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The 2022 IEEE World Congress on Services will be held in a hybrid mode with physical presence in Barcelona, Spain. SERVICES is the premier international forum for presenting and discussing the most recent and significant technical research contributions in the field of services computing. Centered around web-based services, SERVICES 2022 covers various systems and networking research pertaining to cloud, edge and IoT, as well as technologies for intelligent computing, learning, Big Data, blockchain, and digital healthcare applications, addressing critical issues such as knowledge network, high performance, security, privacy, dependability, trustworthiness, and cost-effectiveness. Particularly, the 2022 Congress will welcome papers on the aftermath and the impact of COVID-19 on services and the world infrastructure. In addition to co-located theme-topic conferences, the Congress will also include symposia supporting deep-dive discussions on emerging topics, and complement the SERVICES 2022 program with industry and application presentations and panels. Authors are invited to prepare early and submit original and unpublished papers to any of these conferences at www.easychair.org. All submitted manuscripts will be peer- reviewed by at least three reviewers. Accepted and presented papers will appear in the conference proceedings published by the IEEE Computer Society Press. SERVICES 2022 is the only premier professional event for the services computing field offered by IEEE, under the auspice of the Technical Committee on Services Computing (TCSVC). [ABSTRACT FROM AUTHOR]

In this paper, we present a framework for performing automatic analysis of Land Use Zones based on Location-Based Social Networks (LBSNs). We model city areas using a hierarchical structure of POIs extracted from foursquare. We encode such structures in kernel machines, e.g., Support Vector Machines, using a new Tree Kernel, i.e., the Hierarchical POI Kernel (HPK), which can take the importance of the individual POIs into account during the substructure matching. This way, HPK projects structures in the space of all their possible substructures such that each dimension corresponds to a semantic structural feature, weighted according to the discriminative power of POIs. We generated four different datasets for the following cities: Barcelona, Lisbon, Amsterdam and Milan, where we trained and tested our models. The results show that our approach largely outperforms previous work and standard baseline built on simple features, such as counts of different POIs. Finally, we apply a mining algorithm to extract the most relevant features (tree fragments) from the implicit TK space according to the weights the kernel machine assigned to them. Our approach can produce an explicit set of representative features that can be used to classify and characterize urban areas. [ABSTRACT FROM AUTHOR]