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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3766-3780页

作者： Gan, Wensheng Huang, Gengsen Weng, Jian Gu, Tianlong Yu, Philip S. Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China University of Illinois Chicago Chicago United States

In many real-world applications, sequential rule mining (SRM) can offer prediction and recommendation functions for a variety of services. It is an important technique of pattern mining to discover all valuable rules that can reveal the temporal relationship between objects. Although several algorithms of SRM are proposed to solve various practical problems, there are no studies on the problem of targeted mining. Targeted sequential rule mining aims to obtain those interesting sequential rules that users focus on, thus avoiding the generation of other invalid and unnecessary rules. It can further improve the efficiency of users in analyzing rules and reduce the consumption of computing resources. In this paper, we first present the relevant definitions of target sequential rules and formulate the problem of targeted sequential rule mining. Then, we propose an efficient algorithm called TaSRM. Several pruning strategies and an optimization are introduced to improve the efficiency of TaSRM. Finally, a large number of experiments are conducted on different benchmarks, and we analyze the results in terms of running time, memory consumption, and scalability, as well as query cases with different query rules. It is shown that the novel algorithm TaSRM and its variants can achieve better experimental performance compared to the baseline algorithm. © 2025 IEEE. All rights reserved.

关键词： structured query language

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Incorporating AI Tools Into Database Classes

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Journal of Information Systems Education 2025年第1期36卷 37-52页

作者： Zhang, Xiaoni Department of Management Information Systems and Quantitative Methods University of Alabama at Birmingham BirminghamAL35294 United States

This teaching tip explores the integration of AI tools into database education. The author describes how instructors can use AI tools to prepare teaching materials and how students can use AI to facilitate database development. The teaching tips provided encompass both course-level objectives and assignment-specific strategies. The inclusion of AI prompts and the comprehensive coverage of tasks indicates an integrated approach to enhancing overall course delivery while providing specific guidance on individual assignments. Surveys and email interviews with students revealed positive feedback regarding the AI-integrated assignments. Students found AI tools helpful for learning database concepts, developing databases, understanding SQL code, and improving their problem-solving and critical thinking skills. Additionally, students expressed a desire for further learning in prompt engineering and better communication with AI tools. Although student feedback is positive, educators must also consider challenges such as over-reliance on AI and the need to train students in critical thinking and problem-solving skills. Designing assignments that require reflections and detailed documentation of the problem-solving process could be an effective approach. The detailed process and associated materials in the appendices provide useful resources for instructors who want to incorporate AI tools into their database courses. Copyright © 2025 by the Information Systems & Computing Academic Professionals, Inc. (ISCAP). Permission to make digital or hard copies of all or part of this journal for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial use. All copies must bear this notice and full citation. Permission from the Editor is required to post to servers, redistribute to lists, or utilize in a for-profit or commercial use. Permission requests should be sent to the Editor-in-Chief, Journal of Information Systems

关键词： structured query language

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SQL Autograder: Web-based LLM-powered Autograder for Assessment of SQL Queries

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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 2025年 1-31页

作者： Manikani, Karan Chapaneri, Radhika Shetty, Dharmik Shah, Divyata Mukesh Patel Sch Technol Management & Engn SVKMS N AI Dept Vile Parle West Mumbai 400056 Maharashtra India

structured query language (SQL) queries are an important aspect of database concepts in the information technology (IT) domain. Evaluation of SQL queries ensures that the learners can understand and apply various SQL concepts correctly. However, this can be a laborious task when carried out manually by course instructors at universities, which often does not scale well. To address these limitations, this study proposes a web-based application, SQL autograder, which can be used by instructors of a university course to evaluate assessments and enhance the quality of education and learning outcomes. We propose a framework that makes use of large language models (LLMs) to assess the correctness of SQL queries submitted by students. We train a variety of open-source LLMs of varying sizes on a diverse dataset of SQL queries, with queries ranging from simple ones that include a single JOIN statement to more complex ones involving multiple SQL features. We implemented and tested our LLM-based framework in real-world educational settings for a university course, which shows promising results in enhancing the learning experience for students by providing instant feedback on areas needing improvement. We tested our application on 88 participants and found that the autograder is 180x faster than the instructor, with an average accuracy of 96.77%. After taking the qualitative feedback from the participants, 97% of them found it to be useful. The proposed framework reduces the workload of instructors by offering a more scalable and consistent evaluation process that enhances the performance of students.

关键词： SQL Autograder structured query language Large language Model Low-rank Adaptation Parameter Efficient Fine-tuning

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Selecting Opinion Leaders using Skyline query (SOL-SQ): a new approach

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International Journal of Computers and Applications 2025年第3期47卷 262-278页

作者： Chebil, Malek Bach Tobji, Mohamed Anis Elmi, Sayda Jallouli, Rim Univ. Manouba ESEN Tunisia Université de Tunis ISG de Tunis LARODEC Tunisia Electrical and Computer Engineering and Computer Science Department University of New Haven - Yale University New HavenCT United States Univ. Manouba ESEN LIGUE Tunisia

Online social networks engage millions of users, some of them emerge as opinion leaders influencing others. Identifying opinion leaders within a specific domain is critical given their pivotal role in shaping public opinion in several fields. The literature has explored several methods to detect opinion leaders according to different metrics. In this paper, we propose a new hybrid method called Selecting Opinion Leaders using Skyline query (SOL-SQ). It aims to identify opinion leaders by analyzing both user interaction and content. The proposed method employs machine learning for topic-based analysis to assess specific context user's influence. It also uses statistical metrics to evaluate engagement, graph-based metrics for network centrality, and PageRank to measure popularity. SOL-SQ selects automatically the key opinion leaders through multi-dimensional metrics without weighting, and without choosing the Top-N parameter, i.e. the N best opinion leaders to select like in most existing methods. We evaluated SOL-SQ against existing methods, comparing average similarity and standard deviation in identifying opinion leaders. Our method outperformed others in all identified domain categories. Additionally, a subjective evaluation showed that tweets of the opinion leaders produced by SOL-SQ had the highest influence scores across all four domain categories, based on expert judgment and quartile analysis. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： structured query language

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L2SM: a query-optimized linked LSM-tree for HTAP workloads

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FRONTIERS OF COMPUTER SCIENCE 2025年第7期19卷 197606-197606页

作者： Feng, Xiaoyue Wei, Dashan Guo, Chaopeng Song, Jie Northeastern Univ Software Coll Shenyang 110819 Peoples R China

This paper proposes a novel dual-pointer structure with a root node (called L2SM) and designs three optimization algorithms to improve the performance of L2SM. These improvements greatly enhance point and range query ... 详细信息

关键词： structured query language

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Towards Practical Doubly-Efficient Private Information Retrieval 28th

Towards Practical Doubly-Efficient Private Information Retri...

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28th International Conference on Financial Cryptography and Data Security

作者： Okada, Hiroki Player, Rachel Pohmann, Simon Weinert, Christian Royal Holloway Univ London London England KDDI Res Fujimino Japan Univ Tokyo Bunkyo Japan

ISBN: (纸本)9783031786785;9783031786792

Private information retrieval (PIR) protocols allow clients to access database entries without revealing the queried indices. They have many real-world applications, including privately querying patent-, compromised credential-, and contact databases. While existing PIR protocols that have been implemented perform reasonably well in practice, they all have suboptimal asymptotic complexities. A line of work has explored so-called doubly-efficient PIR (DEPIR), which refers to single-server PIR protocols with optimal asymptotic complexities. Recently, Lin, Mook, and Wichs (STOC 2023) presented the first protocol that completely satisfies the DEPIR constraints and can be rigorously proven secure. Unfortunately, their proposal is purely theoretical in nature. It is even speculated that such protocols are completely impractical, and hence no implementation of any DEPIR protocol exists. In this work, we challenge this assumption. We propose several optimizations for the protocol of Lin, Mook, and Wichs that improve both asymptotic and concrete running times, as well as storage requirements, by orders of magnitude. Furthermore, we implement the resulting protocol and show that for batch queries it can outperform state-of-the-art protocols.

关键词： structured query language

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One language to Rule Them All: Behavioural querying of Process Data Using SQL

One Language to Rule Them All: Behavioural Querying of Pro...

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International Workshops which were held in conjunction with the 6th International Conference on Process Mining, ICPM 2024

作者： Brand, Jakob Kampik, Timotheus Okulmus, Cem Weidlich, Matthias Humboldt-Universität zu Berlin Berlin Germany SAP Signavio Berlin Germany Umeå University Umeå Sweden

ISBN: (纸本)9783031822247

State-of-the-art solutions for process mining rely on proprietary, domain-specific languages to query data recorded during business process execution. To support common analysis tasks, these languages focus on the definition of queries for behavioural patterns. Yet, the use of domain-specific languages for process mining has drawbacks: they require specific user training, lead to a decoupling of the query models for (i) data extraction and transformation, and (ii) the actual analysis, and induce engineering overhead through the development of a dedicated query engine. In this work, we therefore explore the use of standard SQL for process mining tasks. In particular, we demonstrate that the SQL concepts for row pattern recognition as realised by the MATCH_RECOGNIZE clause are sufficient to capture queries for behavioural patterns as specified in the SIGNAL language by SAP Signavio as well as the Process querying language (PQL) by Celonis. Based on a discussion of the respective language features, we outline a translation of SIGNAL and PQL queries into standard SQL. This way, we provide the basis for the adoption of widely used, general purpose query engines for process mining tasks. © The Author(s) 2025.

关键词： structured query language

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Structurally-Encrypted Databases Combined with Filters: Enhanced Security and Rich Queries

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4567-4580页

作者： Liu, Feng Yang, Jinjiang Zhao, Jingcheng Xue, Yingjie Xue, Kaiping University of Science and Technology of China School of Cyber Science and Technology Anhui Hefei230027 China

Building encrypted databases has been a long-standing challenge in the field of database security. In recent years, structured Encryption (STE) has emerged as a promising approach to constructing encrypted databases, striking a balance between security and efficiency. Although existing STE-based encrypted database systems achieve high efficiency in query processing, all these schemes struggle to support rich queries with minimal information leakage. In this paper, we present a new STE-based encrypted database system, named Filter-integrated Encrypted Database (FinEDB), which supports exact-match and range queries, conjunctive queries and join operations, while maintaining limited information leakage. We first design a novel secure inverted index to avoid storage overhead blow-up when extending to support rich query capabilities. Then, we integrate Binary Fuse filters into our proposed inverted index to enable efficient query processing. By leveraging the homomorphic property of Binary Fuse filters, our approach leaks less information than existing STE-based solutions. Besides, we provide rigorous proof for our proposed scheme under the simulation paradigm. To evaluate the performance, we implement the prototype of FinEDB and compare it with the baseline STE-based scheme. Experiment results demonstrate that FinEDB is practical and can support rich queries on real-world databases. © 2005-2012 IEEE.

关键词： structured query language

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Scaling Optimization over Uncertainty via Compilation

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Proceedings of the ACM on Programming languages 2025年第1期9卷 1546-1574页

作者： Cho, Minsung Gouwar, John Holtzen, Steven Northeastern University United States

Probabilistic inference is fundamentally hard, yet many tasks require optimization on top of inference, which is even harder. We present a new optimization-via-compilation strategy to scalably solve a certain class of such problems. In particular, we introduce a new intermediate representation (IR), binary decision diagrams weighted by a novel notion of branch-and-bound semiring, that enables a scalable branch-and-bound based optimization procedure. This IR automatically factorizes problems through program structure and prunes suboptimal values via a straightforward branch-and-bound style algorithm to find optima. Additionally, the IR is naturally amenable to staged compilation, allowing the programmer to query for optima mid-compilation to inform further executions of the program. We showcase the effectiveness and flexibility of the IR by implementing two performant languages that both compile to it: dappl and pineappl. dappl is a functional language that solves maximum expected utility problems with first-class support for rewards, decision making, and conditioning. pineappl is an imperative language that performs exact probabilistic inference with support for nested marginal maximum a posteriori (MMAP) optimization via staging. © 2025 Copyright held by the owner/author(s).

关键词： structured query language

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Observations on Bloom Filters for Traversal-Based query Execution over Solid Pods

Observations on Bloom Filters for Traversal-Based Query Exec...

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21st International Conference on The Semantic Web (ESWC)

作者： Hanski, Jonni Taelman, Ruben Verborgh, Ruben Univ Ghent Dept Elect & Informat Syst IDLab IMEC Ghent Belgium

ISBN: (纸本)9783031789519;9783031789526

Traversal-based query execution enables the resolving of queries over Linked Data documents, using a follow-your-nose approach to locating query-relevant data by following series of links through documents. This traversal, however, incurs an unavoidable overhead in the form of data access costs. Through only following links known to be relevant for answering a given query, this overhead could be minimized. Prior work exists in the form of reachability conditions to determine the links to dereference, however this does not take into consideration the contents behind a given link. Within this work, we have explored the possibility of using Bloom filters to prune query-irrelevant links based on the triple patterns contained within a given query, when performing traversal-based query execution over Solid pods containing simulated social network data as an example use case. Our discoveries show that, with relatively uniform data across an entire benchmark dataset, this approach fails to effectively filter links, especially when the queries contain triple patterns with low selectivity. Thus, future work should consider the query plan beyond individual patterns, or the structure of the data beyond individual triples, to allow for more effective pruning of links.

关键词： structured query language

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