Abstract This study introduces an innovative framework named scale for processing dynamic skyline queries securely in cloud environments. Unlike previous approaches that require complex operations on encrypted data, scale simplifies dynamic skyline domination to mere comparisons, significantly improving query efficiency. Through empirical evaluations over four datasets, we show that scale accelerates query processing nearly 1000-fold compared to existing state-of-the-art methods. Specifically, scale shows significant efficiency improvements by simplifying query interactions to a single round between the user and the cloud, which is validated through empirical studies on multiple datasets. Moreover, we introduce two distributed versions of scale , dist-scale-s and dist-scale-e , which further optimize performance by facilitating parallel processing. This adaptation showcases a substantial reduction in response times and computational overhead, underpinning the scalability and effectiveness of our framework in handling large-scale, secure cloud-based queries.

The database systems course in an undergraduate computer science degree program is gaining increasing importance due to the continuous supply of database-related jobs as well as the rise of Data Science. A key learning goal of learners taking such a course is to understand how SQL queries are executed in an RDBMS in practice. An RDBMS typically exposes a query execution plan (QEP) in a visual or textual format, which describes the execution steps for a given query. However, it is often daunting for a learner to comprehend these QEPs containing vendor-specific implementation details. In this demonstration, we present a novel, generic, and portable system called LANTERN that generates a natural language (NL)-based description of the execution strategy chosen by the underlying RDBMS to process a query. It provides a declarative framework called POOL for subject matter experts (SME) to efficiently create and manipulate the NL descriptions of physical operators of any RDBMS. It then exploits POOL to generate the NL descriptions of QEPs by integrating a rule-based and a deep learning-based techniques to infuse language variability in the descriptions. Such an NL generation strategy mitigates the impact of boredom on learners caused by repeated exposure of similar text generated by a rule-based system.

The database systems course is offered as part of an undergraduate computer science degree program in many major universities. A key learning goal of learners taking such a course is to understand how sql queries are processed in a rdbms in practice. Since aquery execution plan (qep ) describes the execution steps of a query, learners can acquire the understanding by perusing the qep s generated by a rdbms. Unfortunately, in practice, it is often daunting for a learner to comprehend these qep s containing vendor-specific implementation details, hindering her learning process. In this paper, we present a novel, end-to-end,generic system called lantern that generates a natural language description of a qep to facilitate understanding of the query execution steps. It takes as input an sql query and its qep, and generates a natural language description of the execution strategy deployed by the underlying rdbms. Specifically, it deploys adeclarative framework called pool that enablessubject matter experts to efficiently create and maintain natural language descriptions of physical operators used in qep s. Arule-based framework called rule-lantern is proposed that exploits pool to generate natural language descriptions of qep s. Despite the high accuracy of rule-lantern, our engagement with learners reveal that, consistent with existing psychology theories, perusing such rule-based descriptions lead toboredom due to repetitive statements across different qep s. To address this issue, we present a noveldeep learning-based language generation framework called neural -lantern that infuses language variability in the generated description by exploiting a set ofparaphrasing tools andword embedding. Our experimental study with real learners shows the effectiveness of lantern in facilitating comprehension of qep s.