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The case for interactive data exploration accelerators (IDEAs)

Published:26 June 2016Publication History

ABSTRACT

Enabling interactive visualization over new datasets at "human speed" is key to democratizing data science and maximizing human productivity. In this work, we first argue why existing analytics infrastructures do not support interactive data exploration and then outline the challenges and opportunities of building a system specifically designed for interactive data exploration. Finally, we present an Interactive Data Exploration Accelerator (IDEA), a new type of system for interactive data exploration that is specifically designed to integrate with existing data management landscapes and allow users to explore their data instantly without expensive data preparation costs.

References

  1. S. Agarwal et al. BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data. In EuroSys, pages 29--42, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Apache Flink. http://flink.apache.org/.Google ScholarGoogle Scholar
  3. C. Binnig et al. The End of Slow Networks: It's Time for a Redesign. In VLDB, pages 528--539, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. C. Böhm, S. Berchtold, H. Kriegel, and U. Michel. Multidimensional Index Structures in Relational Databases. J. Intell. Inf. Syst., pages 51--70, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. Chaudhuri, G. Das, and V. R. Narasayya. Optimized Stratified Sampling for Approximate Query Processing. TODS, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. Crotty et al. Vizdom Demo Video. https://vimeo.com/139165014.Google ScholarGoogle Scholar
  7. A. Crotty et al. An Architecture for Compiling UDF-centric Workflows. In VLDB, pages 1466--1477, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. Crotty et al. Vizdom: Interactive Analytics through Pen and Touch. In VLDB, pages 2024--2035, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. G. Cumming and S. Finch. Inference by Eye: Confidence Intervals and How to Read Pictures of Data. American Psychologist, pages 170--180, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  10. M. El-Hindi, Z. Zhao, C. Binnig, and T. Kraska. VisTrees: Fast Indexes for Interactive Data Exploration. In HILDA, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. J. M. Hellerstein, P. J. Haas, and H. J. Wang. Online Aggregation. In SIGMOD, pages 171--182, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. S. Idreos, M. L. Kersten, and S. Manegold. Database Cracking. In CIDR, pages 68--78, 2007.Google ScholarGoogle Scholar
  13. M. Lichman. UCI Machine Learning Repository, 2013.Google ScholarGoogle Scholar
  14. Z. Liu and J. Heer. The Effects of Interactive Latency on Exploratory Visual Analysis. TVCG, pages 2122--2131, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  15. Z. Liu, B. Jiang, and J. Heer. imMens: Real-time Visual Querying of Big Data. In EuroVis, pages 421--430, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. F. Olken and D. Rotem. Random Sampling from Relational Databases. In VLDB, pages 160--169, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. N. Pansare, V. R. Borkar, C. Jermaine, and T. Condie. Online Aggregation for Large MapReduce Jobs. In VLDB, pages 1135--1145, 2011.Google ScholarGoogle Scholar
  18. The Apache Software Foundation. Hadoop. http://hadoop.apache.org.Google ScholarGoogle Scholar
  19. M. Zaharia, T. Das, H. Li, T. Hunter, S. Shenker, and I. Stoica. Discretized Streams: Fault-tolerant Streaming Computation at Scale. In SOSP, pages 423--438, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. M. Zaharia et al. Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-memory Cluster Computing. In NSDI, pages 15--28, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  • Published in

    cover image ACM Other conferences
    HILDA '16: Proceedings of the Workshop on Human-In-the-Loop Data Analytics
    June 2016
    93 pages
    ISBN:9781450342070
    DOI:10.1145/2939502

    Copyright © 2016 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 26 June 2016

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    HILDA '16 Paper Acceptance Rate16of32submissions,50%Overall Acceptance Rate28of56submissions,50%

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