Currently, people can collect data from themselves and their surrounding environment quickly due to the exponential development of sensors and communication technologies and social networks. The ability to collect such data opens the new opportunity to understand better the association between human beings and the properties of the surrounding environment. These associations can be utilized for intelligence, planning, controlling, retrieval, and decision making efficiently and effectively by governments, industries, and citizens. Wearable sensors, lifelog cameras, and social networks can report people’s health, activities, and behaviors by the first-view perspective while surrounding sensors, social networks interaction, and third-party data can give the third-view perspective of how their society activities look like. Several investigations have been done to deal with each perspective, but few investigations focus on how to analyze and retrieve cross-data come from different perspectives to bring better benefits to human beings. The target of the workshop is to attract researchers to work on the intelligent cross-data analysis and retrieval to bring the smart sustainable society to human beings. The domain of the research can vary from wellbeing, disaster prevention & mitigation, mobility, to food computing, to name a few.

Example topics of interest include but is not limited to the following

• Event-based cross-data retrieval
• Data mining and AI technology to discover and predict spatial-temporal-semantic correlations between cross-data.
• Complex event processing for linking sensors data from individuals, regions, to broad areas dynamically.
• Transfer Learning from one region to another region to construct or customize similar analysis and prediction of events using locally-collected data effectively and efficiently.
• Hypotheses Development of the associations within the heterogeneous data contributes towards building good multimodal models that make it possible to understand the impact of the surrounding environment on human beings at the local and individual scale.
• Realization of a prosperous and independent region in which people and nature coexist.
• Applications leverage intelligent cross-data analysis for a particular domain.
• Cross-datasets for Repeatable Experimentation.

• The goal of the workshop is to attract researchers and experts in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, and atmospheric science to tackle the intelligent cross-data analysis issue.
• The accepted papers are expected to be published in the workshop proceedings. Excellent papers are encouraged to submit to journals or a special issue that will be organized by the organizers.