People can currently collect data from themselves and their surrounding environment quickly due to the exponential development of sensors, communication technologies, and social networks. Besides, data has evolved and become more intelligent than ever. Thanks to artificial intelligence and advanced application techniques, data can now be presented in meaningful forms that provide more information and knowledge for other near-humancognitive analytics and retrieval. The ability to collect such (intelligent) data opens the new opportunity to understand better the association between human beings and the surrounding environment's properties (i.e., humans as the center). 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 from the first-view perspective. In contrast, surrounding sensors, social network 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 analyzing and retrieving cross-data from different perspectives to bring better benefits to human beings.

Multimedia analytics and retrieval have gained significant improvement within a decade. People can now extract more data insights precisely and quickly towards having many excellent applications serving human lives. Nevertheless, people create multimedia and other types of data that reflect the diverse perspectives of human lives. In other words, multimedia and other data types are just pieces of the puzzle of the world's pictures. Hence, it is necessary to assembly all these pieces towards having a better solution for human-centered problems. Hence, the workshop welcomes those who work with multimedia and others and come from diverse research domains and disciplines to work on intelligent cross-data analytics and retrieval to bring a smart, sustainable society to human beings. The research domain can vary from well-being, disaster prevention and 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.

Federated Analytics and Federated Learning for cross-data.

Privacy-public data collaboration.

Followed by the success of the ICMR 2020 first workshop on intelligent cross-data analytics and retrieval, the second ICDAR workshop aims to provide the playground to people interested in the workshop's topics. In this playground, people share their experiences and brave new ideas towards making cross-data more intelligent by compensating each type of data's strengths and propose a new way to analyze and retrieve cross-data under different perspectives.

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.