Engineering Adaptive Model-Driven User Interfaces for Enterprise Applications

Enterprise applications such as enterprise resource planning systems have numerous complex user interfaces (UIs). Usability problems plague these UIs because they are offered as a generic off-the-shelf solution to end-users with diverse needs in terms of their required features and layout preferences. Adaptive UIs can help in improving usability by tailoring the features and layout based on the context-of-use. The model-driven UI development approach offers the possibility of applying different types of adaptations on the various UI levels of abstraction. This approach forms the basis for many works researching the development of adaptive UIs. Yet, several gaps were identified in the state-of-the-art adaptive model-driven UI development systems. To fill these gaps, this thesis presents an approach that offers the following novel contributions: - The Cedar Architecture serves as a reference for developing adaptive model-driven enterprise application user interfaces. - Role-Based User Interface Simplification (RBUIS) is a mechanism for improving usability through adaptive behavior, by providing end-users with a minimal feature-set and an optimal layout based on the context-of-use. - Cedar Studio is an integrated development environment, which provides tool support for building adaptive model-driven enterprise application UIs using RBUIS based on the Cedar Architecture. The contributions were evaluated from the technical and human perspectives. Several metrics were established and applied to measure the technical characteristics of the proposed approach after integrating it into an open-source enterprise application. Additional insights about the approach were obtained through the opinions of industry experts and data from real-life projects. Usability studies showed the approach’s ability to significantly improve usability in terms of end-user efficiency, effectiveness and satisfaction

Considering the User in the Wireless World

The near future promises significant advances in communication capabilities, but one of the keys to success is the capability understanding of the people with regards to its value and usage. In considering the role of the user in the wireless world of the future, the Human Perspective Working Group (WG1) of the Wireless World Research Forum has gathered input and developed positions in four important areas: methods, processes, and best practices for user-centered research and design; reference frameworks for modeling user needs within the context of wireless systems; user scenario creation and analysis; and user interaction technologies. This article provides an overview of WG1's work in these areas that are critical to ensuring that the future wireless world meets and exceeds the expectations of people in the coming decades

RBUIS: simplifying enterprise application user interfaces through engineering role-based adaptive behavior

Enterprise applications such as customer relationship management (CRM) and enterprise resource planning (ERP) are very large scale, encompassing millions of lines-of-code and thousands of user interfaces (UI). These applications have to be sold as feature-bloated off-the-shelf products to be used by people with diverse needs in required feature-set and layout preferences based on aspects such as skills, culture, etc. Although several approaches have been proposed for adapting UIs to various contexts-of-use, little work has focused on simplifying enterprise application UIs through engineering adaptive behavior. We define UI simplification as a mechanism for increasing usability through adaptive behavior by providing users with a minimal feature-set and an optimal layout based on the context-of-use. In this paper we present Role-Based UI Simplification (RBUIS), a tool supported approach based on our CEDAR architecture for simplifying enterprise application UIs through engineering role-based adaptive behavior. RBUIS is integrated in our general-purpose platform for developing adaptive model-driven enterprise UIs. Our approach is validated from the technical and end-user perspectives by applying it to developing a prototype enterprise application and user-testing the outcome

Responsive and Personalized Web Layouts with Integer Programming

Over the past decade, responsive web design (RWD) has become the de facto standard for adapting web pages to a wide range of devices used for browsing. While RWD has improved the usability of web pages, it is not without drawbacks and limitations: designers and developers must manually design the web layouts for multiple screen sizes and implement associated adaptation rules, and its "one responsive design fits all"approach lacks support for personalization. This paper presents a novel approach for automated generation of responsive and personalized web layouts. Given an existing web page design and preferences related to design objectives, our integer programming -based optimizer generates a consistent set of web designs. Where relevant data is available, these can be further automatically personalized for the user and browsing device. The paper includes presentation of techniques for runtime adaptation of the designs generated into a fully responsive grid layout for web browsing. Results from our ratings-based online studies with end users (N = 86) and designers (N = 64) show that the proposed approach can automatically create high-quality responsive web layouts for a variety of real-world websites.Peer reviewe

Next challenges for adaptive learning systems

Learning from evolving streaming data has become a 'hot' research topic in the last decade and many adaptive learning algorithms have been developed. This research was stimulated by rapidly growing amounts of industrial, transactional, sensor and other business data that arrives in real time and needs to be mined in real time. Under such circumstances, constant manual adjustment of models is in-efficient and with increasing amounts of data is becoming infeasible. Nevertheless, adaptive learning models are still rarely employed in business applications in practice. In the light of rapidly growing structurally rich 'big data', new generation of parallel computing solutions and cloud computing services as well as recent advances in portable computing devices, this article aims to identify the current key research directions to be taken to bring the adaptive learning closer to application needs. We identify six forthcoming challenges in designing and building adaptive learning (pre-diction) systems: making adaptive systems scalable, dealing with realistic data, improving usability and trust, integrat-ing expert knowledge, taking into account various application needs, and moving from adaptive algorithms towards adaptive tools. Those challenges are critical for the evolving stream settings, as the process of model building needs to be fully automated and continuous.</jats:p