research-article

Lytic: synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics

Authors:

Edward Clarkson,

Haesun ParkAuthors Info & Claims

IDEA '13: Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

Pages 36 - 44

https://doi.org/10.1145/2501511.2501518

Published: 11 August 2013 Publication History

Abstract

We present Lytic, a domain-independent, faceted visual analytic (VA) system for interactive exploration of large datasets. It combines a flexible UI that adapts to arbitrary character-separated value (CSV) datasets with algorithmic preprocessing to compute unsupervised dimension reduction and cluster data from high-dimensional fields. It provides a variety of visualization options that require minimal user effort to configure and a consistent user experience between visualization types and underlying datasets. Filtering, comparison and visualization operations work in concert, allowing users to hop seamlessly between actions and pursue answers to expected and unexpected data hypotheses.

References

[1]

Ahlberg, C. Spotfire: an information exploration environment. SIGMOD Rec. 25, 4 (1996), 25--29.

Digital Library

[2]

Aiken, A., Chen, J., Stonebraker, M., and Woodruff, A. Tioga-2: A Direct Manipulation Database Visualization Environment. (1996), 208--217.

Digital Library

[3]

Bae, S.-H., Choi, J. Y., Qiu, J., and Fox, G. C. Dimension reduction and visualization of large high-dimensional data via interpolation. Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, ACM (2010), 203--214.

Digital Library

[4]

Becker, R. A. and Cleveland, W. S. Brushing Scatterplots. Technometrics 29, 2 (1987), 127.

Digital Library

[5]

Capra, R. G. and Marchionini, G. The relation browser tool for faceted exploratory search. Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries, (2008), 420--420.

Digital Library

[6]

Card, S. K., Robertson, G. G., and Mackinlay, J. D. The information visualizer, an information workspace. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM (1991), 181--186.

Digital Library

[7]

Ceruzzi, P. and Grad, B. Guest Editors' Introduction: PC Software--Spreadsheets for Everyone. IEEE Annals of the History of Computing 29, 3 (2007), 4--5.

Digital Library

[8]

Chau, D. H., Kittur, A., Hong, J. I., and Faloutsos, C. Apolo: making sense of large network data by combining rich user interaction and machine learning. Proceedings of the 2011 annual conference on Human factors in computing systems, (2011), 167--176.

Digital Library

[9]

Choo, J., Lee, C., Clarkson, E., et al. VisIRR: Interactive Visual Information Retrieval and Recommendation for Large-scale Document Data. In submission to IEEE Transactions on Visualization and Computer Graphics (TVCG),.

[10]

Choo, J., Lee, H., Liu, Z., Stasko, J., and Park, H. An interactive visual testbed system for dimension reduction and clustering of large-scale high-dimensional data. Proceedings SPIE 8654, Visualization and Data Analysis, (2013).

[11]

Clarkson, E., Hurt, J., Zutty, J., Skeels, C., Parise, B., and Rohling, G. Supporting robust system analysis with the test matrix tool framework. Proceedings of the 2013 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, ACM (2013), 23--34.

Digital Library

[12]

Clarkson, E., Navathe, S. B., and Foley, J. D. Generalized formal models for faceted user interfaces. (2009), 125--134.

Digital Library

[13]

Draper, G. M. and Riesenfeld, R. F. Who votes for what? A visual query language for opinion data. IEEE transactions on visualization and computer graphics 14, 6 (2008), 1197--1204.

Digital Library

[14]

Fekete, J.-D., Wijk, J. J., Stasko, J. T., and North, C. The Value of Information Visualization. In A. Kerren, J. T. Stasko, J.-D. Fekete and C. North, eds., Information Visualization: Human-Centered Issues and Perspectives. Springer-Verlag, Berlin, Heidelberg, 2008, 1--18.

Digital Library

[15]

Huser, V., Narus, S. P., and Rocha, R. A. Evaluation of a flowchart-based EHR query system: a case study of RetroGuide. Journal of biomedical informatics 43, 1 (2010), 41--50.

Digital Library

[16]

Inselberg, A. The plane with parallel coordinates. The Visual Computer 1, 2 (1985), 69--91.

[17]

Kandel, S., Paepcke, A., Hellerstein, J., and Heer, J. Wrangler: interactive visual specification of data transformation scripts. Proceedings of the 2011 Conference on Human Factors in Computing Systems (CHI), ACM (2011), 3363--3372.

Digital Library

[18]

Kandel, S., Paepcke, A., Hellerstein, J. M., and Heer, J. Enterprise Data Analysis and Visualization: An Interview Study. IEEE Transactions on Visualization and Computer Graphics 18, 12 (2012), 2917--2926.

Digital Library

[19]

Kimball, R. A dimensional modeling manifesto. DBMS 10, 9 (1997), 58--70.

Digital Library

[20]

Krishnamurthy, R. and Zloof, M. RBE: Rendering by example. Proceedings of the Eleventh International Conference on Data Engineering, 1995, (1995), 288--297.

Digital Library

[21]

Liu, Z., Jiang, B., and Heer, J. imMens: Real-time Visual Querying of Big Data. Computer Graphics Forum 32, 3pt4 (2013), 421--430.

Digital Library

[22]

Marchionini, G. and Shneiderman, B. Finding Facts vs. Browsing Knowledge in Hypertext Systems. Computer 21, 1 (1988), 70--80.

Digital Library

[23]

Pileggi, H., Stolper, C. D., Boyle, J. M., and Stasko, J. T. SnapShot: Visualization to Propel Ice Hockey Analytics. IEEE Transactions on Visualization and Computer Graphics 18, 12 (2012), 2819--2828.

Digital Library

[24]

Ranganathan, S. Colon Classification. Madras Library Association, Madras, 1933.

[25]

Shneiderman, B. The eyes have it: A task by data type taxonomy for information visualizations. Visual Languages, 1996. Proceedings., IEEE Symposium on, (1996), 336--343.

Digital Library

[26]

Spence, R. and Tweedie, L. The Attribute Explorer: information synthesis via exploration. Interacting with Computers 11, 2 (1998), 137--146.

[27]

Stasko, J., Görg, C., and Liu, Z. Jigsaw: supporting investigative analysis through interactive visualization. Information Visualization 7, 2 (2008), 118--132.

Digital Library

[28]

Stolte, C., Tang, D., and Hanrahan, P. Polaris: A System for Query, Analysis, and Visualization of Multidimensional Relational Databases. IEEE Transactions on Visualization and Computer Graphics 8, 1 (2002), 52--65.

Digital Library

[29]

Thomas, J. J., Cowley, P. J., Kuchar, O., Nowell, L. T., Thompson, J., and Wong, P. C. Discovering knowledge through visual analysis. Journal of Universal Computer Science 7, 6 (2001), 517--529.

[30]

Wilson, M., Russell, A., and Smith, D. A. mSpace: improving information access to multimedia domains with multimodal exploratory search. Communications of the ACM 49, 4 (2006), 47--49.

Digital Library

[31]

Yee, K.-P., Swearingen, K., Li, K., and Hearst, M. Faceted metadata for image search and browsing. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM (2003), 401--408.

Digital Library

[32]

Yi, J. S., Kang, Y., Stasko, J. T., and Jacko, J. A. Toward a Deeper Understanding of the Role of Interaction in Information Visualization. IEEE Transactions on Visualization and Computer Graphics 13, 6 (2007), 1224--1231.

Digital Library

[33]

Zloof, M. M. Query-by-example - Operations on hierarchical data bases. Managing Requirements Knowledge, International Workshop on, IEEE Computer Society (1976), 845.

Cited By

Li RYin CYang SQian BZhang P(2020)DG-Viz: Deep Visual Analytics with Domain Knowledge Guided Recurrent Neural Networks on Electronic Medical Records (Preprint)Journal of Medical Internet Research10.2196/20645Online publication date: 29-May-2020
https://doi.org/10.2196/20645

Index Terms

Lytic: synthesizing high-dimensional algorithmic analysis with domain-agnostic, faceted visual analytics
1. Applied computing
  1. Operations research
    1. Decision analysis
2. Information systems
  1. Information systems applications
    1. Decision support systems

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Published In

cover image ACM Conferences

IDEA '13: Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

August 2013

104 pages

ISBN:9781450323291

DOI:10.1145/2501511

Editors:
Duen Horng Chau
Georgia Tech
,
Jilles Vreeken
University of Antwerp
,
Matthijs van Leeuwen
KU Leuven
,
Christos Faloutsos
Carnegie Mellon University

Copyright © 2013 ACM.

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Publication History

Published: 11 August 2013

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Division of Computing and Communication Foundations

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KDD' 13

Sponsor:

KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11, 2013

Illinois, Chicago

Acceptance Rates

IDEA '13 Paper Acceptance Rate 11 of 25 submissions, 44%;

Overall Acceptance Rate 11 of 25 submissions, 44%

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KDD '25

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The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

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Cited By

Li RYin CYang SQian BZhang P(2020)DG-Viz: Deep Visual Analytics with Domain Knowledge Guided Recurrent Neural Networks on Electronic Medical Records (Preprint)Journal of Medical Internet Research10.2196/20645Online publication date: 29-May-2020
https://doi.org/10.2196/20645

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