Anticipatory Mobile Computing

Pejović, Veljko; Musolesi, Mirco

doi:10.1145/2693843

Cited by 139 publications

(22 citation statements)

References 113 publications

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“…In addition, Lee and Lee [2012] use the location prediction to support ubiquitous decision support. Rosa et al [2016] highlighted recent studies that discuss and classify works in research topics related to context prediction, such as anticipatory mobile computing [Pejovic and Musolesi 2015] and prediction in pervasive computing [Ameyed et al 2015]. These surveys present and compare context prediction techniques and propose solutions, particularly highlighting challenges and opportunities associated with this research field.…”

Section: Context Prediction Manager (Future)mentioning

confidence: 99%

Multi-Temporal Aspects on Contextual Variability Modeling

Barbosa

2019

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP)

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Time is one of the most relevant aspects when we model contextual variability. The temporal perspective guides the modeling of context-aware systems. Despite natural and consensual perception of time, the integrated modeling of its dimensions for developing context-aware software is a recent theme of study. The Past is stored in Contexts Histories, the Present is modeled through Profile Management and the Future is anticipated using Context Prediction. This article discusses the modeling of these three dimensions in the context-aware systems, indicates challenges to each dimension and proposes a system architecture to manage the contextual variability on multi-temporal systems. I believe that this text can be a seminal article to stimulate and guide future researches on temporal aspects of computational environments.

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Section: Context Prediction Manager (Future)mentioning

confidence: 99%

Multi-Temporal Aspects on Contextual Variability Modeling

Barbosa

2019

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP)

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“…Anticipatory computing: Using your mobile as a crystal ball. Anticipatory mobile computing [56] builds on our current capabilities to sense, model, and even predict context to construct algorithms and mechanisms that enable automated decision making processes based on these sensing and prediction capabilities. Anticipatory computing applications have long been the holy grail of mobile and pervasive computing.…”

Section: Challenges (And Opportunities) Aheadmentioning

confidence: 99%

Software engineering for mobility: reflecting on the past, peering into the future

Picco

Julien

Murphy

et al. 2014

Future of Software Engineering Proceedings

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At the end of the second millennium, mobility was a hot research topic. Physical mobility of devices was becoming commonplace with the availability of cheap wireless cards, the first attempts to transform phones into personal do-it-all devices were beginning to appear, and mobile ad hoc networks were attracting a huge interest from many research communities. Logical mobility of code was still going strong as a design option for distributed systems, with the Java language providing some of the ready-to-use building blocks. In 2000, when we put forth a research "roadmap" for software engineering for mobility, the challenges posed by this dynamic scenario were many.A decade and a half later, many things have changed. Mobility is no longer exotic: we juggle multiple personal devices every day while on the move, plus we grab and update applications on a whim from virtual stores. Indeed, some trends and visions we considered in our original paper materialized, while others faded, disappeared, or morphed into something else. Moreover, some players unexpected at the time (e.g., cloud computing and online social networks) appeared on the scene as game changers.In this paper we revisit critically our original vision, reflecting on the past and peering into the future of the lively and exciting research area of mobility. Further, we ask ourselves to what extent the software engineering community is still interested in taking up the challenges mobility bears.

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“…A number of recent survey articles investigated attention management systems indicating the rising importance of this field. The existing reviews incorporated concepts from cognitive sciences [96], or focused on technical realizations of anticipatory mobile computing, including interruptions as part of personalized interactions [83]. The survey of interruptibility prediction in ubiquitous systems by Turner et al is the one most related to our work.…”

Section: Introductionmentioning

confidence: 99%

“…We refer the reader to Section 4.3 of[83], which summarizes the most commonly used features in different inference domains.Proc. ACM Interact.…”

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confidence: 99%

A Survey of Attention Management Systems in Ubiquitous Computing Environments

Anderson

Hübener

Seipp

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Today's information and communication devices provide always-on connectivity, instant access to an endless repository of information, and represent the most direct point of contact to almost any person in the world. Despite these advantages, devices such as smartphones or personal computers lead to the phenomenon of attention fragmentation, continuously interrupting individuals' activities and tasks with notifications. Attention management systems aim to provide active support in such scenarios, managing interruptions, for example, by postponing notifications to opportune moments for information delivery. In this article, we review attention management system research with a particular focus on ubiquitous computing environments. We first examine cognitive theories of attention and extract guidelines for practical attention management systems. Mathematical models of human attention are at the core of these systems, and in this article, we review sensing and machine learning techniques that make such models possible. We then discuss design challenges towards the implementation of such systems, and finally, we investigate future directions in this area, paving the way for new approaches and systems supporting users in their attention management.:2 • C. Anderson et al. Key takeaway points⋆ Attention is captured and steered by external and internal stimuli, while stimulus properties, such as its duration, location, intensity, etc., impact the user's reaction to the stimulus. Multimodal alert types, e.g., sound, light, vibration, and multiple device environments call for a coordinated judicious use of alerting in ubiquitous computing. ⋆ Limited cognitive capacities and threaded task processing imply that, in order to minimize disruptions, interruptions need to arrive at task boundaries or during routine tasks, should allow for task state rehearsal, and support context retrieval through hints presented to the user after an interruption. ⋆ Sensor data from ubiquitous computing devices reveals a user's location, physical activity, collocation with other people, and other information of a user's context that can be related to interruptibility. Next generation wearable devices and personalized machine learning models promise to bring us closer to direct inference of a user's cognitive processes. ATTENTION AND INTERRUPTION: DEFINITIONS AND STRATEGIESIn this section, we review definitions of the terms attention and interruption. We examine the connection between attention shifting and interruptions and discuss how interruptions can be handled through attention management systems in ubiquitous environments. What is Attention?There is no common understanding of attention in the literature. Attention is often considered as selective processing of incoming sensory information [33], with limited capacity [23] and reactive and deliberate processes [92]. Attention is also referred to as the ability to ignore irrelevant information [24]. The process of selecting stimuli can be voluntary or be steered by external events. The former ty...

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Anticipatory Mobile Computing

Cited by 139 publications

References 113 publications

Multi-Temporal Aspects on Contextual Variability Modeling

Multi-Temporal Aspects on Contextual Variability Modeling

Software engineering for mobility: reflecting on the past, peering into the future

A Survey of Attention Management Systems in Ubiquitous Computing Environments

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