Decision Support for Smart City and Digital Services Minitrack
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Developing smart city and enhancing e-society services are the critical important to urbanization process for improving the effectiveness and efficiency of traditional cities. With the massive applications of Internet of things (IoT), mobile networks, and social networks, unprecedentedly large amount of various heterogeneous data can be gathered and processed in terms of advanced analytics to support smart applications and e-society services. Furthermore, decision support tools and soft computing models can be employed to speed up the whole process.
This minitrack addresses issues that focus on the applications of various decision support tools, such as big data analytics, decision analysis, and soft computing, to develop smart city applications and e-society services. We also encourage papers to report on system level research and case studies related to smart city and e-society. Topics of interest include, but are not limited to:
- Advanced analytics for smart city planning and e-society services
- Case study and best practices for smart cities and e-society services
- Decision support models and tools for smart city and e-society services
- Design and implementation of intelligent systems for smart city applications
- Innovative applications in smart cities, such as smart finance, smart health, smart research, and smart travel
- Novel applications in e-society services, such as information recommendation, social media analytics, and crowdsourcing applications
- Soft computing for smart city and e-society services
Minitrack Co-Chairs:
Wei Xu (Primary Contact)
Renmin University of China
Email: weixu@ruc.edu.cn
Jian Ma
City University of Hong Kong
Email: isjian@cityu.edu.hk
Jianshan Sun
Hefei University of Technology
Email: sunjs9413@gmail.com
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Recent Submissions
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ItemThe Development of a Smart Map for Minimum "Exertion" Routing Applications( 2017-01-04)The problem of minimum cost routing has been extensively explored in a variety of contexts. While there is a prevalence of routing applications based on least distance, time, and related attributes, exertion-based routing has remained relatively unexplored. In particular, the network structures traditionally used to construct minimum cost paths are not suited to representing exertion or finding paths of least exertion based on road gradient. In this paper, we introduce a topographical network or “topograph” that enables minimum cost routing based on the exertion metric on each arc in a given road network as it is related to changes in road gradient. We describe an algorithm for topograph construction and present the implementation of the topograph on a road network of the state of California with ~22 million nodes.
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ItemModel Design and Implementation of Enterprise Credit Information Based On Data Mining( 2017-01-04)Smart city construction emphasizes building an effective whole social credit system, promoting the construction of government integrity, business integrity, social integrity and public confidence in the judiciary. For credit risk has become an important assessment. Meanwhile, administration credit system is one of three major data. It proposes unified credit discipline and warning regulatory purposes, led by the government and its main functions, taken governmental data as the main basis. Accordingly, the paper constructs Corporate Dishonest Credit Executed (CDCE) Risk Assessment Model, based on governmental data. The model uses a set of urban enterprise data, selecting the explanatory variables from five aspects, administrative punishment, innovation, credit information, credit situation, and social responsibility, to screen CDCE Logit regression, to filter out and find out those variables which are significantly predicted effects for CDCE risk. And then construct a Logit regression model with the above selected variables. The experimental results and comparison of practical applications in China, we found that the model promises to higher business risk identification accuracy for CDCE. The model has a higher applied value and developmental prospects.
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ItemInvestigating the Relationship among Characteristics of Social Commerce, Consumers’ Trust and Trust Performance( 2017-01-04)Social commerce as a subset of e-commerce, popularizes rapidly with an increasing number of users, and consumers’ trust has become a crucial factor in the success of social commerce firms, and impacts on their decision on purchasing. In this regard, the study tries to research the characteristics of social commerce (transaction safety, concentration and enjoyment, communication and information quality) that influence consumers’ trust and assess the effects of trust on trust performance (purchase and word-of-mouth intentions), and trust performance will provides a basis for consumers to decide to purchase, and put forward feasible suggestions to social commerce firms. The results of an empirical analysis based on a sample of 133 users indicate that all the characteristics of social commerce involved had significant effects on trust, and then will positively influence trust performance.
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ItemConnecting Researchers with Companies for University-Industry Collaboration( 2017-01-04)Nowadays, companies are spending more time and money to enhance their innovation ability to respond to the increasing market competition. The pressure makes companies seek help from external knowledge, especially those from academia. Unfortunately, there is a gap between knowledge seekers (companies) and suppliers (researchers) due to the scattered and asymmetric information. To facilitate shared economy, various platforms are designed to connect the two parties. In this context, we design a researcher recommendation system to promote their collaboration (e.g. patent license, collaborative research, contract research and consultancy) based on a research social network with complete information about both researchers and companies. In the recommendation system, we evaluate researchers from three aspects, including expertise relevance, quality and trustworthiness. The experiment result shows that our system performs well in recommending suitable researchers for companies. The recommendation system has been implemented on an innovation platform, InnoCity. \
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