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E-MIIM: an ensemble-learning-based context-aware mobile telephony model for intelligent interruption management

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Abstract

Nowadays, mobile telephony interruptions in our daily life activities are common because of the inappropriate ringing notifications of incoming phone calls in different contexts. Such interruptions may impact on the work attention not only for the mobile phone owners, but also for the surrounding people. Decision tree is the most popular machine-learning classification technique that is used in existing context-aware mobile intelligent interruption management (MIIM) model to overcome such issues. However, a single-decision tree-based context-aware model may cause over-fitting problem and thus decrease the prediction accuracy of the inferred model. Therefore, in this paper, we propose an ensemble machine-learning-based context-aware mobile telephony model for the purpose of intelligent interruption management by taking into account multi-dimensional contexts and name it “E-MIIM”. The experimental results on individuals’ real-life mobile telephony data sets show that our E-MIIM model is more effective and outperforms existing MIIM model for predicting and managing individual’s mobile telephony interruptions based on their relevant contextual information.

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References

  • Amit Y, Geman D (1997) Shape quantization and recognition with randomized trees. Neural Comput 9(7):1545–1588

    Article  Google Scholar 

  • Bailey BP, Konstan JA (2006) On the need for attention-aware systems: measuring effects of interruption on task performance, error rate, and affective state. Comput Hum Behav 22(4):685–708

    Article  Google Scholar 

  • Böhmer M, Lander C, Gehring S, Brumby DP, Krüger A (2014) Interrupted by a phone call: exploring designs for lowering the impact of call notifications for smartphone users. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 3045–3054

  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Chapman & Hall, New York

    MATH  Google Scholar 

  • Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    MATH  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Bureau of labor statistics (2019). http://www.bls.gov

  • Chang Y-J, Tang JC (2015) Investigating mobile users’ ringer mode usage and attentiveness and responsiveness to communication. In: Proceedings of the 17th international conference on human-computer interaction with mobile devices and services. ACM, pp 6–15

  • De Guzman ES, Sharmin M, Bailey BP (2007) Should i call now? understanding what context is considered when deciding whether to initiate remote communication via mobile devices. In: Proceedings of graphics interface 2007. ACM, pp 143–150

  • Dekel A, Nacht D, Kirkpatrick S (2009) Minimizing mobile phone disruption via smart profile management. In: Proceedings of the 11th international conference on human-computer interaction with mobile devices and services. ACM, pp 43

  • Eagle N, Pentland AS (2006) Reality mining: sensing complex social systems. Person Ubiquitous Comput 10(4):255–268

    Article  Google Scholar 

  • Freitas AA (2000) Understanding the crucial differences between classification and discovery of association rules: a position paper. ACM SIGKDD Explor Newsl 2(1):65–69

    Article  Google Scholar 

  • Grandhi S, Jones Q (2010) Technology-mediated interruption management. Int J Hum Comput Stud 68(5):288–306

    Article  Google Scholar 

  • Hong J, Suh E-H, Kim J, Kim SY (2009) Context-aware system for proactive personalized service based on context history. Expert Syst Appl 36(4):7448–7457

    Article  Google Scholar 

  • Khalil A, Connelly K (2005) Improving cell phone awareness by using calendar information. In: Human-computer interaction. Springer, pp 588–600

  • Khalil A, Connelly K (2006) Context-aware telephony: privacy preferences and sharing patterns. In: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work. ACM, pp 469–478

  • Lee W-P (2007) Deploying personalized mobile services in an agent-based environment. Expert Syst Appl 32(4):1194–1207

    Article  Google Scholar 

  • Lovett T, O’Neill E, Irwin J, Pollington D (2010) The calendar as a sensor: analysis and improvement using data fusion with social networks and location. In: Proceedings of the 12th international conference on ubiquitous computing. ACM, pp 3–12

  • Pejovic V, Musolesi M (2014) Interruptme: designing intelligent prompting mechanisms for pervasive applications. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 897–908

  • Ross Quinlan J (1993) C4.5: programs for machine learning. Machine Learning, Morgan Kaufmann Publishers, Inc., pp 235–240

  • Rosenthal S, Dey AK, Veloso M (2011) Using decision-theoretic experience sampling to build personalized mobile phone interruption models. International Conference on Pervasive Computing. Springer, Berlin, Heidelberg, pp 170–187

    Chapter  Google Scholar 

  • Salovaara A, Lindqvist A, Hasu T, Häkkilä J (2011) The phone rings but the user doesn’t answer: unavailability in mobile communication. In: Proceedings of the 13th international conference on human computer interaction with mobile devices and services. ACM, pp 503–512

  • Sarker IH (2018a) Behavminer: mining user behaviors from mobile phone data for personalized services. In: Proceedings of the 2018 IEEE international conference on pervasive computing and communications (PerCom 2018), Athens. Greece, IEEE

  • Sarker IH (2018b) Mobile data science: Towards understanding data-driven intelligent mobile applications. EAI Endorsed Trans Scalable Inf Syst 5(19):1–11

    Google Scholar 

  • Sarker IH (2018c) Research issues in mining user behavioral rules for context-aware intelligent mobile applications. Iran J Comput Sci 2(1):41–51

    Article  MathSciNet  Google Scholar 

  • Sarker IH (2018d) Silentphone: inferring user unavailability based opportune moments to minimize call interruptions. EAI Endorsed Trans Mobile Commun Appl 4(15):1–8

    Google Scholar 

  • Sarker IH (2018e) Understanding the role of data-centric social context in personalized mobile applications. EAI Endorsed Trans Context-aware Syst Appl 5(15):1–6

    Google Scholar 

  • Sarker IH (2019) A machine learning based robust prediction model for real-life mobile phone data. Internet Things 5:180–193

    Article  Google Scholar 

  • Sarker IH, Colman A, Han J (2019) Recencyminer: mining recency-based personalized behavior from contextual smartphone data. Journal of Big Data 6(1):49

    Article  Google Scholar 

  • Sarker IH, Colman A, Kabir MA, Han J (2016) Behavior-oriented time segmentation for mining individualized rules of mobile phone users. In: Proceedings of the 2016 IEEE international conference on data science and advanced analytics (IEEE DSAA), Montreal, Canada. IEEE, pp 488–497

  • Sarker IH, Colman A, Kabir MA, Han J (2016) Phone call log as a context source to modeling individual user behavior. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing (Ubicomp): adjunct, Germany. ACM, pp 630–634

  • Sarker IH, Colman A, Kabir MA, Han J (2018) Individualized time-series segmentation for mining mobile phone user behavior. Comput J Oxf Univ UK 61(3):349–368

    Google Scholar 

  • Sarker IH, Kabir MA, Colman A, Han J (2016) Evidence-based behavioral model for calendar schedules of individual mobile phone users. In: Proceedings of the 2016 IEEE international conference on data science and advanced analytics (IEEE DSAA), Montreal, Canada. IEEE, pp 584–593

  • Sarker Iqbal H, Kabir MA, Colman Alan, Han Jun (2017) Designing architecture of a rule-based system for managing phone call interruptions. In: Proceedings of the 2017 ACM international joint conference on pervasive and ubiquitous computing and proceedings of the 2017 ACM international symposium on wearable computers, USA. ACM, pp 898–903

  • Sarker IH, Kayes ASM, Watters P (2019) Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. J Big Data

  • Seo S-S, Kwon A, Kang J-M, Strassner J, Hong JW-K (2011) Pyp: design and implementation of a context-aware configuration manager for smartphones. In: Proceedings of the 1st international workshop on smart mobile applications (SmartApps’ 11)

  • Smith J, Dulay N (2014) Ringlearn: long-term mitigation of disruptive smartphone interruptions. In: 2014 IEEE international conference on pervasive computing and communications workshops (PERCOM workshops). IEEE, pp 27–35

  • Spira JB, Feintuch JB (2005) The cost of not paying attention: how interruptions impact knowledge worker productivity. Rep Basex

  • Toninelli A, Montanari R, Lassila O, Khushraj D (2009) What’s on users’ minds? toward a usable smart phone security model. Pervasive Comput IEEE 8(2):32–39

    Article  Google Scholar 

  • Witten IH, Frank E, Trigg LE, Hall MA, Holmes G, Cunningham SJ (1999) Weka: practical machine learning tools and techniques with java implementations. University of Waikato, Hamilton, New Zealand

    Google Scholar 

  • Zulkernain S, Madiraju P, Ahamed SI (2010a) A context aware interruption management system for mobile devices. In: Mobile wireless middleware, operating systems, and applications. Springer, pp 221–234

  • Zulkernain S, Madiraju P, Ahamed SI, Stamm K (2010b) A mobile intelligent interruption management system. J UCS 16(15):2060–2080

    Google Scholar 

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Authors and Affiliations

  1. Chittagong University of Engineering and Technology, Chittagong, Bangladesh

    Iqbal H. Sarker

  2. Swinburne University of Technology, Melbourne, VIC, 3122, Australia

    Iqbal H. Sarker

  3. La Trobe University, Melbourne, VIC, 3086, Australia

    A. S. M. Kayes

  4. Canberra Institute of Technology, Reid, ACT, Australia

    Md Hasan Furhad

  5. Extend View Inc., Los Angeles, CA, USA

    Mohammad Mainul Islam

  6. University of California, Riverside, CA, USA

    Md Shohidul Islam

Authors
  1. Iqbal H. Sarker
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  2. A. S. M. Kayes
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  3. Md Hasan Furhad
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  4. Mohammad Mainul Islam
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  5. Md Shohidul Islam
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Correspondence to Iqbal H. Sarker.

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Sarker, I.H., Kayes, A.S.M., Furhad, M.H. et al. E-MIIM: an ensemble-learning-based context-aware mobile telephony model for intelligent interruption management. AI & Soc 35, 459–467 (2020). https://doi.org/10.1007/s00146-019-00898-8

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  • DOI: https://doi.org/10.1007/s00146-019-00898-8

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