OpenUIDL, A User Interface Description Language for Runtime Omni-Channel User Interfaces

2020 ◽  
Vol 4 (EICS) ◽  
pp. 1-52
Author(s):  
Alex Moldovan ◽  
Vlad Nicula ◽  
Ionut Pasca ◽  
Mihai Popa ◽  
Jaya Krishna Namburu ◽  
...  
Keyword(s):  
User Interface ◽  
User Interfaces ◽  
Description Language ◽  
User Interface Description Language

scholarly journals Workflow User Interfaces Patterns

2012 ◽  
Vol 22 ◽  
pp. 62-68
Author(s):  
Josefina Guerrero-García ◽  
Juan Manuel González-Calleros ◽  
Jean Vanderdonckt
Keyword(s):  
Life Cycle ◽  
Information Systems ◽  
User Interface ◽  
User Interfaces ◽  
Interface Design ◽  
Design Patterns ◽  
Markup Language ◽  
Description Language ◽  
Workflow Pattern ◽  
User Interface Description Language

A collection of user interface design patterns for workflow information systems is presented that contains forty three resource patterns classified in seven categories. These categories and their corresponding patterns have been logically identified from the task life cycle based on offering and allocation operations. Each Workflow User Interface Pattern (WUIP) is characterized by properties expressed in the PLML markup language for expressing patterns and augmented by additional attributes and models attached to the pattern: the abstract user interface and the corresponding task model. These models are specified in a User Interface Description Language. All WUIPs are stored in a library and can be retrieved within a workflow editor that links each workflow pattern to its corresponding WUIP, thus giving rise to a user interface for each workflow pattern.

MULTI-VALUED CONSTANT PROPAGATION ANALYSIS FOR USER INTERFACE REENGINEERING

1995 ◽  
Vol 05 (01) ◽  
pp. 5-23 ◽  
Author(s):  
ETTORE MERLO ◽  
JEAN-FRANCOIS GIRARD ◽  
LAURIE HENDREN ◽  
R. DE MORI
Keyword(s):  
User Interface ◽  
Description Language ◽  
Experimental Context ◽  
Propagation Analysis ◽  
And Behavior ◽  
Interface Structures ◽  
User Interface Description Language ◽  
Constant Propagation

The definition and use of multi-valued constant propagation analysis (MVCP), which is an extension of simple constant propagation analysis, is presented in this paper in the context of a user interface reengineering process. A brief description of the adopted COBOL/CICS user interface reengineering model, which makes use of an Abstract User Interface Description Language (AUIDL) to represent user interface structures and behavior, is also given. The experimental context is described and results are shown and discussed. Suggestions for further directions of research and investigation are also presented.

scholarly journals Effects of User Interfaces on Take-Over Performance: A Review of the Empirical Evidence

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 162
Author(s):  
Soyeon Kim ◽  
René van Egmond ◽  
Riender Happee
Keyword(s):  
User Interface ◽  
Situation Awareness ◽  
User Interfaces ◽  
Situational Awareness ◽  
Selection Criterion ◽  
Auditory Modality ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Positive Effects ◽  
High Urgency

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios.

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