More Web Proxy on the site http://driver.im/

research-article

MobileMiner: mining your frequent patterns on your phone

Authors:

Vijay Srinivasan,

Saeed Moghaddam,

Abhishek Mukherji,

Kiran K. Rachuri,

Emmanuel Munguia TapiaAuthors Info & Claims

UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Pages 389 - 400

https://doi.org/10.1145/2632048.2632052

Published: 13 September 2014 Publication History

Abstract

Smartphones can collect considerable context data about the user, ranging from apps used to places visited. Frequent user patterns discovered from longitudinal, multi-modal context data could help personalize and improve overall user experience. Our long term goal is to develop novel middleware and algorithms to efficiently mine user behavior patterns entirely on the phone by utilizing idle processor cycles. Mining patterns on the mobile device provides better privacy guarantees to users, and reduces dependency on cloud connectivity. As an important step in this direction, we develop a novel general-purpose service called MobileMiner that runs on the phone and discovers frequent co-occurrence patterns indicating which context events frequently occur together. Using longitudinal context data collected from 106 users over 1--3 months, we show that MobileMiner efficiently generates patterns using limited phone resources. Further, we find interesting behavior patterns for individual users and across users, ranging from calling patterns to place visitation patterns. Finally, we show how our co-occurrence patterns can be used by developers to improve the phone UI for launching apps or calling contacts.

Supplementary Material

MOV File (p389-srinivasan.mov)

Download
175.55 MB

References

[1]

Aggarwal, C. C., and Yu, P. S. A new approach to online generation of association rules. IEEE Transactions on Knowledge and Data Engineering 13, 4 (2001), 527--540.

Digital Library

[2]

Agrawal, R., and Srikant, R. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB '94), Morgan Kaufmann (1994).

Digital Library

[3]

Aharony, N., Pan, W., Ip, C., Khayal, I., and Pentland, A. Social fmri: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing 7, 6 (2011).

Digital Library

[4]

Allen, J. F. Maintaining knowledge about temporal intervals. Communications of the ACM 26, 11 (1983), 832--843.

Digital Library

[5]

Android operating system. http://www.android.com/.

[6]

Azizyan, M., Constandache, I., and Roy Choudhury, R. Surroundsense: Mobile phone localization via ambience fingerprinting. In Proceedings of the 15th Annual International Conference on Mobile Computing and Networking (MobiCom '09) (2009).

Digital Library

[7]

Banerjee, N., Agarwal, S., Bahl, P., Chandra, R., Wolman, A., and Corner, M. Virtual compass: Relative positioning to sense mobile social interactions. In Proceedings of the 8th International Conference on Pervasive Computing (Pervasive '10), Springer-Verlag (2010).

Digital Library

[8]

Borgelt, C. Efficient implementations of apriori, eclat and fp-growth. http://www.borgelt.net, August 2013.

[9]

Cheung, D. W., Han, J., Ng, V. T., and Wong, C. Maintenance of discovered association rules in large databases: An incremental updating technique. In Data Engineering, 1996. Proceedings of the Twelfth International Conference on, IEEE (1996), 106--114.

Digital Library

[10]

Samsung galaxy s4. http://www.samsung.com/latin_en/consumer/mobile-phones/mobile-phones/smartphone/GT-I9500ZKLTPA-spec.

[11]

Samsung gear. http://www.samsung.com/us/mobile/wearable-tech.

[12]

Han, J., Kamber, M., and Pei, J. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., 2011.

Digital Library

[13]

Hao, T., Xing, G., and Zhou, G. isleep: Unobtrusive sleep quality monitoring using smartphones. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys '13), ACM (2013).

Digital Library

[14]

Ifttt mobile recipes. https://ifttt.com/recipes.

[15]

ios 7. https://www.apple.com/ios/what-is/.

[16]

Kwapisz, J. R., Weiss, G. M., and Moore, S. A. Activity recognition using cell phone accelerometers. SIGKDD Explorations Newsletter 12, 2 (2011), 74--82.

Digital Library

[17]

Li, W., Han, J., and Pei, J. Cmar: accurate and efficient classification based on multiple class-association rules. In Proceedings of IEEE International Conference on Data Mining (ICDM '01), IEEE (2001).

Digital Library

[18]

Lin, K., Kansal, A., Lymberopoulos, D., and Zhao, F. Energy-accuracy trade-off for continuous mobile device location. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys '10), ACM (2010).

Digital Library

[19]

Linux foundation. http://collabprojects.linuxfoundation.org/.

[20]

Liu, B., Jiang, Y., Sha, F., and Govindan, R. Cloud-enabled privacy-preserving collaborative learning for mobile sensing. In Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems (SenSys '12), ACM (2012).

Digital Library

[21]

Liu, J., Priyantha, B., Hart, T., Ramos, H. S., Loureiro, A. A. F., and Wang, Q. Energy efficient gps sensing with cloud offloading. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems (SenSys '12), ACM (2012).

Digital Library

[22]

Lu, H., Pan, W., Lane, N. D., Choudhury, T., and Campbell, A. T. Soundsense: Scalable sound sensing for people-centric applications on mobile phones. In Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services (MobiSys '09), ACM (2009).

Digital Library

[23]

Miluzzo, E., Lane, N. D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S. B., Zheng, X., and Campbell, A. T. Sensing meets mobile social networks: The design, implementation and evaluation of the cenceme application. In Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems(SenSys '08), ACM (2008).

Digital Library

[24]

Monsoon power monitor. https://www.msoon.com/LabEquipment/PowerMonitor/.

[25]

Nath, S. Ace: Exploiting correlation for energy-efficient and continuous context sensing. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys '12), ACM (2012).

Digital Library

[26]

Parate, A., Böhmer, M., Chu, D., Ganesan, D., and Marlin, B. M. Practical prediction and prefetch for faster access to applications on mobile phones. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, ACM (2013), 275--284.

Digital Library

[27]

Shin, C., Hong, J.-H., and Dey, A. K. Understanding and prediction of mobile application usage for smart phones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp '12), ACM (2012).

Digital Library

[28]

Srinivasan, V., Moghaddam, S., Mukherji, A., Rachuri, K., Xu, C., and Tapia, E. M. On-device mining of mobile users' co-occurrence patterns. In Proceedings of the 15th International Workshop on Mobile Computing Systems and Applications (POSTER) (2014).

[29]

Survey monkey. https://www.surveymonkey.com/.

[30]

Tizen platform. https://www.tizen.org.

[31]

Welbourne, E., Wu, P., Bao, X., and Munguia-Tapia, E. Crowdsourced mobile data collection: lessons learned from a new study methodology. In Proceedings of the 15th Workshop on Mobile Computing Systems and Applications, ACM (2014), 2.

Digital Library

[32]

Yan, T., Chu, D., Ganesan, D., Kansal, A., and Liu, J. Fast app launching for mobile devices using predictive user context. In Proceedings of the 10th international conference on Mobile systems, applications, and services, ACM (2012), 113--126.

Digital Library

[33]

Yin, X., and Han, J. Cpar: Classification based on predictive association rules. In Proceedings of the 2003 SIAM International Conference on Data Mining (SDM '03), SIAM (2003).

[34]

Zaki, M. J. Spade: An efficient algorithm for mining frequent sequences. 31--60.

[35]

Zou, X., Zhang, W., Li, S., and Pan, G. Prophet: What app you wish to use next. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication (UbiComp '13 Adjunct), ACM (2013).

Digital Library

Cited By

Lee HPark TLee UKostakos VKay JHoang T(2024)Smartphone-based Human Behavior Task Modeling for Explainable Mental Health Detection ModelCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3679003(894-896)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3679003
Khaokaew YSalim FXue H(2023)Understanding Mobile Information Needs and BehavioursAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610760(210-214)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610760
Chen WYu CWang HWang ZYang LWang YShi WShi Y(2023)From Gap to Synergy: Enhancing Contextual Understanding through Human-Machine Collaboration in Personalized SystemsProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606741(1-15)Online publication date: 29-Oct-2023
https://dl.acm.org/doi/10.1145/3586183.3606741
Show More Cited By

Index Terms

MobileMiner: mining your frequent patterns on your phone
1. Information systems
  1. Information systems applications

Recommendations

On Mining Movement Pattern from Mobile Users
Heterogenous Wireless Ad Hoc and Sensor Networks

In the era in which activities performed by mobile users are tracked through various sensing mechanisms, the movement data collected through these sensors is submitted into a data mining algorithm in order to determine the movement pattern. The movement ...
Mining User Behavioral Rules from Smartphone Data Through Association Analysis
Advances in Knowledge Discovery and Data Mining
Abstract
The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contextual information and mobile phone usage records through the device logs. This paper formulates the problem of mining ...
Mobile Data Stream Mining: From Algorithms to Applications
MDM '12: Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (mdm 2012)

This paper presents an overview of the current state-of-the-art in mobile data stream mining. This area of mobile data stream mining is significant for a number of new application domains such as mobile crowd sensing and mobile activity recognition. The ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 2014

973 pages

ISBN:9781450329682

DOI:10.1145/2632048

General Chairs:
AJ Brush
Microsoft Research
,
Adrian Friday
Lancaster University, UK
,
Program Chairs:
Julie Kientz
University of Washington
,
James Scott
Microsoft Research, UK
,
Junehwa Song
Korea Advanced Institute of Science and Technology (KAIST), KR

Copyright © 2014 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

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UbiComp '14

Sponsor:

UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing

September 13 - 17, 2014

Washington, Seattle

Acceptance Rates

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

112
Total Citations
View Citations
1,682
Total Downloads

Downloads (Last 12 months)43
Downloads (Last 6 weeks)4

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Lee HPark TLee UKostakos VKay JHoang T(2024)Smartphone-based Human Behavior Task Modeling for Explainable Mental Health Detection ModelCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3679003(894-896)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3679003
Khaokaew YSalim FXue H(2023)Understanding Mobile Information Needs and BehavioursAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610760(210-214)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3610760
Chen WYu CWang HWang ZYang LWang YShi WShi Y(2023)From Gap to Synergy: Enhancing Contextual Understanding through Human-Machine Collaboration in Personalized SystemsProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606741(1-15)Online publication date: 29-Oct-2023
https://dl.acm.org/doi/10.1145/3586183.3606741
Li TFan YLi YTarkoma SHui P(2023)Understanding the Long-Term Evolution of Mobile App UsageIEEE Transactions on Mobile Computing10.1109/TMC.2021.309866422:2(1213-1230)Online publication date: 1-Feb-2023
https://doi.org/10.1109/TMC.2021.3098664
Chen CTso GHe K(2023)Quantum Optimized Cost Based Feature Selection and Credit Scoring for Mobile Micro-financingComputational Economics10.1007/s10614-023-10365-863:2(919-950)Online publication date: 13-Mar-2023
https://doi.org/10.1007/s10614-023-10365-8
Khan MLu LAfzal HNasir J(2023)PredictionMiner: mining the latest individual behavioral rules for personalized contextual pattern predictionsSoft Computing10.1007/s00500-023-08572-4Online publication date: 17-Jul-2023
https://doi.org/10.1007/s00500-023-08572-4
Leung CSuleiman BAlibasa MAl-Naymat G(2023)Anonymous Yet Alike: A Privacy-Preserving DeepProfile Clustering for Mobile Usage PatternsMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-031-34776-4_5(81-100)Online publication date: 27-Jun-2023
https://doi.org/10.1007/978-3-031-34776-4_5
Sadhu VZonouz SPompili D(2022) DeepContext: Mobile Context Modeling and Prediction Via HMMs and Deep Learning IEEE Transactions on Mobile Computing10.1109/TMC.2022.3200947(1-16)Online publication date: 2022
https://doi.org/10.1109/TMC.2022.3200947
Dammak HMejri ORiahi MMoussa F(2022)User Group Profiling through Mobile Application Usage Behavior2022 IEEE 9th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)10.1109/SETIT54465.2022.9875502(278-285)Online publication date: 28-May-2022
https://doi.org/10.1109/SETIT54465.2022.9875502
Li TXia TWang HTu ZTarkoma SHan ZHui P(2022)Smartphone App Usage Analysis: Datasets, Methods, and ApplicationsIEEE Communications Surveys & Tutorials10.1109/COMST.2022.316317624:2(937-966)Online publication date: Oct-2023
https://doi.org/10.1109/COMST.2022.3163176
Show More Cited By

View Options

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.

Figures

Tables

Media

View Table of Conten