Throughout index tuning, existing index advisors allocate tuning budget equally across all queries in the workload, even though a considerable portion of queries benefit negligible from index tuning, leading to high costs and inefficiency. This paper introduces a novel learning-based index advisor named GITEE, which increases tuning efficiency and effectiveness by intelligently guiding the exploration of the large search space on candidate index. Our solution consists of three components. First, we utilize execution plan and predicate information to accurately estimate the maximum improvement indexing can bring, which serves as preliminary knowledge for reasonable tuning budget allocation. Second, we filter out queries based on the impact of indexing on the individual queries and their influence on others, thereby reducing the number of candidate indexes. Third, we leverage a Monte Carlo Tree Search-based solution, guided by the knowledge, to accelerate the selection of high-quality index configurations within the valuable search space. Extensive experiments across various benchmarks demonstrate that GITEE achieves superior tuning performance compared to state-of-theart heuristic or learning-based index advisors, while reducing tuning overhead by 1-2 orders of magnitude.

The challenges of population management as urban density increase globally have compelled researchers and developers to consider more efficient means of managing resources in cities. Consequently, the smart city concept has emerged as a response to addressing the challenge of optimal resource utilization in urban centers. However, with digital technologies proliferating as key components of the solution, it is necessary to develop a digital identity solution for all components of the smart city environment. For completeness, the solution must encompass all entities, including physical and intangible assets, processes, and most importantly, its residents. Consequently, a unified, distributed data integration and efficient analysis platform is required: the digital city operating system. In this article, we focus on a key component of digital city management in the form of secure identification of individual residents. We collect user attributes and securely transmit them to other system components for verification. Upon successful completion of the verification process, a digital identity is created for the applying resident and the set of transactions leading to the ID creation are stored in the blockchain. Our system is secure and can serve as the basis for the development of a digital infrastructure for smart city management.