Knowledge-based analytics for massively distributed networks with noisy data

Xin W. Chen

International Journal of Production Research, 2018, vol. 56, issue 8, 2841-2854

Abstract: This article develops and implements an improving search algorithm that effectively and efficiently identifies pathways of interest using knowledge-based analytics for massively distributed networks with noisy data. The method developed in this article fundamentally changes how critical information is extracted from large data-sets. Many methods have been developed in the past to identify structures in large graphs. Most of these methods are computationally inefficient for large graphs and their outcome depends on the graph metrics and statistical measures. There has been limited research on using optimisation techniques for data mining in large networks with noisy data. The algorithm developed in this article converges to the optimal solution by traversing the interior of a feasible region. Experiments show that it identifies a pathway of interest from a network of 160,000 components in 10 hours using parallel computing. Future work will include customisation and implementation of the method to other large networks in a variety of applications.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2017.1408972 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:56:y:2018:i:8:p:2841-2854

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2017.1408972

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().