Modeling Cost of Interruption (COI) to Manage Unwanted Interruptions for Mobile Devices

Unwanted and untimely interruptions have been a major cause in the loss of productivity in recent years. It has been found that they are mostly detrimental to the immediate task at hand. Multiple approaches have been proposed to address the problem of interruption by calculating cost of it. The Cost Of Interruption (COI) gives a measure of the probabilistic value of harmfulness of an inopportune interruption. Bayesian Inference stands as the premier model so far to calculate this COI. However, Bayesian-based models suffer from not being able to model context accurately in situations where a priori, conditional probabilities and uncertainties exist while utilizing context information. Hence, this thesis introduces the Dempster-Shafer Theory of Evidence to model COI. Along the way, it identifies specific contexts that are necessary to take into account. Simulation results and performance evaluation suggest that this is a very good approach to decision making. The thesis also discusses an illustrative example of a mobile interruption management application where the Dempster-Shafer theory is used to get a better measurement of whether or not to interrupt

A Context-aware Cost of Interruption Model for Mobile Devices

Unwanted and untimely interruptions have been a major cause in the loss of productivity in recent years as they are mostly detrimental to the immediate task at hand. Multiple approaches have been proposed to address the problem of interruption by calculating cost of interruption. The cost of interruption (COI) gives as a measure the probabilistic value of harmfulness of an inopportune interruption. Bayesian Inference stands atop among the models that have been applied to calculate this COI. However Bayesian inference based models suffer from not being able to model context accurately in situations where priori, conditional probabilities and uncertainties exist while utilizing context information. Hence, this paper introduces Dempster-Shafer Theory of Evidence to model COI. Along the way, we also identify different contexts necessary for interruption management applications. We also show an illustrative example of a mobile interruption management application where the Dempster-Shafer theory is used to get a better measurement of whether to interrupt or not

A Context Aware Interruption Management System for Mobile Devices

To prevent unwanted interruptions from cell phones, this paper proposes a system solution considering user’s unavailability. We first look at desirable characteristics of the system, then design a system architecture which takes as input user preferences, relevant context information and then produces as output if an incoming call should be allowed to ring. We also present a case study application that benefit by using the interruption management system. Finally, we discuss evaluations of the system by (i) evaluating the prototype and (ii) undertaking cognitive walkthroughs of the application

A Mobile Intelligent Interruption Management System

Abstract: Mobile phones have become the most hated device that people cannot live without. For its primary usage as a communication device, it has surpassed any other medium. But it comes with a high price, interruption, anywhere anytime. These unwanted interruptions cause loss of productivity and also mostly not beneficial to the immediate task at hand, and moving them few minutes into the future can increase productivity. Considering receiver’s unavailability, it is possible to manage cell phone disruptions using advanced features like sensing capability, ubiquitous computing and context aware systems. This paper proposes the architecture of a system named Mobile Intelligent Interruptions Management (MIIM), created for the automated administration of personal unavailability with regard to cell phones. We provide the problem description of interruption and its impact. Next, we state the desirable characteristics and architecture of the MIIM system. We also provide a case study implementation of MIIM system on the Android platform. Simulation and evaluation results show that its computational volumes are low enough for a mobile device. The analysis of the system also successfully satisfies all the characteristics requirements