research-article

Discovering frequent work procedures from resource connections

Authors:

Jianqiang Shen,

Erin Fitzhenry,

Thomas G. DietterichAuthors Info & Claims

IUI '09: Proceedings of the 14th international conference on Intelligent user interfaces

Pages 277 - 286

https://doi.org/10.1145/1502650.1502690

Published: 08 February 2009 Publication History

Abstract

Intelligent desktop assistants could provide more help for users if they could learn models of the users' workflows. However, discovering desktop workflows is difficult because they unfold over extended periods of time (days or weeks) and they are interleaved with many other workflows because of user multi-tasking. This paper describes an approach to discovering desktop workflows based on rich instrumentation of information flow actions such as copy/paste, SaveAs, file copy, attach file to email message, and save attachment. These actions allow us to construct a graph whose nodes are files, email messages, and web pages and whose edges are these information flow actions. A class of workflows that we call work procedures can be discovered by applying graph mining algorithms to find frequent subgraphs. This paper describes an algorithm for mining frequent closed connected subgraphs and then describes the results of applying this method to data collected from a group of real users.

References

[1]

R. Agrawal, D. Gunopulos, and F. Leymann. Mining process models from workflow logs. In Proc. of Sixth International Conference on Extending Database Technology, pp 469--483, 1998.

Digital Library

[2]

S. Amershi and C. Conati. Unsupervised and supervised machine learning in user modeling for intelligent learning environments. In IUI-07, pp 72--81.

Digital Library

[3]

V. Bellotti, N. Ducheneaut, M. Howard, and I. Smith. Taking email to task: the design and evaluation of a task management centered email tool. In CHI-03, pp 345--352, 2003.

Digital Library

[4]

T. Blanc-Brude and D. L. Scapin. What do people recall about their documents?: implications for desktop search tools. In Proc. of IUI-07, pp 102--111, 2007.

Digital Library

[5]

B. Chiu, E. Keogh, and S. Lonardi. Probabilistic discovery of time series motifs. In KDD-03, pp 493--498, 2003.

Digital Library

[6]

W. W. Cohen. Minorthird: Methods for Identifying Names and Ontological Relations in Text using Heuristics for Inducing Regularities from Data, 2004. Software available at http://minorthird. sourceforge. net.

[7]

A. N. Dragunov, T. G. Dietterich, K. Johnsrude, M. McLaughlin, L. Li, and J. L. Herlocker. Tasktracer: A desktop environment to support multi-tasking knowledge workers. In IUI-05, pp 75--82, 2005.

Digital Library

[8]

M. Dredze, T. Lau, and N. Kushmerick. Automatically classifying emails into activities. In Proc. of IUI-06, pp 70--77, 2006.

Digital Library

[9]

M. El-Ramly, E. Stroulia, and P. Sorenson. From run-time behavior to usage scenarios: an interaction-pattern mining approach. In Proc. of KDD-02, pp 315--324, 2002.

Digital Library

[10]

C. A. Ellis, K. -H. Kim, and A. J. Rembert. Workflow mining: Definitions, techniques, and future directions. Workflow Handbook, pp 213--228, 2006.

[11]

X. Z. Fern, C. Komireddy, and M. Burnett. Mining interpretable human strategies: A case study. In Proc. of ICDM-07, pp 475--480, 2007.

Digital Library

[12]

J. Frew, D. Metzger, and P. Slaughter. Automatic capture and reconstruction of computational provenance. Concurr. Comput.: Pract. Exper., 20(5):485--496, 2008.

Digital Library

[13]

D. Gonçalves and J. A. Jorge. Describing documents: what can users tell us? In IUI-04, pp 247--249, 2004.

Digital Library

[14]

G. Greco, A. Guzzo, G. Manco, and D. Sacca. Mining and reasoning on workflows. IEEE Trans. on Knowl. and Data Eng., 17(4):519--534, 2005.

Digital Library

[15]

R. Hamid, S. Maddi, A. Johnson, A. Bobick, I. Essa, and C. Isbell. Unsupervised activity discovery and characterization from event-streams. In UAI-05, 2005.

[16]

A. Inokuchi, T. Washio, K. Nishimura, and H. Motoda. A fast algorithm for mining frequent connected graphs. Technical Report RT0448, IBM Research, 2002.

[17]

T. Joachims. Learning to Classify Text Using Support Vector Machines. Kluwer Academic Publishers, 2001.

Digital Library

[18]

N. Kushmerick and T. Lau. Automated email activity management: an unsupervised learning approach. In Proc. of IUI-05, pp 67--74, 2005.

Digital Library

[19]

D. Lam, S. L. Rohall, C. Schmandt, and M. K. Stern. Exploiting e-mail structure to improve summarization. In Proc. of CSCW-02, 2002.

[20]

G. Leshed, E. Haber, T. Matthews, and T. Lau. Coscripter: Automating and sharing how-to knowledge in the enterprise. In CHI 2008, 2008.

Digital Library

[21]

J. Lin, E. Keogh, S. Lonardi, and P. Patel. Finding motifs in time series. In the 2nd workshop on temporal data mining at the 8th SIGKDD, pp 53--68, 2002.

[22]

G. Mark, V. M. Gonzalez, and J. Harris. No task left behind? examining the nature of fragmented work. In Proc. of CHI-05, pp 321--330, 2005.

Digital Library

[23]

A. K. A. D. Medeiros, W. M. P. van der Aalst, and A. J. M. M. Weijters. Workflow mining: Current status and future directions. In CoopIS/DOA/ODBASE, LNCS-2888, pp 389--406, 2003.

[24]

D. Minnen, C. Isbell, I. Essa, and T. Starner. Discovering multivariate motifs using subsequence density estimation and greedy mixture learning. In Proc. of AAAI-07, pp 615--620, 2007.

Digital Library

[25]

D. Minnen, T. Starner, I. Essa, and C. Isbell. Improving activity discovery with automatic neighborhood estimation. In Proc. of IJCAI-07, pp 6--12, 2007.

Digital Library

[26]

T. M. Mitchell, S. H. Wang, Y. Huang, and A. Cheyer. Extracting knowledge about users' activities from raw workstation contents. In Proc. of AAAI-06, 2006.

Digital Library

[27]

K.-K. Muniswamy-Reddy, D. A. Holland, U. Braun, and M. Seltzer. Provenance-aware storage systems. In Proc. of the annual conference on USENIX-06, 2006.

Digital Library

[28]

S. Natarajan, H. H. Bui, P. Tadepalli, K. Kersting, and W. -K. Wong. Logical hierarchical hidden markov models for modeling user activities. In Proc. of ILP-08, pp 192--209, 2008.

Digital Library

[29]

S. Nijssen and J. N. Kok. A quickstart in frequent structure mining can make a difference. In Proc. of KDD-04, pp 647--652, 2004.

Digital Library

[30]

S. Shah, C. A. N. Soules, G. R. Ganger, andB. D. Noble. Using provenance to aid in personal file search. In Proc. of the USENIX-07, 2007.

Digital Library

[31]

J. Shen, L. Li, and T. G. Dietterich. Real-time detection of task switches of desktop users. In Proc. of IJCAI-07, pp 2868--2873, 2007.

Digital Library

[32]

Y. Tanaka, K. Iwamoto, and K. Uehara. Discovery of time-series motif from multi-dimensional data based on mdl principle. Machine Learning, 58(2-3):269--300, 2005.

Digital Library

[33]

X. Yan and J. Han. Closegraph: mining closed frequent graph patterns. In KDD-03, pp 286--295, 2003.

Digital Library

Cited By

Langerak TChristen SAlbaba MGebhardt CHolz CHilliges O(2024)MARLUI: Multi-Agent Reinforcement Learning for Adaptive Point-and-Click UIsProceedings of the ACM on Human-Computer Interaction10.1145/36611478:EICS(1-27)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3661147
Völkel SSchneegass CEiband MBuschek DPaternò FOliver NConati CSpano LTintarev N(2020)What is "intelligent" in intelligent user interfaces?Proceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377500(477-487)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377500
Ginon BStumpf SJean-Daubias SCostabile MBuono PMatera M(2016)Towards the Right Assistance at the Right Time for Using Complex InterfacesProceedings of the International Working Conference on Advanced Visual Interfaces10.1145/2909132.2909275(240-243)Online publication date: 7-Jun-2016
https://dl.acm.org/doi/10.1145/2909132.2909275
Show More Cited By

Index Terms

Discovering frequent work procedures from resource connections

Recommendations

An Efficient Hash-Based Method for Discovering the Maximal Frequent Set
COMPSAC '01: Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development

The association rule mining can be divided into two steps. The first step is to find out all frequent itemsets, whose occurrences are greater than or equal to the user-specified threshold. The second step is to generate reliable association rules based ...
Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining

Since mining frequent patterns from transactional databases involves an exponential mining space and generates a huge number of patterns, efficient discovery of user-interest-based frequent pattern set becomes the first priority for a mining algorithm. ...
A Frequent Item Graph Approach for Discovering Frequent Itemsets
ICACTE '08: Proceedings of the 2008 International Conference on Advanced Computer Theory and Engineering

Efficient algorithms to discover frequent patterns are crucial in data mining research. Finding frequent item sets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

IUI '09: Proceedings of the 14th international conference on Intelligent user interfaces

February 2009

522 pages

ISBN:9781605581682

DOI:10.1145/1502650

General Chairs:
Cristina Conati
University of British Columbia, Canada
,
Mathias Bauer
mineway GmbH, Germany
,
Program Chairs:
Nuria Oliver
Telefonica Research, Spain
,
Dan Weld
University of Washington, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 February 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

IUI09

Sponsor:

IUI09: 14th International Conference on Intelligent User Interfaces

February 8 - 11, 2009

Florida, Sanibel Island, USA

Acceptance Rates

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
423
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Langerak TChristen SAlbaba MGebhardt CHolz CHilliges O(2024)MARLUI: Multi-Agent Reinforcement Learning for Adaptive Point-and-Click UIsProceedings of the ACM on Human-Computer Interaction10.1145/36611478:EICS(1-27)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3661147
Völkel SSchneegass CEiband MBuschek DPaternò FOliver NConati CSpano LTintarev N(2020)What is "intelligent" in intelligent user interfaces?Proceedings of the 25th International Conference on Intelligent User Interfaces10.1145/3377325.3377500(477-487)Online publication date: 17-Mar-2020
https://dl.acm.org/doi/10.1145/3377325.3377500
Ginon BStumpf SJean-Daubias SCostabile MBuono PMatera M(2016)Towards the Right Assistance at the Right Time for Using Complex InterfacesProceedings of the International Working Conference on Advanced Visual Interfaces10.1145/2909132.2909275(240-243)Online publication date: 7-Jun-2016
https://dl.acm.org/doi/10.1145/2909132.2909275
Patidar MRohatgi SChaudhary ASingh MAgarwal PShroff G(2016)Activity Detection from Email Meta-Data Clustering2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2016.0087(568-575)Online publication date: Dec-2016
https://doi.org/10.1109/ICDMW.2016.0087
Pachidi SSpruit MVan De Weerd I(2014)Understanding users' behavior with software operation data miningComputers in Human Behavior10.1016/j.chb.2013.07.04930(583-594)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1016/j.chb.2013.07.049
Conati C(2013)Virtual butlerYour Virtual Butler10.5555/2554494.2554501(29-41)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.5555/2554494.2554501
Babaian TLucas W(2013)Modeling Data for Enterprise Systems with MemoriesJournal of Database Management10.4018/jdm.201304010124:2(1-12)Online publication date: Apr-2013
https://doi.org/10.4018/jdm.2013040101
Amershi SMahmud JNichols JLau TRuiz G(2013)LiveActionACM Transactions on Interactive Intelligent Systems10.1145/2533670.25336723:3(1-23)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1145/2533670.2533672
Gervasio MLee T(2013)Discovering action idioms bridging the gap between system-level events and human-level actions2013 IEEE Symposium on Visual Languages and Human Centric Computing10.1109/VLHCC.2013.6645236(11-14)Online publication date: Sep-2013
https://doi.org/10.1109/VLHCC.2013.6645236
Babaian TLucas W(2013)Composing Interface Demonstrations Automatically from Usage LogsEnterprise Information Systems10.1007/978-3-642-40654-6_23(376-392)Online publication date: 2013
https://doi.org/10.1007/978-3-642-40654-6_23
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents