demonstration

Stat!: an interactive analytics environment for big data

Authors:

Mike Barnett,

Badrish Chandramouli,

John PlattAuthors Info & Claims

SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

Pages 1013 - 1016

https://doi.org/10.1145/2463676.2463683

Published: 22 June 2013 Publication History

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Abstract

Exploratory analysis on big data requires us to rethink data management across the entire stack -- from the underlying data processing techniques to the user experience. We demonstrate Stat! -- a visualization and analytics environment that allows users to rapidly experiment with exploratory queries over big data. Data scientists can use Stat! to quickly refine to the correct query, while getting immediate feedback after processing a fraction of the data. Stat! can work with multiple processing engines in the backend; in this demo, we use Stat! with the Microsoft StreamInsight streaming engine. StreamInsight is used to generate incremental early results to queries and refine these results as more data is processed. Stat! allows data scientists to explore data, dynamically compose multiple queries to generate streams of partial results, and display partial results in both textual and visual form.

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Cited By

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Ahmed SIslam MRajan H(2022)Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series DataACM/IMS Transactions on Data Science10.1145/35119182:4(1-25)Online publication date: 30-Mar-2022
https://dl.acm.org/doi/10.1145/3511918
Bruhwiler KBuddhika TPallickara SPallickara S(2020)Iris: Amortized, Resource Efficient Visualizations of Voluminous Spatiotemporal Datasets2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)10.1109/BDCAT50828.2020.00003(47-56)Online publication date: Dec-2020
https://doi.org/10.1109/BDCAT50828.2020.00003
Budiu MGopalan PSuresh LWieder UKruiger HAguilera M(2019)HillviewProceedings of the VLDB Endowment10.14778/3342263.334227912:11(1442-1457)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.14778/3342263.3342279
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Index Terms

Stat!: an interactive analytics environment for big data
1. Information systems
  1. Data management systems
    1. Database management system engines

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Information

Published In

SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

June 2013

1322 pages

ISBN:9781450320375

DOI:10.1145/2463676

General Chairs:
Kenneth Ross
Columbia University
,
Divesh Srivastava
AT&T Research
,
Program Chair:
Dimitris Papadias
HKUST

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2013

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Demonstration

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SIGMOD/PODS'13

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SIGMOD

SIGMOD/PODS'13: International Conference on Management of Data

June 22 - 27, 2013

New York, New York, USA

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SIGMOD '13 Paper Acceptance Rate 76 of 372 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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Ahmed SIslam MRajan H(2022)Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series DataACM/IMS Transactions on Data Science10.1145/35119182:4(1-25)Online publication date: 30-Mar-2022
https://dl.acm.org/doi/10.1145/3511918
Bruhwiler KBuddhika TPallickara SPallickara S(2020)Iris: Amortized, Resource Efficient Visualizations of Voluminous Spatiotemporal Datasets2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)10.1109/BDCAT50828.2020.00003(47-56)Online publication date: Dec-2020
https://doi.org/10.1109/BDCAT50828.2020.00003
Budiu MGopalan PSuresh LWieder UKruiger HAguilera M(2019)HillviewProceedings of the VLDB Endowment10.14778/3342263.334227912:11(1442-1457)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.14778/3342263.3342279
Kamat NNandi A(2018)A Session-Based Approach to Fast-But-Approximate Interactive Data Cube ExplorationACM Transactions on Knowledge Discovery from Data10.1145/307064812:1(1-26)Online publication date: 13-Feb-2018
https://dl.acm.org/doi/10.1145/3070648
Choi HGim JSeo YBaik D(2018)VPL-Based Big Data Analysis System: UDASIEEE Access10.1109/ACCESS.2018.28578456(40883-40897)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2857845
Mani MFei SDesai BHong J(2017)Effective Big Data VisualizationProceedings of the 21st International Database Engineering & Applications Symposium10.1145/3105831.3105857(298-303)Online publication date: 12-Jul-2017
https://dl.acm.org/doi/10.1145/3105831.3105857
Dayarathna MMalshan MPerera SJayasinghe M(2017)Scalable Complex Event Processing on a NotebookProceedings of the 11th ACM International Conference on Distributed and Event-based Systems10.1145/3093742.3095093(327-330)Online publication date: 8-Jun-2017
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Wang ZFerreira NWei YBhaskar AScheidegger C(2017)Gaussian CubesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2016.259869423:1(681-690)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TVCG.2016.2598694
Budiu MIsaacs RMurray DPlotkin GBarham PAl-Kiswany SBoshmaf YLuo QAndoni ATelea AGobbetti EBethel W(2016)Interacting with large distributed datasets using sketchProceedings of the 16th Eurographics Symposium on Parallel Graphics and Visualization10.5555/3061436.3061442(31-43)Online publication date: 6-Jun-2016
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Khalifa SElshater YSundaravarathan KBhat AMartin PImam FRope DMcroberts MStatchuk C(2016)The Six Pillars for Building Big Data Analytics EcosystemsACM Computing Surveys10.1145/296314349:2(1-36)Online publication date: 2-Aug-2016
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