Models in the Design of Context-Aware Well-Being Applications

Bosems, Steven; Sinderen, Marten van

doi:10.1007/978-3-662-45550-0_6

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…The same holds for many R&D CA projects, such as AWARENESS [5]. Bosems and Van Sinderen have considered "context-aware computing" as the combination of sensor, reasoning, and other technology that provides systems with real-time awareness [26] but the "reasoning" has not been explicitly addressed and is mainly related to Event-Condition-Action (ECA) rules [27]. In our view, ECA-rules are only limited to situations that are known at design time.…”

Section: Discussionmentioning

confidence: 99%

Making Enterprise Information Systems Resilient Against Disruptive Events: A Conceptual View

Shishkov

Verbraeck

2020

Lecture Notes in Business Information Processing

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Enterprise Information Systems (EIS) are designed to deal with normal variability in their inputs and data. Empowered by CONTEXT-AWARENESS, some EIS even count on sensors and/or data analytics for capturing changes outside of the system. Nevertheless, context-awareness would often fail when EIS are affected by (large-scale) disruptive events, such as disasters, virus outbreaks, or military conflicts. Hence, in the current paper, we take a step forward, by considering context-awareness for disruptive events. We combine context-awareness with risk management techniques, such as FMECA and FTA, that are useful for defining and mitigating risk events. To avoid having to define the likelihood for such verylow-probability disruptive risks, we use CONSEQUENCE-BASED RISK MAN-AGEMENT rather than traditional risk management. We augment this approach with the context-awareness paradigm, delivering a contribution that is two-fold: (i) We propose context-awareness-related measures and consequence-based-riskmanagement-related measures, to address disruptive events; (ii) We reflect this in a method featuring the application of context-awareness and risk management for designing robust and resilient EIS.

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Section: Discussionmentioning

confidence: 99%

Making Enterprise Information Systems Resilient Against Disruptive Events: A Conceptual View

Shishkov

Verbraeck

2020

Lecture Notes in Business Information Processing

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“…This section introduces the Dynamic Well-being Domain Model (DWDM) language [ 22 , 23 ] and discusses an analysis method to predict the effect of the system on its context.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Domain Behavior through Dynamic Well‐Being Domain Model Analysis

Bosems

Sinderen

2015

The Scientific World Journal

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As the concept of context-awareness is becoming more popular the demand for improved quality of context-aware systems increases too. Due to the inherent challenges posed by context-awareness, it is harder to predict what the behavior of the systems and their context will be once provided to the end-user than is the case for non-context-aware systems. A domain where such upfront knowledge is highly important is that of well-being. In this paper, we introduce a method to model the well-being domain and to predict the effects the system will have on its context when implemented. This analysis can be performed at design time. Using these predictions, the design can be fine-tuned to increase the chance that systems will have the desired effect. The method has been tested using three existing well-being applications. For these applications, domain models were created in the Dynamic Well-being Domain Model language. This language allows for causal reasoning over the application domain. The models created were used to perform the analysis and behavior prediction. The analysis results were compared to existing application end-user evaluation studies. Results showed that our analysis could accurately predict success and possible problems in the focus of the systems, although certain limitation regarding the predictions should be kept into consideration.

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“…As a problem theory for context-aware systems we postulate that end-users (users, for short) of information systems often have different needs for services provided by such systems, where different needs correspond to different context situations. As studied in [13], context-aware (information) systems are a "treatment" for this problem if they can provide context-specific services to users in accordance to their context-dependent needs [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. "Context" here is the context of the context-aware system, where the former is a given (i.e., not designed) and the latter is the object of design.…”

Section: Context-awarenessmentioning

confidence: 99%

Combining Context-Awareness and Data Analytics in Support of Drone Technology

Shishkov

Ivanova

Verbraeck

et al. 2022

Communications in Computer and Information Science

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Drones performing an autonomous mission need to adapt to frequent changes in their environment. In other words, they have to be context-aware. Most current context-aware systems are designed to distinguish between situations that have been pre-defined in terms of anticipated situation types and corresponding desired behavior types. This only partially benefits drone technology because many types of drone missions can be characterized by situations that are hard to predict at design time. We suggest combining context-awareness and data analytics for a better situation coverage. This could be achieved by using performance data (generated at real-time) as training data for supervised machine learning -it would allow relating situations to appropriate behaviors that a drone could follow. The conceptual ideas are presented in this position paper while validation is left for future work.

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Models in the Design of Context-Aware Well-Being Applications

Cited by 11 publications

References 10 publications

Making Enterprise Information Systems Resilient Against Disruptive Events: A Conceptual View

Making Enterprise Information Systems Resilient Against Disruptive Events: A Conceptual View

Prediction of Domain Behavior through Dynamic Well‐Being Domain Model Analysis

Combining Context-Awareness and Data Analytics in Support of Drone Technology

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