Adapting workflows to intelligent environments

Melanie Hartmann; Marcus Ständer; Victoria Uren

doi:10.1109/IE.2011.37

Adapting workflows to intelligent environments

Melanie Hartmann, Marcus Ständer, Victoria Uren

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Intelligent environments aim at supporting the user in executing her everyday tasks, e.g. by guiding her through a maintenance or cooking procedure. This requires a machine processable representation of the tasks for which workflows have proven an efficient means. The increasing number of available sensors in intelligent environments can facilitate the execution of workflows. The sensors can help to recognize when a user has finished a step in the workflow and thus to automatically proceed to the next step. This can heavily reduce the amount of required user interaction. However, manually specifying the conditions for triggering the next step in a workflow is very cumbersome and almost impossible for environments which are not known at design time. In this paper, we present a novel approach for learning and adapting these conditions from observation. We show that the learned conditions can even outperform the quality as conditions manually specified by workflow experts. Thus, the presented approach is very well suited for automatically adapting workflows in intelligent environments and can in that way increase the efficiency of the workflow execution.

Original language	English
Title of host publication	2011 7th International Conference on Intelligent Environments (IE)
Publisher	IEEE
Pages	9-16
Number of pages	8
ISBN (Electronic)	978-0-7695-4452-6
ISBN (Print)	978-1-4577-0830-5
DOIs	https://doi.org/10.1109/IE.2011.37
Publication status	Published - 2011
Event	7th International Conference on Intelligent Environments - Nottingham, United Kingdom Duration: 25 Jul 2011 → 28 Jul 2011

Conference

Conference	7th International Conference on Intelligent Environments
Abbreviated title	IE 2011
Country/Territory	United Kingdom
City	Nottingham
Period	25/07/11 → 28/07/11

Bibliographical note

© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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10.1109/IE.2011.37

Adapting workflows to intelligent environments
© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 318 KB

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6063359

Cite this

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title = "Adapting workflows to intelligent environments",

abstract = "Intelligent environments aim at supporting the user in executing her everyday tasks, e.g. by guiding her through a maintenance or cooking procedure. This requires a machine processable representation of the tasks for which workflows have proven an efficient means. The increasing number of available sensors in intelligent environments can facilitate the execution of workflows. The sensors can help to recognize when a user has finished a step in the workflow and thus to automatically proceed to the next step. This can heavily reduce the amount of required user interaction. However, manually specifying the conditions for triggering the next step in a workflow is very cumbersome and almost impossible for environments which are not known at design time. In this paper, we present a novel approach for learning and adapting these conditions from observation. We show that the learned conditions can even outperform the quality as conditions manually specified by workflow experts. Thus, the presented approach is very well suited for automatically adapting workflows in intelligent environments and can in that way increase the efficiency of the workflow execution.",

author = "Melanie Hartmann and Marcus St{\"a}nder and Victoria Uren",

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AU - Ständer, Marcus

AU - Uren, Victoria

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N2 - Intelligent environments aim at supporting the user in executing her everyday tasks, e.g. by guiding her through a maintenance or cooking procedure. This requires a machine processable representation of the tasks for which workflows have proven an efficient means. The increasing number of available sensors in intelligent environments can facilitate the execution of workflows. The sensors can help to recognize when a user has finished a step in the workflow and thus to automatically proceed to the next step. This can heavily reduce the amount of required user interaction. However, manually specifying the conditions for triggering the next step in a workflow is very cumbersome and almost impossible for environments which are not known at design time. In this paper, we present a novel approach for learning and adapting these conditions from observation. We show that the learned conditions can even outperform the quality as conditions manually specified by workflow experts. Thus, the presented approach is very well suited for automatically adapting workflows in intelligent environments and can in that way increase the efficiency of the workflow execution.

AB - Intelligent environments aim at supporting the user in executing her everyday tasks, e.g. by guiding her through a maintenance or cooking procedure. This requires a machine processable representation of the tasks for which workflows have proven an efficient means. The increasing number of available sensors in intelligent environments can facilitate the execution of workflows. The sensors can help to recognize when a user has finished a step in the workflow and thus to automatically proceed to the next step. This can heavily reduce the amount of required user interaction. However, manually specifying the conditions for triggering the next step in a workflow is very cumbersome and almost impossible for environments which are not known at design time. In this paper, we present a novel approach for learning and adapting these conditions from observation. We show that the learned conditions can even outperform the quality as conditions manually specified by workflow experts. Thus, the presented approach is very well suited for automatically adapting workflows in intelligent environments and can in that way increase the efficiency of the workflow execution.

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Adapting workflows to intelligent environments

Abstract

Conference

Bibliographical note

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Cite this