Generation of Adaptive Mobile Applications Based on Design Patterns for User Interfaces

Braham, Amani; Buendía, Félix; Khemaja, Maha; Gargouri, Faı̈ez

doi:10.3390/proceedings2019031019

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…DriverSense is implemented on the Android platform and adapts smartphone user interfaces based on real-time contextual factors such as driver preferences, environmental factors, and device usage using adaptation rules. In contrast, Braham et al (2019) presented a framework that combines ontology-based models with UI design pattern methods to create adaptive mobile apps. Their proposed framework allows for the dynamic collection of appropriate UI design patterns based on user needs.…”

Section: Related Workmentioning

confidence: 99%

“…The best solution to the above-mentioned issues is through the Self-Adaptive User-Interface (SAUI) (Machado et al 2018). SAUI can be viewed as an interface that adjusts its structure or content based on the needs and usage contexts (Braham et al 2019). The SAUI aims to enhance the user and UI interaction by adapting aspects, including device layout distribution and available actions, to the user's current goals and needs (Machado et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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A conceptual framework for context-driven self-adaptive intelligent user interface based on Android

Ali,

Khan,

Mashkoor

et al. 2024

Cogn Tech Work

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Adaptive User Interface (AUI) can change its layout, appearance, and/or elements based on the needs of its user requirements and current usage context. The AUIs are used in state-of-the-art software products, applications for mobile devices, and websites. Moreover, AUI is an emerging research field in a mobile context, as it can enhance usability, performance, and user satisfaction. This study aims to propose a conceptual framework for developing a real-time self-adaptive user interface based on the Android Operating System (OS). Furthermore, the focus is on developing the core algorithms for the modules of the proposed framework. To evaluate the performance of the proposed framework, three case studies have been designed based on the daily and weekly activities of the user. Moreover, an expert-based validation approach is employed to obtain the expert’s feedback regarding the proposed framework. The result indicates that the proposed framework helps improve user satisfaction and experience by making an intelligent mobile device interface. The results of the framework’s evaluation and validation show the proposed framework’s feasibility and effectiveness. We conclude that the current work is beneficial in filling the identified research gap. Moreover, this research shows the significance of an adaptive interface in an Android OS-based context. In addition, it not only helps in improving the user interest and satisfaction but also enhances the overall performance of the mobile device.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A conceptual framework for context-driven self-adaptive intelligent user interface based on Android

Ali,

Khan,

Mashkoor

et al. 2024

Cogn Tech Work

View full text Add to dashboard Cite

show abstract

“…An adaptive interface needs to be a smart solution that can learn new contexts, sense existing contexts and modify itself according to any unknown context. Formal ontology is a method to develop such context knowledge-base [22]. It has ability to keep all the context picture consistent, standardized, shared and scalable [23].…”

Section: Problem Statementmentioning

confidence: 99%

“…The selected systems contain different applications of context-aware behavior. For example, cyber guide, context toolkit, active badge, NETMAN and augmentable reality have context types (identity, location) and context-aware types (presentation and tagging) [28]. Open Service Gateway Initiative (OSGi) provided secure service delivery and reliable remote management of context aware mobile services.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Ontologies are used to express these concepts formally to define a computational model. Ontology driven information systems approach is being used to design the application in human's perspective [22]. Thus, we are exploiting the benefits of ontologies to formally state the usage context of adaptive user interfaces.…”

Section: Ontological Representation Of Contextmentioning

confidence: 99%

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User Context Ontology for Adaptive Mobile-Phone Interfaces

Iqbal

Nickabadi²,

Shahzad

et al. 2021

IEEE Access

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The Adaptive User Interface (AUI) adapts to the changes in the context of use and provides improved interaction abilities for different users. The adaptivity in the user interfaces requires in depth knowledge of context. There is a need to enrich user profiles to achieve the personalized services with the ability to adapt the user's context. The context can be reflected in a particular kind of knowledge and hence modeled as ontology. Ontology based context models are effective means to handle complex situations that support the sharing or integration of context information. This paper presents ontology based context model using OWL for adaptive mobile devices. It models the context over its four major elements including device, user, environment (location and time) and activity. The proposed ontology was derived in different classes, relationships, associations, dependencies and constraints to model dynamic context. Ontologies present a standardized, consistent and shareable context model. The context model and consequent context snapshots can be acknowledged by AUI to present a suitable user interface. The ontology was developed using Protégé on the basis of each context type having different values. Semantic querying (SPARQL) was used for knowledge acquisition. Moreover, the Pellet and HermiT Reasoner were used to verify the rules, relations and constraints to avoid the inconsistency between classes. Comparative to other context models for adaptive interfaces, ontological model provides more of scalability and growth with learning new context in to the shared context knowledge.

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User interface design patterns and ontology models for adaptive mobile applications

Braham

Buendía

Khemaja

et al. 2021

Pers Ubiquit Comput

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Generation of Adaptive Mobile Applications Based on Design Patterns for User Interfaces

Cited by 6 publications

References 24 publications

A conceptual framework for context-driven self-adaptive intelligent user interface based on Android

A conceptual framework for context-driven self-adaptive intelligent user interface based on Android

User Context Ontology for Adaptive Mobile-Phone Interfaces

User interface design patterns and ontology models for adaptive mobile applications

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