A Histogram Based Analytical Approximate Query Processing for Massive Data

Yi Jun Wang; Han Hu Wang; Hui Li

doi:10.4028/www.scientific.net/AMM.411-414.362

Paper Titles

Behavior-Based Data Item Correlation in the Dataspace
p.345

A Survey of Scientific Metadata Schema
p.349

A Biorepository Management Scheme for Data Integration
p.353

A Spatial Database Management System for Urban and Rural Planning
p.357

A Histogram Based Analytical Approximate Query Processing for Massive Data
p.362

KSR-Tree: A Clustering Based High-Dimensional Indexing Approach
p.366

Design and Implementation of Real-Time Database in Embedded Configuration Software
p.370

The Brief Introduction of Database Technology Application in Irrigation Information Management System
p.377

Metadata Update Strategy with High Reliability
p.382

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 411-414A Histogram Based Analytical Approximate Query...

A Histogram Based Analytical Approximate Query Processing for Massive Data

Abstract:

In this paper, we study the characteristics of analytical query processing and proposed a histogram based approximate method for query processing over massive data. We implemented this approach into Hive system and evaluate it with Hive and BlinkDB cluster, the experimental results verified that our method is significantly fast than these existing techniques.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 411-414)

Pages:

362-365

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.411-414.362

Citation:

Cite this paper

Online since:

September 2013

Authors:

Yi Jun Wang, Han Hu Wang, Hui Li*

Keywords:

Approximate Query, BlinkDB, Hadoop, Histogram, Massive Data

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Sameer Agarwal, Aurojit Panda, Barzan Mozafari, Anand P. Iyer, Samuel Madden, Ion Stoica. Blink and It's Done: Interactive Queries on Very Large Data. In PVLDB 5(12): 1902-1905, (2012).

DOI: 10.14778/2367502.2367533

Google Scholar

[2] Sameer Agarwal, Barzan Mozafari, Aurojit Panda, etc. BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data. To Appear in ACM EuroSys (2013).

DOI: 10.1145/2465351.2465355

Google Scholar

[3] Feng Kaiping, Zhang Hua, Feng Chaoying, Chen Heng, in: Application of Histogram Method on Cost Estimate in Query Optimization. Computer & Digital Engineering, (2010).

Google Scholar

[4] S. Acharya, P. B. Gibbons, and V. Poosala. Congressional samples for approximate answering of group-by queries. In ACM SIGMOD, May (2000).

DOI: 10.1145/335191.335450

Google Scholar

[5] S. Agarwal, S. Kandula, N. Bruno, etc. Re-optimizing Data Parallel Computing. In NSDI, (2012).

Google Scholar

[6] G. Cormode. Sketch techniques for massive data. In Synopses for Massive Data: Samples, Histograms, Wavelets and Sketches, (2011).

DOI: 10.1561/9781601985170

Google Scholar