A3 thinking approach to support knowledge-driven design

Problem solving is a crucial skill in product development. Any lack of effective decision making at an early design stage will affect productivity and increase costs and the lead time for the other stages of the product development life cycle. This could be improved by the use of a simple and informative approach which allows the designers and engineers to make decisions in product design by providing useful knowledge. This paper presents a novel A3 thinking approach to problem solving in product design, and provides a new A3 template which is structured from a combination of customised elements (e.g. the 8 Disciplines approach) and reflection practice. This approach was validated using a case study in the Electromagnetic Compatibility (EMC) design issue for an automotive electrical sub-assembly product. The main advantage of the developed approach is to create and capture the useful knowledge in a simple manner. Moreover, the approach provides a reflection section allowing the designers to turn their experience of design problem solving into proper learning and to represent their understanding of the design solution. These will be systematically structured (e.g. as a design checklist) to be circulated and shared as a reference for future design projects. Thus, the recurrence of similar design problems will be prevented and will aid the designers in adopting the expected EMC test results

Managing contextual information in semantically-driven temporal information systems

Context-aware (CA) systems have demonstrated the provision of a robust solution for personalized information delivery in the current content-rich and dynamic information age we live in. They allow software agents to autonomously interact with users by modeling the user’s environment (e.g. profile, location, relevant public information etc.) as dynamically-evolving and interoperable contexts. There is a flurry of research activities in a wide spectrum at context-aware research areas such as managing the user’s profile, context acquisition from external environments, context storage, context representation and interpretation, context service delivery and matching of context attributes to users‘ queries etc. We propose SDCAS, a Semantic-Driven Context Aware System that facilitates public services recommendation to users at temporal location. This paper focuses on information management and service recommendation using semantic technologies, taking into account the challenges of relationship complexity in temporal and contextual information

MOBILITY ANALYSIS AND PROFILING FOR SMART MOBILITY SERVICES: A BIG DATA DRIVEN APPROACH. An Integration of Data Science and Travel Behaviour Analytics

Smart mobility proved to be an important but challenging component of the smart cities paradigm. The increased urbanization and the advent of sharing economy require a complete digitalisation of the way travellers interact with the mobility services. New sharing mobility services and smart transportation models are emerging as partial solutions for solving some tra c problems, improve the resource e ciency and reduce the environmental impact. The high connectivity between travellers and the sharing services generates enormous quantity of data which can reveal valuable knowledge and help understanding complex travel behaviour. Advances in data science, embedded computing, sensing systems, and arti cial intelligence technologies make the development of a new generation of intelligent recommendation systems possible. These systems have the potential to act as intelligent transportation advisors that can o er recommendations for an e cient usage of the sharing services and in uence the travel behaviour towards a more sustainable mobility. However, their methodological and technological requirements will far exceed the capabilities of today's smart mobility systems. This dissertation presents a new data-driven approach for mobility analysis and travel behaviour pro ling for smart mobility services. The main objective of this thesis is to investigate how the latest technologies from data science can contribute to the development of the next generation of mobility recommendation systems. Therefore, the main contribution of this thesis is the development of new methodologies and tools for mobility analysis that aim at combining the domain of transportation engineering with the domain of data science. The addressed challenges are derived from speci c open issues and problems in the current state of the art from the smart mobility domain. First, an intelligent recommendation system for sharing services needs a general metric which can assess if a group of users are compatible for speci c sharing solutions. For this problem, this thesis presents a data driven indicator for collaborative mobility that can give an indication whether it is economically bene cial for a group of users to share the ride, a vehicle or a parking space. Secondly, the complex sharing mobility scenarios involve a high number of users and big data that must be handled by capable modelling frameworks and data analytic platforms. To tackle this problem, a suitable meta model for the transportation domain is created, using the state of the art multi-dimensional graph data models, technologies and analytic frameworks. Thirdly, the sharing mobility paradigm needs an user-centric approach for dynamic extraction of travel habits and mobility patterns. To address this challenge, this dissertation proposes a method capable of dynamically pro ling users and the visited locations in order to extract knowledge (mobility patterns and habits) from raw data that can be used for the implementation of shared mobility solutions. Fourthly, the entire process of data collection and extraction of the knowledge should be done with near no interaction from user side. To tackle this issue, this thesis presents practical applications such as classi cation of visited locations and learning of users' travel habits and mobility patterns using historical and external contextual data

Studying and Modeling the Connection between People's Preferences and Content Sharing

People regularly share items using online social media. However, people's decisions around sharing---who shares what to whom and why---are not well understood. We present a user study involving 87 pairs of Facebook users to understand how people make their sharing decisions. We find that even when sharing to a specific individual, people's own preference for an item (individuation) dominates over the recipient's preferences (altruism). People's open-ended responses about how they share, however, indicate that they do try to personalize shares based on the recipient. To explain these contrasting results, we propose a novel process model of sharing that takes into account people's preferences and the salience of an item. We also present encouraging results for a sharing prediction model that incorporates both the senders' and the recipients' preferences. These results suggest improvements to both algorithms that support sharing in social media and to information diffusion models.Comment: CSCW 201

Why not empower knowledge workers and lifelong learners to develop their own environments?

In industrial and educational practice, learning environments are designed and implemented by experts from many different fields, reaching from traditional software development and product management to pedagogy and didactics. Workplace and lifelong learning, however, implicate that learners are more self-motivated, capable, and self-confident in achieving their goals and, consequently, tempt to consider that certain development tasks can be shifted to end-users in order to facilitate a more flexible, open, and responsive learning environment. With respect to streams like end-user development and opportunistic design, this paper elaborates a methodology for user-driven environment design for action-based activities. Based on a former research approach named 'Mash-Up Personal Learning Environments'(MUPPLE) we demonstrate how workplace and lifelong learners can be empowered to develop their own environment for collaborating in learner networks and which prerequisites and support facilities are necessary for this methodology

Content Reuse and Interest Sharing in Tagging Communities

Tagging communities represent a subclass of a broader class of user-generated content-sharing online communities. In such communities users introduce and tag content for later use. Although recent studies advocate and attempt to harness social knowledge in this context by exploiting collaboration among users, little research has been done to quantify the current level of user collaboration in these communities. This paper introduces two metrics to quantify the level of collaboration: content reuse and shared interest. Using these two metrics, this paper shows that the current level of collaboration in CiteULike and Connotea is consistently low, which significantly limits the potential of harnessing the social knowledge in communities. This study also discusses implications of these findings in the context of recommendation and reputation systems.Comment: 6 pages, 6 figures, AAAI Spring Symposium on Social Information Processin