ﻻ يوجد ملخص باللغة العربية
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 a query execution plan (QEP) describes the execution steps of a query, learners can acquire the understanding by perusing the QEPs generated by a RDBMS. Unfortunately, in practice, it is often daunting for a learner to comprehend these QEPs 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 a declarative framework called pool that enables subject matter experts to efficiently create and maintain natural language descriptions of physical operators used in QEPs. A rule-based framework called RULE-LANTERN is proposed that exploits pool to generate natural language descriptions of QEPs. Despite the high accuracy of RULE-LANTERN, our engagement with learners reveal that, consistent with existing psychology theories, perusing such rule-based descriptions lead to boredom due to repetitive statements across different QEPs. To address this issue, we present a novel deep learning-based language generation framework called NEURAL-LANTERN that infuses language variability in the generated description by exploiting a set of paraphrasing tools and word embedding. Our experimental study with real learners shows the effectiveness of lantern in facilitating comprehension of QEPs.
XML data warehouses form an interesting basis for decision-support applications that exploit heterogeneous data from multiple sources. However, XML-native database systems currently suffer from limited performances in terms of manageable data volume
The query log of a DBMS is a powerful resource. It enables many practical applications, including query optimization and user experience enhancement. And yet, mining SQL queries is a difficult task. The fundamental problem is that queries are symboli
Predicting the execution time of queries is an important problem with applications in scheduling, service level agreements and error detection. During query planning, a cost is associated with the chosen execution plan and used to rank competing plan
Natural Language Search (NLS) extends the capabilities of search engines that perform keyword search allowing users to issue queries in a more natural language. The engine tries to understand the meaning of the queries and to map the query words to t
Federated Learning aims to learn machine learning models from multiple decentralized edge devices (e.g. mobiles) or servers without sacrificing local data privacy. Recent Natural Language Processing techniques rely on deep learning and large pre-trai