Data have played a critical role in human life. In the digital era, where data can be collected almost anywhere, at any time, and by anything, people can own a vast volume of real-time data reflecting their living environment in various granularity. From these data, people can extract the necessary information to gain knowledge towards becoming wise. Since data do not come from a sole source, they only reflect a small part of a massive puzzle of life. Hence, the more pieces of data can be collected and filled into a canvas, the faster the puzzle can be solved. If we consider a puzzle piece as single-modal data, the puzzle game becomes a multimodal data analytic problem. If we consider a group of puzzle pieces assembled as a segment of the puzzle as one domain (e.g., mountain, house, animal), the puzzle game becomes a multi-domain problem. If we consider a 3D puzzle game, we are talking of a multi-platform problem. Finally, the bidirectional mapping between puzzle pieces and the frame (e.g., sample picture of a puzzle) during the game can be considered as cross-data/domain/platform problem. In other words, we can use a set of data (i.e., multimodal data) from certain domains with analytic models built on one platform to infer (e.g., prediction, interpolation, query) data from another domain(s) and vice versa. We have witnessed the rise of cross-data against multimodal data problems recently. The cross-modal retrieval system uses a textual query to look for images; the air quality index can be predicted using lifelogging images; the congestion can be predicted using weather and tweets data; daily exercises and meals can help to predict the sleeping quality are some examples of this research direction. Although vast investigations focusing on multimodal data analytics have been developed, few cross-data (e.g., cross-modal data, cross-domain, cross-platform) research has been carried on. In order to promote intelligent cross-data analytics and retrieval research and to bring a smart, sustainable society to human beings, the specific article collection on "Intelligent Cross-Data Analysis and Retrieval" is introduced. This Research Topic welcomes those who come from diverse research domains and disciplines such as well-being, disaster prevention and mitigation, mobility, climate change, tourism, healthcare, and food computing.

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

Event-based cross-data retrieval Data mining and AI technology.

Complex event processing for linking sensors data from individuals, regions to broad areas dynamically.

Transfer Learning and Transformers.

Hypotheses Development of the associations within the heterogeneous data

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.

Integration of diverse multimodal data

Followed by the success of the ICMR-ICDAR 2020, ICMR-ICDAR 2021, and ICMR-ICDAR 2022 workshops on intelligent cross-data analytics and retrieval, this proposal aims to organize the fourth workshop that provides 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: Journal of Algorithms