research-article

Vizdom: interactive analytics through pen and touch

Editors: Chen Li, Volker Markl Authors:

Tim KraskaAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 8, Issue 12

Pages 2024 - 2027

https://doi.org/10.14778/2824032.2824127

Published: 01 August 2015 Publication History

Get Access

Abstract

Machine learning (ML) and advanced statistics are important tools for drawing insights from large datasets. However, these techniques often require human intervention to steer computation towards meaningful results. In this demo, we present Vizdom, a new system for interactive analytics through pen and touch. Vizdom's frontend allows users to visually compose complex workflows of ML and statistics operators on an interactive whiteboard, and the back-end leverages recent advances in workflow compilation techniques to run these computations at interactive speeds. Additionally, we are exploring approximation techniques for quickly visualizing partial results that incrementally refine over time. This demo will show Vizdom's capabilities by allowing users to interactively build complex analytics workflows using real-world datasets.

References

[1]

Apache Hadoop. http://hadoop.apache.org.

Google Scholar

[2]

Apache Mahout. http://mahout.apache.org.

Google Scholar

[3]

MIMIC II Dataset. http://mimic.physionet.org.

Google Scholar

[4]

RapidMiner. http://rapidminer.com.

Google Scholar

[5]

D. Antenucci, M. Cafarella, M. Levenstein, C. Ré, and M. D. Shapiro. Using Social Media to Measure Labor Market Flows. Technical report, 2014.

Google Scholar

[6]

A. Crotty, A. Galakatos, K. Dursun, T. Kraska, U. Çetintemel, and S. B. Zdonik. Tupleware: "Big" Data, Big Analytics, Small Clusters. In CIDR, 2015.

Google Scholar

[7]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA Data Mining Software: An Update. SIGKDD Explorations, pages 10--18, 2009.

Crossref

Google Scholar

[8]

J. M. Hellerstein, C. Ré, F. Schoppmann, D. Z. Wang, E. Fratkin, A. Gorajek, K. S. Ng, C. Welton, X. Feng, K. Li, and A. Kumar. The MADlib Analytics Library or MAD Skills, the SQL. In VLDB, pages 1700--1711, 2012.

Crossref

Google Scholar

[9]

T. Kraska, A. Talwalkar, J. C. Duchi, R. Griffith, M. J. Franklin, and M. I. Jordan. MLbase: A Distributed Machine-learning System. In CIDR, 2013.

Google Scholar

[10]

Z. Liu and J. Heer. The Effects of Interactive Latency on Exploratory Visual Analysis. IEEE Trans. Vis. Comput. Graph., pages 2122--2131, 2014.

Google Scholar

[11]

E. R. Sparks, A. Talwalkar, V. Smith, J. Kottalam, X. Pan, J. E. Gonzalez, M. J. Franklin, M. I. Jordan, and T. Kraska. MLI: An API for Distributed Machine Learning. In ICDM, pages 1187--1192, 2013.

Google Scholar

[12]

M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauly, M. J. Franklin, S. Shenker, and I. Stoica. Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. In NSDI, pages 15--28, 2012.

Crossref

Google Scholar

[13]

E. Zgraggen, R. C. Zeleznik, and S. M. Drucker. PanoramicData: Data Analysis through Pen & Touch. IEEE Trans. Vis. Comput. Graph., pages 2112--2121, 2014.

Google Scholar

[14]

C. Zhang, A. Kumar, and C. Ré. Materialization Optimizations for Feature Selection Workloads. In SIGMOD, pages 265--276, 2014.

Crossref

Google Scholar

Cited By

View all

Zhang ZShao ZCrotty A(2024)DeepSketch: A Query Sketching Interface for Deep Time Series Similarity SearchProceedings of the VLDB Endowment10.14778/3685800.368587717:12(4369-4372)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685877
Li ZChockchowwat SFang HSahu RThakurdesai SPridaphatrakun KPark YBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Demonstration of ElasticNotebook: Migrating Live Computational Notebook StatesCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654752(540-543)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654752
Chang JDionysio CNargesian FBoehm MBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)PLUTUS: Understanding Data Distribution Tailoring for Machine LearningCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654745(528-531)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654745
Show More Cited By

Vizdom: interactive analytics through pen and touch
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

Recommendations

Browsing Zoomable Treemaps: Structure-Aware Multi-Scale Navigation Techniques

Treemaps provide an interesting solution for representing hierarchical data. However, most studies have mainly focused on layout algorithms and paid limited attention to the interaction with treemaps. This makes it difficult to explore large data sets ...
A Model and Framework for Visualization Exploration

Visualization exploration is the process of extracting insight from data via interaction with visual depictions of that data. Visualization exploration is more than presentation; the interaction with both the data and its depiction is as important as ...
Eikon: a tool for geometric coding of large multivariate datasets

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 8, Issue 12

Proceedings of the 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii

August 2015

728 pages

ISSN:2150-8097

Editors:
Chen Li
University of California, Irvine
,
Volker Markl
TU Berlin

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 August 2015

Published in PVLDB Volume 8, Issue 12

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

47
Total Citations
View Citations
293
Total Downloads

Downloads (Last 12 months)19
Downloads (Last 6 weeks)2

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhang ZShao ZCrotty A(2024)DeepSketch: A Query Sketching Interface for Deep Time Series Similarity SearchProceedings of the VLDB Endowment10.14778/3685800.368587717:12(4369-4372)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.14778/3685800.3685877
Li ZChockchowwat SFang HSahu RThakurdesai SPridaphatrakun KPark YBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Demonstration of ElasticNotebook: Migrating Live Computational Notebook StatesCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654752(540-543)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654752
Chang JDionysio CNargesian FBoehm MBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)PLUTUS: Understanding Data Distribution Tailoring for Machine LearningCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3654745(528-531)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3654745
Vaithilingam PGlassman EInala JWang C(2024)DynaVis: Dynamically Synthesized UI Widgets for Visualization EditingProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642639(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642639
Li ZGor PPrabhu RYu HMao YPark Y(2023)ElasticNotebook: Enabling Live Migration for Computational NotebooksProceedings of the VLDB Endowment10.14778/3626292.362629617:2(119-133)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.14778/3626292.3626296
Cavez VAppert CPietriga E(2023)Spreadsheets on Interactive Surfaces: Breaking through the Grid with the PenACM Transactions on Computer-Human Interaction10.1145/363009731:2(1-33)Online publication date: 25-Oct-2023
https://dl.acm.org/doi/10.1145/3630097
Li JSun YLei ZChen SAndrienko GAndrienko NChen W(2022)A hybrid prediction and search approach for flexible and efficient exploration of big dataJournal of Visualization10.1007/s12650-022-00887-y26:2(457-475)Online publication date: 6-Oct-2022
https://dl.acm.org/doi/10.1007/s12650-022-00887-y
Shang ZZgraggen EBuratti BEichmann PKarimeddiny NMeyer CRunnels WKraska T(2021)DavosProceedings of the VLDB Endowment10.14778/3476311.347637014:12(2893-2905)Online publication date: 28-Oct-2021
https://dl.acm.org/doi/10.14778/3476311.3476370
Manousis AShah HMilner HLi YZhang HSekar VLevin DMislove AAmann JLuckie M(2021)The shape of viewProceedings of the 21st ACM Internet Measurement Conference10.1145/3487552.3487819(245-260)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3487552.3487819
Da Col SCiucanu RSoare MBouarour NAmer-Yahia SDemartini GZuccon GCulpepper JHuang ZTong H(2021)DashBotProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481968(4696-4700)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481968
Show More Cited By

View Options

Login options

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

Cited By

Recommendations

Browsing Zoomable Treemaps: Structure-Aware Multi-Scale Navigation Techniques

A Model and Framework for Visualization Exploration

Eikon: a tool for geometric coding of large multivariate datasets

Comments

Published In

Publisher

Publication History

Qualifiers

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Recommendations

Browsing Zoomable Treemaps: Structure-Aware Multi-Scale Navigation Techniques

A Model and Framework for Visualization Exploration

Eikon: a tool for geometric coding of large multivariate datasets

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations