skip to main content
10.1145/3099023.3099046acmconferencesArticle/Chapter ViewAbstractPublication PagesumapConference Proceedingsconference-collections
research-article

Synthesis & Evaluation of a Mobile Notification Dataset

Published:09 July 2017Publication History

ABSTRACT

Open-source mobile notification datasets are a rarity in the research community. Due to the sensitive nature of mobile notifications it is difficult to find a dataset which captures their features in such a way that their inherently personal information is kept private. For this reason, the majority of research in the domain of Notification Management requires ad-hoc software to be developed for capturing the data necessary to test hypotheses, train algorithms and evaluate proposed systems. As an alternative, this paper discusses the process, advantages and limitations with harnessing a large-scale mobile usage dataset for deriving a synthetic mobile notification dataset used in testing and improving an intelligent Notification Management System (NMS).

References

  1. Nadav Aharony, Wei Pan, Cory Ip, Inas Khayal, and Alex Pentland. 2011. Social fMRI: Investigating and Shaping Social Mechanisms in the Real World. Pervasive Mob. Comput. 7, 6 (Dec. 2011), 643--659. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Stephen Bell, Alisdair McDiarmid, and James Irvine. 2011. Nodobo: Mobile phone as a software sensor for social network research. In Vehicular Technology Conference (VTC Spring), 2011 IEEE 73rd. IEEE, 1--5.Google ScholarGoogle ScholarCross RefCross Ref
  3. Vincent D Blondel, Adeline Decuyper, and Gautier Krings. 2015. A survey of results on mobile phone datasets analysis. EPJ Data Science 4, 1 (2015), 10.Google ScholarGoogle ScholarCross RefCross Ref
  4. Fulvio Corno, Luigi De Russis, and Teodoro Montanaro. 2015. A context and user aware smart notification system. In Internet of things (WF-IoT), 2015 IEEE 2nd World Forum on. IEEE, 645--651. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. CrowdSignals.io. 2017. A massive new mobile data collection. http://crowdsignals. io/. (2017). Online; accessed 19 April 2017.Google ScholarGoogle Scholar
  6. Denzil Ferreira, Vassilis Kostakos, and Anind K Dey. 2012. Lessons learned from large-scale user studies: Using android market as a source of data. International Journal of Mobile Human Computer Interaction (IJMHCI) 4, 3 (2012), 28--43. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Joel E. Fischer, Chris Greenhalgh, and Steve Benford. 2011. Investigating Episodes of Mobile Phone Activity As Indicators of Opportune Moments to Deliver Notifications. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI '11). ACM, New York, NY, USA, 181--190. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Kieran Fraser, Bilal Yousuf, and Owen Conlan. 2016. A Context-aware, Info-bead and Fuzzy Inference Approach to Notification Management. In 2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference sponsored by IEEE (Published by IEEE Xplore) (IEEE UEMCON 2016). New York, USA.Google ScholarGoogle Scholar
  9. Kieran Fraser, Bilal Yousuf, and Owen Conlan. forthcoming. An In-the-wild & Synthetic Mobile Notification Dataset Evaluation.Google ScholarGoogle Scholar
  10. Gabriella M Harari, Nicholas D Lane, Rui Wang, Benjamin S Crosier, Andrew T Campbell, and Samuel D Gosling. 2016. Using smartphones to collect behavioral data in psychological Science: Opportunities, practical considerations, and challenges. Perspectives on Psychological Science 11, 6 (2016), 838--854.Google ScholarGoogle ScholarCross RefCross Ref
  11. Nicky Kern and Bernt Schiele. Context-aware notification for wearable com- puting. In Proceedings of the 7th IEEE International Symposium on Wearable Computers (ISWC'03). 223--230. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Niko Kiukkonen, Jan Blom, Olivier Dousse, Daniel Gatica-Perez, and Juha Laurila. Towards rich mobile phone datasets: Lausanne data collection campaign. (????).Google ScholarGoogle Scholar
  13. Bongshin Lee, Rubaiat Habib Kazi, and Greg Smith. 2013. SketchStory: Telling more engaging stories with data through freeform sketching. IEEE Transactions on Visualization and Computer Graphics 19, 12 (2013), 2416--2425. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Soo Ling Lim, Peter J Bentley, Natalie Kanakam, Fuyuki Ishikawa, and Shinichi Honiden. 2015. Investigating country differences in mobile app user behavior and challenges for software engineering. IEEE Transactions on Software Engineering 41, 1 (2015), 40--64.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Emilie Lundin, Håkan Kvarnström, and Erland Jonsson. 2002. A synthetic fraud data generation methodology. Information and Communications Security (2002), 265--277. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Abhinav Mehrotra, Mirco Musolesi, Robert Hendley, and Veljko Pejovic. 2015. Designing Content-driven Intelligent Notification Mechanisms for Mobile Applications. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). ACM, New York, NY, USA, 813--824. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Abhinav Mehrotra, Mirco Musolesi, Robert Hendley, and Veljko Pejovic. 2015. Designing content-driven intelligent notification mechanisms for mobile applications. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 813--824. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Abhinav Mehrotra, Veljko Pejovic, Jo Vermeulen, Robert Hendley, and Mirco Musolesi. 2016. My Phone and Me: Understanding People's Receptivity to Mobile Notifications.Google ScholarGoogle Scholar
  19. Tadashi Okoshi, Julian Ramos, Hiroki Nozaki, Jin Nakazawa, Anind K. Dey, and Hideyuki Tokuda. 2015. Reducing Users' Perceived Mental Effort Due to Interruptive Notifications in Multi-device Mobile Environments. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). ACM, New York, NY, USA, 475--486. DOI: https://doi. org/10.1145/2750858.2807517 Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Earl Oliver. 2010. The challenges in large-scale smartphone user studies. In Proceedings of the 2nd ACM International Workshop on Hot Topics in Planet-scale Measurement. ACM, 5. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Veljko Pejovic and Mirco Musolesi. 2014. InterruptMe: Designing Intelligent Prompting Mechanisms for Pervasive Applications. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubi- Comp '14). ACM, New York, NY, USA, 897--908. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Yujue Qin, Tanusri Bhattacharya, Lars Kulik, and James Bailey. 2014. A Context- aware Do-not-disturb Service for Mobile Devices. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia (MUM '14). ACM, New York, NY, USA, 236--239. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Daniel T Wagner, Andrew Rice, and Alastair R Beresford. 2014. Device Analyzer: Large-scale mobile data collection. ACM SIGMETRICS Performance Evaluation Review 41, 4 (2014), 53--56. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Bilal Yousuf and Owen Conlan. 2014. Enhancing Learner Engagement through Personalized Visual Narratives. In Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference on. IEEE, 89--93. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Bilal Yousuf and Owen Conlan. 2015. VisEN: Motivating Learner Engagement through Explorable Visual Narratives. In Design for Teaching and Learning in a Networked World. Springer, 367--380.Google ScholarGoogle Scholar

Index Terms

  1. Synthesis & Evaluation of a Mobile Notification Dataset

      Recommendations

      Comments

      Login options

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

      Sign in
      • Published in

        cover image ACM Conferences
        UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization
        July 2017
        456 pages
        ISBN:9781450350679
        DOI:10.1145/3099023

        Copyright © 2017 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]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 9 July 2017

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate162of633submissions,26%

        Upcoming Conference

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader