A Task Completion Engine to Enhance Search Session Support for Air Traffic Work Tasks

Moshfeghi, Y., Rothfeld, R., Azzopardi, L. and Triantafillou, P. (2017) A Task Completion Engine to Enhance Search Session Support for Air Traffic Work Tasks. In: 39th European Conference on Information Retrieval, Aberdeen, Scotland, 8-13 April 2017, pp. 278-290. (doi:10.1007/978-3-319-56608-5_22)

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Abstract

Providing support for users during their search sessions has been hailed as a major challenge in interactive information retrieval (IIR). Providing such support requires considering the context of the search and facilitating the work task at hand. In this paper, we consider the work tasks associated with air traffic analysts, who perform numerous searches using a multifaceted search interface in order to acquire business intelligence regarding particular events and situations. In particular, we develop a novel task completion engine and seamlessly incorporated it within a current air traffic search system to facilitate the comparison of information objects found. In a study with 24 participants, we found that they completed the complex work task faster using the comparison feature, but for simple work tasks, participants were slower. However, participants reported (statistically) significantly higher satisfaction and had (statistically) significantly higher accuracy using the search system equipped with task completion engine. These findings help to steer systems to provide a better support to users in their search process.

Item Type:Conference Proceedings
Additional Information:Published in Lecture Notes in Computer Science, v. 10193, pp. 278-290
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Azzopardi, Dr Leif and Triantafillou, Professor Peter and Moshfeghi, Dr Yashar
Authors: Moshfeghi, Y., Rothfeld, R., Azzopardi, L., and Triantafillou, P.
College/School:College of Science and Engineering > School of Computing Science
ISSN:0302-9743
Published Online:08 April 2017
Copyright Holders:Copyright © 2017 Springer International Publishing AG
First Published:First published in Lecture Notes in Computer Science 10193:278-290
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
651922Urban Big Data Research CentrePiyushimita ThakuriahEconomic & Social Research Council (ESRC)ES/L011921/1SPS - URBAN STUDIES