research-article

A demonstration of the BigDAWG polystore system

Proceedings of the VLDB Endowment, Volume 8, Issue 12

Pages 1908 - 1911

https://doi.org/10.14778/2824032.2824098

Published: 01 August 2015 Publication History

Abstract

This paper presents BigDAWG, a reference implementation of a new architecture for "Big Data" applications. Such applications not only call for large-scale analytics, but also for real-time streaming support, smaller analytics at interactive speeds, data visualization, and cross-storage-system queries. Guided by the principle that "one size does not fit all", we build on top of a variety of storage engines, each designed for a specialized use case. To illustrate the promise of this approach, we demonstrate its effectiveness on a hospital application using data from an intensive care unit (ICU). This complex application serves the needs of doctors and researchers and provides real-time support for streams of patient data. It showcases novel approaches for querying across multiple storage engines, data visualization, and scalable real-time analytics.

References

[1]

Spark Streaming. https://spark.apache.org/docs/0.9.0/streaming-programming-guide.html#setting-the-right-batch-size.

Google Scholar

[2]

U. Cetintemel, J. Du, T. Kraska, S. Madden, D. Maier, J. Meehan, A. Pavlo, M. Stonebraker, E. Sutherland, and N. Tatbul. S-Store: A Streaming NewSQL System for Big Velocity Applications. PVLDB, 7(13), 2014.

Crossref

Google Scholar

[3]

S. Chandrasekaran, O. Cooper, A. Deshpande, M. J. Franklin, J. M. Hellerstein, W. Hong, S. Krishnamurthy, S. R. Madden, F. Reiss, and M. A. Shah. TelegraphCQ: continuous dataflow processing. In SIGMOD, pages 668--668, 2003.

Crossref

Google Scholar

[4]

J. Chen, D. J. DeWitt, F. Tian, and Y. Wang. NiagaraCQ: A scalable continuous query system for internet databases. In SIGMOD Record, volume 29, pages 379--390, 2000.

Crossref

Google Scholar

[5]

P. Cudré-Mauroux, H. Kimura, K.-T. Lim, J. Rogers, R. Simakov, E. Soroush, P. Velikhov, D. Wang, M. Balazinska, J. Becla, et al. A Demonstration of SciDB: A Science-Oriented DBMS. PVLDB, 2(2):1534--1537, 2009.

Crossref

Google Scholar

[6]

M. Franklin, A. Halevy, and D. Maier. From databases to dataspaces: a new abstraction for information management. Sigmod Record, 34(4):27--33, 2005.

Crossref

Google Scholar

[7]

D. Halperin, V. Teixeira de Almeida, L. L. Choo, S. Chu, P. Koutris, D. Moritz, J. Ortiz, V. Ruamviboonsuk, J. Wang, A. Whitaker, et al. Demonstration of the Myria big data management service. In SIGMOD. ACM, 2014.

Crossref

Google Scholar

[8]

R. Kallman, H. Kimura, J. Natkins, A. Pavlo, A. Rasin, S. B. Zdonik, E. P. C. Jones, S. Madden, M. Stonebraker, Y. Zhang, J. Hugg, and D. J. Abadi. H-store: A high-performance, distributed main memory transaction processing system. In PVLDB, volume 1, 2008.

Crossref

Google Scholar

[9]

S. Kandel, A. Paepcke, J. M. Hellerstein, and J. Heer. Enterprise data analysis and visualization: An interview study. Visualization and Computer Graphics, IEEE Transactions on, 18(12):2917--2926, 2012.

Crossref

Google Scholar

[10]

J. Kepner, W. Arcand, W. Bergeron, N. Bliss, R. Bond, C. Byun, G. Condon, K. Gregson, M. Hubbell, and J. Kurz. Dynamic distributed dimensional data model (d4m) database and computation system. In ICASSP. IEEE, 2012.

Google Scholar

[11]

H. Lim, Y. Han, and S. Babu. How to fit when no one size fits. In CIDR, volume 4, page 35, 2013.

Google Scholar

[12]

A. Nandi and H. Jagadish. Guided interaction: Rethinking the query-result paradigm. PVLDB, 4(12):1466--1469, 2011.

Google Scholar

[13]

M. Saeed, M. Villarroel, A. T. Reisner, G. Clifford, L.-W. Lehman, G. Moody, T. Heldt, T. H. Kyaw, B. Moody, and R. G. Mark. Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): A public-access intensive care unit database. Critical Care Medicine, 39: 952--960, 2011.

Google Scholar

[14]

M. Stonebraker and U. Cetintemel. "One Size Fits All": An Idea Whose time has come and gone. In ICDE, pages 2--11, 2005.

Crossref

Google Scholar

Cited By

View all

Koupil PCrha DHolubová I(2025)A universal approach for simplified redundancy-aware cross-model queryingInformation Systems10.1016/j.is.2024.102456127:COnline publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1016/j.is.2024.102456
Gu ZCorcoglioniti FLanti DMosca AXiao GXiong JCalvanese D(2024)A systematic overview of data federation systemsSemantic Web10.3233/SW-22320115:1(107-165)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-223201
Zheng XKumar AGupta ASerra ESpezzano F(2024)Generating Cross-model Analytics Workloads Using LLMsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679932(4303-4307)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679932
Show More Cited By

A demonstration of the BigDAWG polystore system
1. Information systems
  1. Data management systems

Recommendations

The BigDAWG Polystore System

This paper presents a new view of federated databases to address the growing need for managing information that spans multiple data models. This trend is fueled by the proliferation of storage engines and query languages based on the observation that '...
A demonstration of AQWA: adaptive query-workload-aware partitioning of big spatial data
Proceedings of the 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii

The ubiquity of location-aware devices, e.g., smartphones and GPS devices, has led to a plethora of location-based services in which huge amounts of geotagged information need to be efficiently processed by large-scale computing clusters. This demo ...
Demonstration of the Myria big data management service
SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

In this demonstration, we will showcase Myria, our novel cloud service for big data management and analytics designed to improve productivity. Myria's goal is for users to simply upload their data and for the system to help them be self-sufficient data ...

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

69
Total Citations
View Citations
291
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Koupil PCrha DHolubová I(2025)A universal approach for simplified redundancy-aware cross-model queryingInformation Systems10.1016/j.is.2024.102456127:COnline publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1016/j.is.2024.102456
Gu ZCorcoglioniti FLanti DMosca AXiao GXiong JCalvanese D(2024)A systematic overview of data federation systemsSemantic Web10.3233/SW-22320115:1(107-165)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-223201
Zheng XKumar AGupta ASerra ESpezzano F(2024)Generating Cross-model Analytics Workloads Using LLMsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679932(4303-4307)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679932
Kraska TLi TMadden SMarkakis MNgom AWu ZYu G(2023)Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data MeshesProceedings of the VLDB Endowment10.14778/3611479.361152616:11(3293-3301)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.14778/3611479.3611526
Giatrakos NAlevizos EDeligiannakis AKlinkenberg RArtikis AFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Proactive Streaming Analytics at Scale: A Journey from the State-of-the-art to a Production PlatformProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615293(5204-5207)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615293
Guo QZhang CZhang SLu J(2023)Multi-model query languages: taming the variety of big dataDistributed and Parallel Databases10.1007/s10619-023-07433-142:1(31-71)Online publication date: 31-May-2023
https://dl.acm.org/doi/10.1007/s10619-023-07433-1
Poudel MSarode RWatanobe YMozgovoy MBhalla S(2022)Processing Analytical Queries over Polystore System for a Large Astronomy Data RepositoryApplied Sciences10.3390/app1205266312:5(2663)Online publication date: 4-Mar-2022
https://doi.org/10.3390/app12052663
Holubova IKoupil PLu J(2022)Self-Adapting Design and Maintenance of Multi-Model DatabasesProceedings of the 26th International Database Engineered Applications Symposium10.1145/3548785.3548810(9-15)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1145/3548785.3548810
Zhang JHuang KWang TLv KIves ZBonifati AEl Abbadi A(2022)Skeena: Efficient and Consistent Cross-Engine TransactionsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3526171(34-48)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3526171
Koutroumanis NDoulkeridis CVlachou A(2022)Tearing Down the Tower of Babel: Unified and Efficient Spatio-temporal Queries for NoSQL Stores2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00024(19-28)Online publication date: Jun-2022
https://doi.org/10.1109/MDM55031.2022.00024
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.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Recommendations

The BigDAWG Polystore System

A demonstration of AQWA: adaptive query-workload-aware partitioning of big spatial data

Demonstration of the Myria big data management service

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

Share

Share this Publication link

Share on social media

Affiliations