Heterogeneous Crowdsourced Data Co-mining for Smart Cities

      Advances in computing, communications, storage, and sensing technologies have enabled to digitalize and record citizens’ traces while interacting with cyber-physical spaces. We have entered an era where such data are becoming increasingly big and easily available. These data are heterogeneous, contributed by the crowd in the cities, coming from different sources and with diverse formats. Broadly, such data are generated mainly from three sources: Internet and Web applications, sensor networks, and mobile/wearable devices. The scale and richness of the multimodal, mixed data sources present us an opportunity to compile the data into a comprehensive picture of individual’s daily life facets, transform our understanding of our lives, organizations and societies, and enable completely innovative urban services for smart cities, including public transportation, public safety, city resource management, environment monitoring, and social interaction assistance. However, the raw data cannot be used directly, and the emerging analytical techniques, including big data analytics, text and image analytics, network and mobile analytics need to be studied and applied, according to the specific applications. In the big data era, novel architectures, methodologies, and techniques must be also developed, in order to bring citizens closer to the vision of a smart city (i.e. improving the living quality/safety of citizens, sustainable city development, and so on).

We invite authors from academia and industry to submit their original research as well as review articles to present latest progresses for current development or future goals in this field. In line with applying novel data analytics for smart cities, topics of interest include, but are not limited to:

1.Human mobility behaviours understanding, modeling, predication and supporting applications

2.Advanced multi-sourced data co-mining and fusion techniques, and supporting applications

3.Advanced data integration, management, retrieval, and visualization techniques

4.Advanced information sharing techniques

5.Advanced and context-aware route planning, such as customize driving/travel route recommendation

6.Participant selection in Crowdsensing

7.Urban infrastructure management, designing, and planning

8.Flaw urban planning problem identification and possible solutions

9.Innovative urban services and applications

10.Security and privacy issues and possible solutions in data transmission, processing, etc.