The full papers proceedings are available at: https://link.springer.com/book/10.1007/978-3-030-11238-7

The proceedings of the poster track are available at: http://ceur-ws.org/Vol-2247/

A VLDB 2018 workshop

In urban spaces, there is a huge amount of heterogeneous data being generated by a diversity of sources, such as sensors, devices, vehicles, smart buildings, and others. Although they are used to monitor basic services, they can provide significant information about human interactions and populational dynamics. Moreover, people constantly interact with each other through social media services, and much of interpersonal interaction is nowadays mediated by information technology. Citizens consume and share information about their cities - such as problems, events, ideas, suggestions, criticisms, and demands – acting as ‘human sensors’, forming opinions and participating in the city evolution.

These data explosion has resulted in the emerging topic of “Big Social Data”. Broadly speaking, Big Social Data refers to large data volumes that relate to people interactions or describe their behaviors, needs, and patterns. The volume, the production and spreading velocity, and the variety (providing semantic richness) of such data open enormous possibilities for utilizing and analyzing them for the understanding of urban spaces, tackling the major issues that these localities face, and helping in the creation of smarter and sustainable cities.

Urban computing is a process of acquisition, treatment, and analysis of big and heterogeneous data to better understand how city ecosystems work. This understanding can remedy a wide range of issues affecting the everyday lives of citizens and the long-term health and efficiency of cities. The use of Big Social Data in urban computing helps us to understand the nature of urban phenomena and even predict the future of cities, creating solution to reduce costs and optimize resource consumption, improve population mobility, provide higher human life quality, enhance decision making in emergency scenarios, and engage more effectively with citizens for a continuous city planning.

Urban computing is an interdisciplinary field and this workshop aims to connect works about the use and treatment of Big Social Data in multidisciplinary research spanning across computer science - such as engineering, environmental studies, health, urban planning and social sciences - for urban sustainability, transparency, livability, social inclusion, place-making, accessibility, and resilience.

The workshop welcomes contributions describing original ideas, promising new concepts, and practical experience. In particular, we solicit papers of different types:

• Research - Papers proposing new approaches, models, theories or techniques related to Big Social Data and Urban Computing, including new data structures, algorithms, whole systems, and frameworks. They should make substantial theoretical and empirical contributions to the research field.
• Experiments and Case Studies - Papers focusing on the experimental evaluation of existing approaches including data structures and algorithms for Big Social Data and Urban Computing bring new insights through the analysis of these experiments. Results of experiments and case studies papers, for example, can describe benefits or disadvantages of well-known approaches in new scenarios, opening new research problems and challenges by demonstrating unexpected behavior or phenomena, or comparing a set of traditional approaches in an experimental survey.
• Industry and Application - Papers reporting practical experiences on Big Social Data and Urban Computing. Industry and Application papers might describe specific application domains and detail the solution process.
• Dataset - Papers describing a dataset - completely cleaned, treated, curated, opened and legal - possible to be reused or applied in other scenarios. The final contribution is a dataset available to access and reuse, but the article must present all the information necessary to understand the processes of data gathering and treatment, and how to use them.
• Vision - Papers identifying emerging or future research issues and directions, and describing new research visions related to data-driven innovative solutions and big social data-powered applications to cope with the real-world challenges for building smart cities

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