Next Article in Journal
Applying Length-Dependent Stochastic Context-Free Grammars to RNA Secondary Structure Prediction
Previous Article in Journal
Lempel–Ziv Data Compression on Parallel and Distributed Systems
Article Menu

Export Article

Open AccessArticle
Algorithms 2011, 4(3), 200-222; doi:10.3390/a4030200

Approximating Frequent Items in Asynchronous Data Stream over a Sliding Window

1
Department of Computer Science, University of Hong Kong, Pokfulam, Hong Kong, China
2
MADALGO (Center for Massive Data Algorithmics, a Center of the Danish National Research Foundation), Department of Computer Science, Aarhus University, Aarhus C DK-8000, Denmark
*
Author to whom correspondence should be addressed.
Received: 23 June 2011 / Revised: 23 June 2011 / Accepted: 10 September 2011 / Published: 22 September 2011
View Full-Text   |   Download PDF [405 KB, uploaded 22 September 2011]   |  

Abstract

In an asynchronous data stream, the data items may be out of order with respect to their original timestamps. This paper studies the space complexity required by a data structure to maintain such a data stream so that it can approximate the set of frequent items over a sliding time window with sufficient accuracy. Prior to our work, the best solution is given by Cormode et al. [1], who gave an O (1/ε log W log (εB/ log W) min {log W, 1/εlog |U|)- space data structure that can approximate the frequent items within an ε error bound, where W and B are parameters of the sliding window, and U is the set of all possible item names. We gave a more space-efficient data structure that only requires O (1/ε log W log (εB/ logWlog log W) space.
Keywords: asynchronous data streams; frequent items; sliding window; space complexity asynchronous data streams; frequent items; sliding window; space complexity
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ting, H.-F.; Lee, L.-K.; Chan, H.-L.; Lam, T.-W. Approximating Frequent Items in Asynchronous Data Stream over a Sliding Window. Algorithms 2011, 4, 200-222.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top