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User Experience and Information Architecture
Published in Marcelo M. Soares, Francisco Rebelo, Tareq Z. Ahram, Handbook of Usability and User Experience, 2022
Luiz Agner, Barbara Jane Necyk, Adriano Renzi
It is essential to determine priorities to our attention and interest, create hierarchies, information structures, groups, categories and sequential interaction according to people's affinities and expectations. Information architecture is responsible for creating structures to effectively allow users to transform their informational necessities into actions and reach their goals. In this sense, the traditional information architecture's role is to organize and structure the information to help users discover and consume content as well as facilitate their decisions and actions.
Spreadsheet Tool for Simple Cost-Benefit Analyses of User Experience Engineering
Published in Julie A. Jacko, The Human–Computer Interaction Handbook, 2012
In this task (previously referred to as work reengineering) based on all requirements analysis data and the UX goals extracted from them, user tasks are redesigned at the level of organization and workflow to streamline user tasks and exploit the capabilities of automation. No visual UX design is involved in this task, just abstract organization of functionality and workflow design. The information architecture defines how users will navigate through the information and/or functionality of the application.
Co-simulation of complex engineered systems enabled by a cognitive twin architecture
Published in International Journal of Production Research, 2022
Yuanfu Li, Jinwei Chen, Zhenchao Hu, Huisheng Zhang, Jinzhi Lu, Dimitris Kiritsis
The architecture of CT is divided into three parts: unified ontology modelling, co-simulation, and result reasoning. The architecture of CT is illustrated in Figure 2. The ontology model includes the design information, life information, architecture information, and parameters information mentioned above. Due to the complexity and diversity of information, ontology integrates information according to Classes, Object Properties, and Data properties. The semantic modelling approach should be unified to represent the topology between models of digital entities. A unified ontology modelling approach, named GOPPRR (Graph, Object, Port, Property, Relationship, and Role), is proposed to formalise the ontology modelling process for the co-simulation process. Then, an ontology model is established by the GOPPRR modelling approach. The knowledge needed during co-simulation is formed using the Ontology model to support the automatic integration of digital entities. The co-simulation scenario refers to the co-simulation requirement of the complex engineered system.
Managing digital transformation of smart cities through enterprise architecture – a review and research agenda
Published in Enterprise Information Systems, 2021
The application architecture provides application services centric view of systems that ties business functions and smart services to application components alignment (Atat et al. 2018). The application architecture encompasses applications that process, utilise and transform processed data, analysed data and third-party data (for improvement of smart services or analytics) sources into useful information (Wu et al. 2016; Anthony and Petersen 2019). The application architecture’s is based on the business strategy, standards and scope (Oracle 2009). Information Architecture
A participatory data-centric approach to AI Ethics by Design
Published in Applied Artificial Intelligence, 2022
Focusing on contemporary challenges in data science, Ng stresses the need to “shift our mindset toward not just improving the code but toward a more systematic way of improving the data” (Sagar 2021). Likewise, Kim et al. (2018) empirically investigate challenges and best practices among data scientists and point out that “factors that complicate data understanding include lack of documentation, inconsistent schemas and multiple possible interpretations of data labels” (Kim et al. 2018, 1031). Initiatives such as the previously mentioned datasheets for datasets provide standardized guidelines for dataset documentation, which may improve transparency and accountability and “facilitate better communication between dataset creators and dataset consumers” (Gebru et al. 2020, 1). Correspondingly, in the field of HCI, human-centered approaches to data science are starting to gain traction (Aragon et al. 2016). For example, Seidelin, Dittrich, and Grönvall (2020) show how “data may be foregrounded as an explicit element of design”. The authors outline co-design activities which are didactically designed to facilitate collaborative workshops, which support domain experts in understanding and critically reflecting on data and data structures in a specific database. In this way, co-design activities in collaborative settings serve to empower domain experts and enhance data literacy. However, the authors present a case with challenges related to databased services. Here, domain experts explore and negotiate the meaning of data and data dependencies with the help of a data notation consisting of simple icons representing entities in a database. The co-design activities focus on helping domain experts understand the role of data entities and the information architecture of a database. This contribution is less helpful when tackling challenges that arise in a data-driven ML developmental context. Here, defining the right dataset for an ML project determines whether the project succeeds or fails. Nevertheless, Seidelin, Dittrich, and Grönvall (2020) provide examples of how domain expert empowerment can be facilitated by collaborative activities and stresses the importance of positioning users at the center of system development.