Intelligent Decision Support using Pattern Matching

Victoria Jane Hodge; Tom Jackson; Jim Austin

doi:10.5220/0004473000440054

Intelligent Decision Support using Pattern Matching

Victoria Jane Hodge, Tom Jackson, Jim Austin

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The aim of our work is to develop Intelligent Decision Support (IDS) tools and techniques to convert traffic data into intelligence to assist network managers, operators and to aid the travelling public. The IDS system detects traffic problems, identifies the likely cause and recommends suitable interventions which are most likely to mitigate congestion of that traffic problem. In this paper, we propose to extend the existing tools to include dynamic hierarchical and distributed processing; algorithm optimisation using natural computation techniques; and, using a meta-learner to short-circuit the optimisation by learning the best settings for specific data set characteristics and using these settings to initialise the GA.

Original language	English
Title of host publication	Proceedings of the 1st International Workshop on Future Internet Applications for Traffic Surveillance and Management
Publisher	SciTePress
ISBN (Print)	978-989-8425-87-4
DOIs	https://doi.org/10.5220/0004473000440054
Publication status	Published - 26 Oct 2011
Event	Proceedings of the 1st International Workshop on Future Internet Applications for Traffic Surveillance and Management (FIATS-M 2011) - Sofia, Bulgaria Duration: 26 Oct 2011 → …

Conference

Conference	Proceedings of the 1st International Workshop on Future Internet Applications for Traffic Surveillance and Management (FIATS-M 2011)
Country/Territory	Bulgaria
City	Sofia
Period	26/10/11 → …

Access to Document

10.5220/0004473000440054

Cite this

@inproceedings{033b7a4c79464c5e86553f372212e074,

title = "Intelligent Decision Support using Pattern Matching",

abstract = "The aim of our work is to develop Intelligent Decision Support (IDS) tools and techniques to convert traffic data into intelligence to assist network managers, operators and to aid the travelling public. The IDS system detects traffic problems, identifies the likely cause and recommends suitable interventions which are most likely to mitigate congestion of that traffic problem. In this paper, we propose to extend the existing tools to include dynamic hierarchical and distributed processing; algorithm optimisation using natural computation techniques; and, using a meta-learner to short-circuit the optimisation by learning the best settings for specific data set characteristics and using these settings to initialise the GA.",

author = "Hodge, {Victoria Jane} and Tom Jackson and Jim Austin",

year = "2011",

month = oct,

day = "26",

doi = "10.5220/0004473000440054",

language = "English",

isbn = "978-989-8425-87-4",

booktitle = "Proceedings of the 1st International Workshop on Future Internet Applications for Traffic Surveillance and Management",

publisher = "SciTePress",

note = "Proceedings of the 1st International Workshop on Future Internet Applications for Traffic Surveillance and Management (FIATS-M 2011) ; Conference date: 26-10-2011",

}

Hodge, VJ , Jackson, T & Austin, J 2011, Intelligent Decision Support using Pattern Matching. in Proceedings of the 1st International Workshop on Future Internet Applications for Traffic Surveillance and Management. SciTePress, Proceedings of the 1st International Workshop on Future Internet Applications for Traffic Surveillance and Management (FIATS-M 2011), Sofia, Bulgaria, 26/10/11. https://doi.org/10.5220/0004473000440054

TY - GEN

T1 - Intelligent Decision Support using Pattern Matching

AU - Hodge, Victoria Jane

AU - Jackson, Tom

AU - Austin, Jim

PY - 2011/10/26

Y1 - 2011/10/26

N2 - The aim of our work is to develop Intelligent Decision Support (IDS) tools and techniques to convert traffic data into intelligence to assist network managers, operators and to aid the travelling public. The IDS system detects traffic problems, identifies the likely cause and recommends suitable interventions which are most likely to mitigate congestion of that traffic problem. In this paper, we propose to extend the existing tools to include dynamic hierarchical and distributed processing; algorithm optimisation using natural computation techniques; and, using a meta-learner to short-circuit the optimisation by learning the best settings for specific data set characteristics and using these settings to initialise the GA.

AB - The aim of our work is to develop Intelligent Decision Support (IDS) tools and techniques to convert traffic data into intelligence to assist network managers, operators and to aid the travelling public. The IDS system detects traffic problems, identifies the likely cause and recommends suitable interventions which are most likely to mitigate congestion of that traffic problem. In this paper, we propose to extend the existing tools to include dynamic hierarchical and distributed processing; algorithm optimisation using natural computation techniques; and, using a meta-learner to short-circuit the optimisation by learning the best settings for specific data set characteristics and using these settings to initialise the GA.

U2 - 10.5220/0004473000440054

DO - 10.5220/0004473000440054

M3 - Conference contribution

SN - 978-989-8425-87-4

BT - Proceedings of the 1st International Workshop on Future Internet Applications for Traffic Surveillance and Management

PB - SciTePress

T2 - Proceedings of the 1st International Workshop on Future Internet Applications for Traffic Surveillance and Management (FIATS-M 2011)

Y2 - 26 October 2011

ER -