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A context-aware, info-bead and fuzzy inference approach to notification management

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... The work of Reference [32] is preliminary and attempts only to predict which device should be selected to deliver the notification, however, the set of notification data used to train the algorithms and evaluate the system result is partially synthetic and assumes that the data available for the notification is explicit. In contrast, the management system proposed by Reference [33], attempts to predict the most opportune moment for the notification to be delivered to the user using a set of data obtained from real users. Only the abstract derivation is used by the manager to predict delivery time. ...
... 1. Reference; 2. Solution Name and Year; 3. If the implementation makes use of the user's location; 4. Whether the implementation determines privacy preferences or implements actions to maintain private data; 5. Compares whether the notification solution makes use of multiple devices, being more than one mobile device or another smart device; 6. Informs whether the proposed solution is applied directly to the user's mobile device through an application; 7. Informs whether the solution uses human participation to verify the relevance and manual adjustments in the context of notification management. [27] Attelia (2014) X X [28] Message Monitor (2014) X X X X [29] Face-to-Face (2014) X X X [30] Desktop Notifications (2014) X X X X [31] Intelligent Push (2015) X X [32] Notification Collector (2015) X X X [33] NAbsMobile (2016) X X X X [34] No name (2017) X X X [19] Smartnotify (2018) X X [35] Notification Log (2018) X X X X This Work PRISER (2019) X X X X Although related work addresses location and notification management, many of these studies do not address the privacy features of the user's environment. Therefore, the present work proposes a module of notifications, to make the environment informative and dynamic to control privacy parameters. ...
... However, from a high-level perspective, each component of this taxonomy was based on a series of related works that helped us identify the needs of the NMS. The works used are represented by Table 2. [12,19,33] In this section, we discuss some enabling technologies, which compose the proposed taxonomic model. The taxonomy serves to classify rules and parameters, resulting in a better understanding of the functionality [51]. ...
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With the growing number of mobile devices receiving daily notifications, it is necessary to manage the variety of information produced. New smart devices are developed every day with the ability to generate, send, and display messages about their status, data, and information about other devices. Consequently, the number of notifications received by a user is increasing and their tolerance may decrease in a short time. With this, it is necessary to develop a management system and notification controls. In this context, this work proposes a notification and alert management system called PRISER. Its focus is on user profiles and environments, applying data privacy criteria.
... In this paper, we evaluate a modest notification dataset [16] captured in-the-wild and comprised of: ...
... For privacy reasons the content of notifications were dismissed however. In contrast, Fraser et al. [16] do not completely discard the notification content of their in-the-wild notifications but instead enable the user to uplift the sensitive information to an abstract form. If notification content could be generated synthetically while maintaining real-world underlying traits, there would be no need to abstract or discard valuable contextual information such as this. ...
... In a recent study of notification management [16] two notification datasets were harvested from two users'. An Android device was used to capture features of incoming mobile notifications via an application developed for the purposes of the study. ...
... Due to a myriad of sensors available to researchers through mobile devices, and the rare rate of separation from their owners, they are an invaluable resource of rich user data [10,20]. One such research application for datasets harvested in this manner is the training of intelligent systems for management of mobile noti cations [8]. However, obtaining rich datasets, such as those containing mobile noti cations and contextual user data, from mobile devices does pose a number of problems as there are a number of factors which impede the ease of acquisition. ...
... e di culty being that noti cations are inherently personal and generally contain sensitive information which is unethical to record, use and share. Research in the domain of noti cation management has therefore contained, thus far, many ad-hoc mobile applications which capture a limited number of data points relating to user's incoming noti cations and proceeds to abstract this data to preserve its sensitive nature [8,17,18]. e raw information contained within the noti cations is discarded and intelligent systems are trained on abstracted data which tends to highlight the emphasis currently being placed on ensuring the utmost user privacy and protection. ...
... As an alternative to capturing noti cation data in-the-wild a synthetic noti cation dataset is proposed for use in improving a NMS [8]. In order for the dataset to be useful, it must draw parallels with real-world data. ...
Conference Paper
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).
... are inherently ubiquitous and tend to remain within reach of their owners throughout the day [19], the potential for disruption from mobile push-notifications is much higher than from other notification sources such as desktop computers [15]. For this reason, recent research [1,3,7,8,[11][12][13]18] has been conducted on mobile Notification Management Systems (NMS) which aim to block or delay notifications which are not seen as useful or desired, while still allowing important notifications to be delivered immediately. Currently the majority of these state-of-the-art systems are trained using real user notification data collected in-the-wild and implement some form of supervised learning. ...
... With push notification overload marketers have noticed that "users are more likely to respond to a message with information that directly affects them, as compared to a message that was sent to all of the app's users" 9 . However, this might not be enough and several researchers proposed personalised intelligent notification mechanisms for mobile applications [64], [65] to limit notification disruptions -similarly to what we tested with one application only. ...
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  • L L Pipino
  • Y W Lee
  • R Y Wang
L. L. Pipino, Y. W. Lee and R. Y. Wang, " Data Quality Assessment, " Commun. ACM, vol. 45, no. 4, pp. 211-218, Apr 2002.
A context and user aware smart notification system
  • F Corno
  • L D Russis
  • T Montanaro
F. Corno, L. D. Russis and T. Montanaro, "A context and user aware smart notification system," in Internet of Things (WF-IoT), 2015 IEEE 2nd World Forum on, 2015.
  • E Dim
  • T Kuflik
  • I Reinhartz-Berger
E. Dim, T. Kuflik and I. Reinhartz-Berger, "When User Modeling Intersects Software Engineering: The Info-bead User Modeling Approach," User Modeling and User-Adapted Interaction, vol. 25, no. 3, pp. 189-229, Aug 2015.
  • B Sageder
  • K Boegl
  • K.-P Adlassnig
  • G Kolousek
  • B Trummer
B. Sageder, K. Boegl, K.-P. Adlassnig, G. Kolousek and B. Trummer, "The knowledge model of MedFrame/CADIAG-IV.," Studies in health technology and informatics, vol. 43, pp. 629-633, 1996.