IEICE TRANSACTIONS on Information
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File and Task Abstraction in Task Workflow Patterns for File Recommendation Using File-Access Log
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Summary :
The numbers of files in file systems have increased dramatically in recent years. Office workers spend much time and effort searching for the documents required for their jobs. To reduce these costs, we propose a new method for recommending files and operations on them. Existing technologies for recommendation, such as collaborative filtering, suffer from two problems. First, they can only work with documents that have been accessed in the past, so that they cannot recommend when only newly generated documents are inputted. Second, they cannot easily handle sequences involving similar or differently ordered elements because of the strict matching used in the access sequences. To solve these problems, such minor variations should be ignored. In our proposed method, we introduce the concepts of abstract files as groups of similar files used for a similar purpose, abstract tasks as groups of similar tasks, and frequent abstract workflows grouped from similar workflows, which are sequences of abstract tasks. In experiments using real file-access logs, we confirmed that our proposed method could extract workflow patterns with longer sequences and higher support-count values, which are more suitable as recommendations. In addition, the F-measure for the recommendation results was improved significantly, from 0.301 to 0.598, compared with a method that did not use the concepts of abstract tasks and abstract workflows.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.4 pp.634-643
- Publication Date
- 2014/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.634
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering and Information Management)
- Category
Authors
Qiang SONG
Tokyo Institute of Technology
Takayuki KAWABATA
Canon Inc.
Fumiaki ITOH
Canon Inc.
Yousuke WATANABE
Tokyo Institute of Technology
Haruo YOKOTA
Tokyo Institute of Technology
Keyword
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Qiang SONG, Takayuki KAWABATA, Fumiaki ITOH, Yousuke WATANABE, Haruo YOKOTA, "File and Task Abstraction in Task Workflow Patterns for File Recommendation Using File-Access Log" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 4, pp. 634-643, April 2014, doi: 10.1587/transinf.E97.D.634.
Abstract: The numbers of files in file systems have increased dramatically in recent years. Office workers spend much time and effort searching for the documents required for their jobs. To reduce these costs, we propose a new method for recommending files and operations on them. Existing technologies for recommendation, such as collaborative filtering, suffer from two problems. First, they can only work with documents that have been accessed in the past, so that they cannot recommend when only newly generated documents are inputted. Second, they cannot easily handle sequences involving similar or differently ordered elements because of the strict matching used in the access sequences. To solve these problems, such minor variations should be ignored. In our proposed method, we introduce the concepts of abstract files as groups of similar files used for a similar purpose, abstract tasks as groups of similar tasks, and frequent abstract workflows grouped from similar workflows, which are sequences of abstract tasks. In experiments using real file-access logs, we confirmed that our proposed method could extract workflow patterns with longer sequences and higher support-count values, which are more suitable as recommendations. In addition, the F-measure for the recommendation results was improved significantly, from 0.301 to 0.598, compared with a method that did not use the concepts of abstract tasks and abstract workflows.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.634/_p
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@ARTICLE{e97-d_4_634,
author={Qiang SONG, Takayuki KAWABATA, Fumiaki ITOH, Yousuke WATANABE, Haruo YOKOTA, },
journal={IEICE TRANSACTIONS on Information},
title={File and Task Abstraction in Task Workflow Patterns for File Recommendation Using File-Access Log},
year={2014},
volume={E97-D},
number={4},
pages={634-643},
abstract={The numbers of files in file systems have increased dramatically in recent years. Office workers spend much time and effort searching for the documents required for their jobs. To reduce these costs, we propose a new method for recommending files and operations on them. Existing technologies for recommendation, such as collaborative filtering, suffer from two problems. First, they can only work with documents that have been accessed in the past, so that they cannot recommend when only newly generated documents are inputted. Second, they cannot easily handle sequences involving similar or differently ordered elements because of the strict matching used in the access sequences. To solve these problems, such minor variations should be ignored. In our proposed method, we introduce the concepts of abstract files as groups of similar files used for a similar purpose, abstract tasks as groups of similar tasks, and frequent abstract workflows grouped from similar workflows, which are sequences of abstract tasks. In experiments using real file-access logs, we confirmed that our proposed method could extract workflow patterns with longer sequences and higher support-count values, which are more suitable as recommendations. In addition, the F-measure for the recommendation results was improved significantly, from 0.301 to 0.598, compared with a method that did not use the concepts of abstract tasks and abstract workflows.},
keywords={},
doi={10.1587/transinf.E97.D.634},
ISSN={1745-1361},
month={April},}
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TY - JOUR
TI - File and Task Abstraction in Task Workflow Patterns for File Recommendation Using File-Access Log
T2 - IEICE TRANSACTIONS on Information
SP - 634
EP - 643
AU - Qiang SONG
AU - Takayuki KAWABATA
AU - Fumiaki ITOH
AU - Yousuke WATANABE
AU - Haruo YOKOTA
PY - 2014
DO - 10.1587/transinf.E97.D.634
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2014
AB - The numbers of files in file systems have increased dramatically in recent years. Office workers spend much time and effort searching for the documents required for their jobs. To reduce these costs, we propose a new method for recommending files and operations on them. Existing technologies for recommendation, such as collaborative filtering, suffer from two problems. First, they can only work with documents that have been accessed in the past, so that they cannot recommend when only newly generated documents are inputted. Second, they cannot easily handle sequences involving similar or differently ordered elements because of the strict matching used in the access sequences. To solve these problems, such minor variations should be ignored. In our proposed method, we introduce the concepts of abstract files as groups of similar files used for a similar purpose, abstract tasks as groups of similar tasks, and frequent abstract workflows grouped from similar workflows, which are sequences of abstract tasks. In experiments using real file-access logs, we confirmed that our proposed method could extract workflow patterns with longer sequences and higher support-count values, which are more suitable as recommendations. In addition, the F-measure for the recommendation results was improved significantly, from 0.301 to 0.598, compared with a method that did not use the concepts of abstract tasks and abstract workflows.
ER -
English
