Campus Units

Electrical and Computer Engineering

Document Type

Conference Proceeding

Conference

42nd International Conference on Very Large Data Bases

Publication Version

Accepted Manuscript

Link to Published Version

https://doi.org/10.14778/3025111.3025124

Publication Date

11-2016

Journal or Book Title

Proceedings of the VLDB Endowment

Volume

10

Issue

4

First Page

433

Last Page

444

DOI

10.14778/3025111.3025124

Conference Title

VLDB Endowment

Conference Date

September 5-9, 2016

City

New Delhi, India

Abstract

Modern enterprises generate huge amounts of streaming data, for example, micro-blog feeds, financial data, network monitoring and industrial application monitoring. While Data Stream Management Systems have proven successful in providing support for real-time alerting, many applications, such as network monitoring for intrusion detection and real-time bidding, require complex analytics over historical and real-time data over the data streams. We present a new method to process one of the most fundamental analytical primitives, quantile queries, on the union of historical and streaming data. Our method combines an index on historical data with a memory-efficient sketch on streaming data to answer quantile queries with accuracy-resource tradeoffs that are significantly better than current solutions that are based solely on disk-resident indexes or solely on streaming algorithms.

Comments

This is a manuscript of a proceeding published as Singh, Sneha Aman, Divesh Srivastava, and Srikanta Tirthapura. "Estimating quantiles from the union of historical and streaming data." Proceedings of the VLDB Endowment 10, no. 4 (2016): 433-444. 10.14778/3025111.3025124. Posted with permission.

Rights

© ACM, 2016 This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the VLDB Endowment 10, no. 4 (2016): 433-444. https://doi.org/10.14778/3025111.3025124

Copyright Owner

ACM

Language

en

File Format

application/pdf

Published Version

Share

Article Location

 
COinS