skip to main content
10.1145/3183713.3193545acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
research-article

DeepEye: Creating Good Data Visualizations by Keyword Search

Published:27 May 2018Publication History

ABSTRACT

Creating good visualizations for ordinary users is hard, even with the help of the state-of-the-art interactive data visualization tools, such as Tableau, Qlik, because they require the users to understand the data and visualizations very well. DeepEye is an innovative visualization system that aims at helping everyone create good visualizations simply like a Google search. Given a dataset and a keyword query, DeepEye understands the query intent, generates and ranks good visualizations. The user can pick the one she likes and do a further faceted navigation to easily navigate the candidate visualizations. In this demonstration, the attendees will have the opportunity to experience the following features: (1) visualization recommendation -- Our system can automatically recommends meaningful visualizations by learning from existing known datasets and good visualizations; (2) keyword search -- The attendee can pose text queries for specifying what visualizations she wants (e.g., trends) without specifying how to generate them; (3) faceted navigation -- One can further refine the results by a click-based faceted navigation to find other relevant and interesting visualizations.

References

  1. C. Binnig, L. D. Stefani, T. Kraska, E. Upfal, E. Zgraggen, and Z. Zhao. Toward sustainable insights, or why polygamy is bad for you. In CIDR, 2017.Google ScholarGoogle Scholar
  2. M. Bostock, V. Ogievetsky, and J. Heer. D(^3) data-driven documents. IEEE Trans. Vis. Comput. Graph., 17(12):2301--2309, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. C. J. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. N. Hullender. Learning to rank using gradient descent. In ICML, pages 89--96, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Fan, G. Li, and L. Zhou. Interactive sql query suggestion: Making databases user-friendly. In ICDE, pages 351--362, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Y. Luo, X. Qin, N. Tang, and G. Li. DeepEye: Towards Automatic Data Visualization. In ICDE, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  6. X. Qin, Y. Luo, N. Tang, and G. Li. DeepEye: Visualizing Your Data by Keyword Search {Visionary Paper}. In EDBT, 2018.Google ScholarGoogle Scholar
  7. A. Satyanarayan, D. Moritz, K. Wongsuphasawat, and J. Heer. Vega-lite: A grammar of interactive graphics. IEEE Trans. Vis. Comput. Graph., 23(1):341--350, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. T. Siddiqui, A. Kim, J. Lee, K. Karahalios, and A. G. Parameswaran. Effortless data exploration with zenvisage: An expressive and interactive visual analytics system. PVLDB, 10(4):457--468, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. M. Vartak, S. Huang, T. Siddiqui, S. Madden, and A. G. Parameswaran. Towards visualization recommendation systems. SIGMOD Record, 45(4):34--39, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. M. Vartak, S. Rahman, S. Madden, A. G. Parameswaran, and N. Polyzotis. SEEDB: efficient data-driven visualization recommendations to support visual analytics. PVLDB, 8(13):2182--2193, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. DeepEye: Creating Good Data Visualizations by Keyword Search

              Recommendations

              Comments

              Login options

              Check if you have access through your login credentials or your institution to get full access on this article.

              Sign in
              • Published in

                cover image ACM Conferences
                SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data
                May 2018
                1874 pages
                ISBN:9781450347037
                DOI:10.1145/3183713

                Copyright © 2018 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 ACM 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: 27 May 2018

                Permissions

                Request permissions about this article.

                Request Permissions

                Check for updates

                Qualifiers

                • research-article

                Acceptance Rates

                SIGMOD '18 Paper Acceptance Rate90of461submissions,20%Overall Acceptance Rate785of4,003submissions,20%

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader