Context-Based Assignment and Execution of Human-centric Mobile Services

Pryss, Rüdiger; Reichert, Manfred; Schickler, Marc; Bauer, Thomas

doi:10.1109/mobserv.2016.12

Cited by 6 publications

(7 citation statements)

References 18 publications

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“…This enables, for instance, late selection and late modelling of process fragments (App-2c). (Pryss, Reichert, Schickler, & Bauer, 2016) offers some flexibility at the selection of actors that use mobile clients (App-1c). The potential actors of an activity may be selected based on the type of the mobile device, its current location, or its charging state.…”

Section: Related Workmentioning

confidence: 99%

Business Processes with Pre-modelled Flexibility: Examples and Requirements for Various Process Aspects

2019

AJM

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At process-aware information systems (PAIS), sometimes, a flexible deviation from the rigidly designed process becomes necessary. Otherwise the users would be restricted too much. This paper presents an approach that allows to define the expected flexibility requirements only once already at build-time and apply them at run-time in the PAIS. Compared to dynamic changes during run-time, this has the advantage that the usage of the pre-defined information reduces the effort for the end users at each deviation. In addition, applying flexibility becomes saver; e.g., since user rights can be defined.

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Section: Related Workmentioning

confidence: 99%

Business Processes with Pre-modelled Flexibility: Examples and Requirements for Various Process Aspects

2019

AJM

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“…Since worklist management plays an important role for the exception handling service, we conceive the basic technical aspects behind the management of worklists [5]. To properly assign mobile activities to mobile users, a process client must be provided, which needs to be deployed to the users' mobile devices.…”

Section: Human-centric Mobile Servicesmentioning

confidence: 99%

“…For example, if a mobile activity shall be executed, the battery status of all mobile users will be gathered. A detailed discussion of all parameters can be found in [5].…”

Section: Mobile Contextmentioning

confidence: 99%

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Context-Based Prevention and Handling of Exceptions for Human-Centric Mobile Services

Pryss

Reichert

2017

2017 IEEE International Conference on AI &Amp; Mobile Services (AIMS)

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Using smart mobile devices to support humancentric services is a frequent demand in business scenarios. As a particular challenge, tasks performed in a paper-driven way shall be digitally transformed with the use of mobile devices. With the goal to enable business applications supporting human-centric mobile services in mind, we developed a framework that extends existing process management technology with mobile activities running on smart mobile devices. Note that when considering the frequently changing conditions of mobile environments, the prevention and the proper handling of exceptions (e.g., lost connections) become crucial. The developed framework, therefore, aims to prevent exceptions and to provide a sophisticated exception handling service not supported by existing process management technology so far.

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“…Furthermore, the authors of [3] showed that electronic versions do not affect psychometric properties of questionnaires, but, in turn, enhance the overall quality of the data collected [4]. Finally, digital versions allow for more comprehensive datasets [5] through the application of automatic validation rules or the opportunity to add context information like the time or a location when using smartphones [6]. In addition, the built-in sensors of smartphones can be used to gather even more valuable data, e.g., by measuring vital parameters during an interview [7].…”

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confidence: 99%

Measuring Mental Effort for Creating Mobile Data Collection Applications

Schobel

Probst

Reichert

et al. 2020

IJERPH

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To deal with drawbacks of paper-based data collection procedures, the QuestionSys approach empowers researchers with none or little programming knowledge to flexibly configure mobile data collection applications on demand. The mobile application approach of QuestionSys mainly pursues the goal to mitigate existing drawbacks of paper-based collection procedures in mHealth scenarios. Importantly, researchers shall be enabled to gather data in an efficient way. To evaluate the applicability of QuestionSys, several studies have been carried out to measure the efforts when using the framework in practice. In this work, the results of a study that investigated psychological insights on the required mental effort to configure the mobile applications are presented. Specifically, the mental effort for creating data collection instruments is validated in a study with N = 80 participants across two sessions. Thereby, participants were categorized into novices and experts based on prior knowledge on process modeling, which is a fundamental pillar of the developed approach. Each participant modeled 10 instruments during the course of the study, while concurrently several performance measures are assessed (e.g., time needed or errors). The results of these measures are then compared to the self-reported mental effort with respect to the tasks that had to be modeled. On one hand, the obtained results reveal a strong correlation between mental effort and performance measures. On the other, the self-reported mental effort decreased significantly over the course of the study, and therefore had a positive impact on measured performance metrics. Altogether, this study indicates that novices with no prior knowledge gain enough experience over the short amount of time to successfully model data collection instruments on their own. Therefore, QuestionSys is a helpful instrument to properly deal with large-scale data collection scenarios like clinical trials.

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Context-Based Assignment and Execution of Human-centric Mobile Services

Cited by 6 publications

References 18 publications

Business Processes with Pre-modelled Flexibility: Examples and Requirements for Various Process Aspects

Business Processes with Pre-modelled Flexibility: Examples and Requirements for Various Process Aspects

Context-Based Prevention and Handling of Exceptions for Human-Centric Mobile Services

Measuring Mental Effort for Creating Mobile Data Collection Applications

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