Campus Units

Computer Science, Electrical and Computer Engineering

Document Type

Conference Proceeding

Conference

44th International Conference on Very Large Data Bases 2018

Publication Version

Published Version

Publication Date

2018

Journal or Book Title

Proceedings of the VLDB Endowment

Volume

11

Issue

12

First Page

1974

Last Page

1977

DOI

10.14778/3229863.3236238

Conference Title

44th International Conference on Very Large Data Bases 2018

Conference Date

August 27-31, 2018

City

Rio de Janeiro, Brazil

Abstract

A core requirement of database engine testing is the ability to create synthetic versions of the customer’s data warehouse at the vendor site. Prior work on synthetic data regeneration suffers from critical limitations with regard to (a) scaling to large data volumes, (b) handling complex query workloads, and (c) producing data on demand. In this demo, we present HYDRA, a workload-dependent dynamic data regenerator, that materially addresses these limitations. It introduces the concept of dynamic regeneration by constructing a minuscule memory-resident database summary that can on-the-fly regenerate databases of arbitrary size during query execution. Further, since the data is generated in memory, the velocity of generation can be closely regulated. Finally, to complement dynamic regeneration, Hydra also ensures that the process of summary construction is data-scale-free.

Comments

This proceeding is published as Sanghi, Anupam, Raghav Sood, Dharmendra Singh, Jayant R. Haritsa, and Srikanta Tirthapura. "HYDRA: A Dynamic Big Data Regenerator." Proceedings of the VLDB Endowment 11, no. 12 (2018): 1974. doi: 10.14778/3229863.3236238. Posted with permission.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Copyright Owner

VLDB Endowment

Language

en

File Format

application/pdf

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