RCIS.2014.6861080
[28]
Daniel A Keim. 2002. Information visualization and visual data mining. IEEE Transactions on Visualization & Computer Graphics1 (2002), 1–8.
[29]
David Koop, Carlos E Scheidegger, Steven P Callahan, Juliana Freire, and Cláudio T Silva. 2008. Viscomplete: Automating suggestions for visualization pipelines. IEEE Transactions on Visualization and Computer Graphics 14, 6(2008), 1691–1698.
[30]
Lihong Li, Wei Chu, John Langford, and Robert E. Schapire. 2010. A Contextual-Bandit Approach to Personalized News Article Recommendation. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (WWW ’10). Association for Computing Machinery, New York, NY, USA, 661–670. https://doi.org/10.1145/1772690.1772758
[31]
J. Mackinlay, P. Hanrahan, and C. Stolte. 2007. Show Me: Automatic Presentation for Visual Analysis. IEEE Transactions on Visualization and Computer Graphics 13, 6 (Nov 2007), 1137–1144. https://doi.org/10.1109/TVCG.2007.70594
[32]
José Matute, Alexandru C Telea, and Lars Linsen. 2018. Skeleton-based Scagnostics. IEEE Transactions on Visualization & Computer Graphics1 (2018), 1–1.
[33]
D. Mladenic. 1999. Text-learning and related intelligent agents: a survey. IEEE Intelligent Systems and their Applications 14, 4 (July 1999), 44–54. https://doi.org/10.1109/5254.784084
[34]
Dominik Moritz, Chenglong Wang, Gregory Nelson, Halden Lin, Adam M. Smith, Bill Howe, and Jeffrey Heer. 2019. Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis) (2019). http://idl.cs.washington.edu/papers/draco
[35]
Laurence Moroney. 2017. The firebase realtime database. In The Definitive Guide to Firebase. Springer, 51–71.
[36]
Belgin Mutlu, Eduardo Veas, and Christoph Trattner. 2016. Vizrec: Recommending personalized visualizations. ACM Transactions on Interactive Intelligent Systems (TiiS) 6, 4(2016), 31.
[37]
Pawandeep Kaurand Michael Owonibi. 2017. A Review on Visualization Recommendation Strategies. (2017).
[38]
G. Palmas, M. Bachynskyi, A. Oulasvirta, H. P. Seidel, and T. Weinkauf. 2014. An Edge-Bundling Layout for Interactive Parallel Coordinates. In 2014 IEEE Pacific Visualization Symposium. 57–64. https://doi.org/10.1109/PacificVis.2014.40
[39]
R. Pamula, J. K. Deka, and S. Nandi. 2011. An Outlier Detection Method Based on Clustering. In 2011 Second International Conference on Emerging Applications of Information Technology. 253–256. https://doi.org/10.1109/EAIT.2011.25
[40]
Prasad Patil. 2018. What is Exploratory Data Analysis?https://towardsdatascience.com/exploratory-data-analysis-8fc1cb20fd15 Accessed: 2020-05-05.
[41]
V. Pham and T. Dang. 2019. Outliagnostics: Visualizing Temporal Discrepancy in Outlying Signatures of Data Entries. In 2019 IEEE Visualization in Data Science (VDS). IEEE, Vancouver, BC, Canada, Canada, 29–37. https://doi.org/10.1109/VDS48975.2019.8973379
[42]
Vung Pham and Tommy Dang. 2019. SOAViz: Visualization for Portable X-ray Fluorescence Soil Profiles. In Workshop on Visualisation in Environmental Sciences (EnvirVis), Roxana Bujack, Kathrin Feige, Karsten Rink, and Dirk Zeckzer(Eds.). The Eurographics Association. https://doi.org/10.2312/envirvis.20191102
[43]
Vung Pham and Tommy Dang. 2020. ScagnosticsJS: Extended Scatterplot Visual Features for the Web. In Eurographics 2020 - Short Papers, Alexander Wilkie and Francesco Banterle (Eds.). The Eurographics Association. https://doi.org/10.2312/egs.20201022
[44]
Vung Pham, Ngan Nguyen, and Tommy Dang. 2020. ContiMap: Continuous Heatmap for Large Time Series Data. In 2020 Visualization in Data Science (VDS). 42–51. https://doi.org/10.1109/VDS51726.2020.00009
[45]
Steven F Roth and Joe Mattis. 1990. Data characterization for intelligent graphics presentation. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 193–200.
[46]
Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, and Jeffrey Heer. 2017. Vega-lite: A grammar of interactive graphics. IEEE Transactions on Visualization and Computer Graphics 23, 1(2017), 341–350.
[47]
J. Ben Schafer, Joseph Konstan, and John Riedl. 1999. Recommender Systems in E-Commerce. In Proceedings of the 1st ACM Conference on Electronic Commerce (Denver, Colorado, USA) (EC ’99). Association for Computing Machinery, New York, NY, USA, 158–166. https://doi.org/10.1145/336992.337035
[48]
Jinwook Seo and Ben Shneiderman. 2004. A rank-by-feature framework for unsupervised multidimensional data exploration using low dimensional projections. In Information Visualization, 2004. INFOVIS 2004. IEEE Symposium on. IEEE, 65–72.
[49]
Chris Stolte, Diane Tang, and Pat Hanrahan. 2002. Polaris: A system for query, analysis, and visualization of multidimensional relational databases. IEEE Transactions on Visualization and Computer Graphics 8, 1(2002), 52–65.
[50]
Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence 2009 (2009).
[51]
Manasi Vartak, Silu Huang, Tarique Siddiqui, Samuel Madden, and Aditya Parameswaran. 2017. Towards visualization recommendation systems. ACM SIGMOD Record 45, 4 (2017), 34–39.
[52]
Martin Voigt, Martin Franke, and Klaus Meissner. 2013. Using expert and empirical knowledge for context-aware recommendation of visualization components. Int. J. Adv. Life Sci 5(2013), 27–41.
[53]
Kazuho Watanabe, Hsiang-Yun Wu, Yusuke Niibe, Shigeo Takahashi, and Issei Fujishiro. 2015. Biclustering multivariate data for correlated subspace mining. In Visualization Symposium (PacificVis), 2015 IEEE Pacific. IEEE, 287–294.
[54]
Leland Wilkinson. 2017. Visualizing Big Data Outliers through Distributed Aggregation. IEEE transactions on visualization and computer graphics (2017).
[55]
Leland Wilkinson, Anushka Anand, and Robert Grossman. 2005. Graph-theoretic scagnostics. (2005).
[56]
L. Wilkinson, A. Anand, and R. Grossman. 2006. High-Dimensional Visual Analytics: Interactive Exploration Guided by Pairwise Views of Point Distributions. IEEE Transactions on Visualization and Computer Graphics 12, 6(2006), 1363–1372.
[57]
Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe, and Jeffrey Heer. 2016. Voyager: Exploratory analysis via faceted browsing of visualization recommendations. IEEE Transactions on Visualization & Computer Graphics1 (2016), 1–1.
[58]
Kanit Wongsuphasawat, Zening Qu, Dominik Moritz, Riley Chang, Felix Ouk, Anushka Anand, Jock Mackinlay, Bill Howe, and Jeffrey Heer. 2017. Voyager 2: Augmenting visual analysis with partial view specifications. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. 2648–2659.
[59]
A Yates, Amy Webb, M Sharpnack, H Chamberlin, Kun Huang, and Raghu Machiraju. 2014. Visualizing multidimensional data with glyph sploms. In Computer Graphics Forum, Vol. 33. Wiley Online Library, 301–310.
Index Terms
- HMaViz: Human-machine analytics for visual recommendation
Index terms have been assigned to the content through auto-classification.
Recommendations
Immersive Analytics: A User-Centered Perspective
VRST '21: Proceedings of the 27th ACM Symposium on Virtual Reality Software and TechnologyResearchers have explored using VR and 3D data visualizations for analyzing and presenting data for several decades. Surveys of the literature in the field usually adopt a technical or systemic lens. We propose a survey of the Immersive Analytics ...
Visual Analytics Infrastructures: From Data Management to Exploration
Analysts exploring big data require more from information visualization, data analysis, and data management than these components can now deliver. New infrastructures must address the nature of exploration as well as data scale. The Web extra at http://...
Big data exploration through visual analytics
VAST '12: Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST)SAS® Visual Analytics Explorer is an advanced data visualization and exploratory data analysis application that is a component of the SAS Visual Analytics solution. It excels at handling big data problems like the VAST challenge. With a wide range of ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2021
281 pages
ISBN:9781450390125
DOI:10.1145/3468784
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 July 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
IAIT2021
IAIT2021: The 12th International Conference on Advances in Information Technology
June 29 - July 1, 2021
Bangkok, Thailand
Acceptance Rates
Overall Acceptance Rate 20 of 47 submissions, 43%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 62Total Downloads
- Downloads (Last 12 months)11
- Downloads (Last 6 weeks)0
Reflects downloads up to 19 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format