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structured query language Learning: Concept Map-Based Instruction Based on Cognitive Load Theory

IEEE ACCESS 2020年 8卷 100095-100110页

作者： Shin, Shin-Shing Natl Taitung Univ Dept Informat Sci & Management Syst Taitung 950 Taiwan

structured query language (SQL) is difficult to master because the execution process of SQL statements is invisible. When learning to construct an SQL query, learners must visualise the evolution process of the intermediate datasets of the SQL statement in working memory, which may burden learners' cognitive load and consequently jeopardise learning outcomes. This study describes the execution process of SQL statements by using concept maps to improve learners' understanding of SQL. An empirical experiment was conducted using two database courses, namely concept map-based and conventional instruction, to examine the relationship between concept maps and the understanding of SQL from a cognitive load theory perspective. The experimental results demonstrated the superiority of concept map-based instruction over conventional instruction because concept map-based instruction reduces extraneous load but increases germane load. Concept map construction facilitated learner engagement and promoted meaningful learning. Studying the instructors' concept maps helped learners follow the cognitive structures used by instructors to perform SQL queries, and enabled them to perceive the execution process of SQL queries relatively easily. These results potentially help educators understand the learning difficulties caused by the declarative nature of SQL and motivating researchers to resolve the inherent problem by considering learners' cognitive processes.

关键词： structured query language Animation Tools Databases Semantics Silicon structured query language (SQL) cognitive load theory concept maps semantic network theory

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Symbol question conversion in structured query language using fuzzy with deep attention based rain LSTM

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第22期81卷 32323-32349页

作者： Tuli, Preeti Patra, Jyoti Prakash Shri Shankaracharya Inst Profess Management & Tec Comp Sci & Engn Dept Raipur Madhya Pradesh India

Effective communication between human and machine is achieved using Natural language Processing (NLP). However, the users are not aware about the structured query language (SQL) of the normal database (DB). Therefore, to make users more aware of SQL, a deep attention based rain LSTM (Long Short-Term Memory) is introduced in proposed process for developing the SQL query model. The input query of user is entered in natural language form which is initially pre-processed. Different steps are included in pre-processing they are Lower case conversion, Tokenization, Escape Word Removal and Parts Of speech (POS) tagger. After pre-processing stage, a novel fuzzy approach is introduced with the help of rain LSTM method. With the help of the feature matrix technique, the words of the pre-processed query are given a certain rank. The LSTM-ROA (Rain Optimization Algorithm) method is used for categorizing the words into relations, attributes and clauses on the basis of tagged elements. After removing ambiguous attributes, a final query is generated based on the extracted elements. The proposed method is implemented in the Python platform and the performances are analysed in terms of Accuracy, Precision, Recall and Error Rate. The proposed DA-RLSTM achieved 95.74% accuracy which illustrates that the proposed work has attained better result than the existing methods.

关键词： Natural language processing structured query language Feature matrix Fuzzy logic Rain optimization algorithm Long short-term memory

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PSS-SQL: Protein Secondary Structure - structured query language

PSS-SQL: Protein Secondary Structure - Structured Query Lang...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Mrozek, Dariusz Wieczorek, Dominika Malysiak-Mrozek, Bozena Kozielski, Stanislaw Silesian Tech Univ Inst Informat PL-44100 Gliwice Poland

ISBN: (纸本)9781424441242

Secondary structure representation of proteins provides important information regarding protein general construction and shape. This representation is often used in protein similarity searching. Since existing commercial database management systems do not offer integrated exploration methods for biological data e. g. at the level of the SQL language, the structural similarity searching is usually performed by external tools. In the paper, we present our newly developed PSS-SQL language, which allows searching a database in order to identify proteins having secondary structure similar to the structure specified by the user in a PSS-SQL query. Therefore, we provide a simple and declarative language for protein structure similarity searching.

关键词： PSS-SQL SQL SQL language bioinformatics biological data integrated exploration methods data mining database management systems molecular biophysics molecular configurations protein construction protein secondary structure protein shape protein structure similarity search proteins relational databases structured query language

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Integrity Constraint Verification of structured query language by Abstract Interpretation

Integrity Constraint Verification of Structured Query Langua...

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Information Technology (OCIT), OITS International Conference on

作者： Anwesha Kashyap Angshuman Jana Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

Over the decades and now-a-days the data-driven applications are playing a pivotal role in every aspect of our daily lives by providing an easy interface to store, access and process crucial data with the help of Database Management System (DBMS). However, it is always necessary to ensure the data integrity for every operation on a database. In this paper, we propose a novel framework for structured query language (SQL), aiming at automatically and formally verifying integrity constraints in terms of enterprise policy specifications on data in the underlying database. To this aim, we extend the abstract interpretation theory to the case of structured query languages.

关键词： structured query language Data integrity Semantics Database systems Proposals Information technology Business

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Auto-conversion from Natural language to structured query language using Neural Networks Embedded with Pre-training and Fine-tuning Mechanism

Auto-conversion from Natural Language to Structured Query La...

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Chinese Automation Congress (CAC)

作者： Jian Liu Qian Cui Hongwei Cao Tianyuan Shi Min Zhou Chongqing University College of automation Chongqing P. R. China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

From Natural language to structured query language(NL2SQL) represents an interesting yet challenging process in translating natural questions into database queries in human-machine dialogue systems. NL2SQL requires a high level of semantic understanding, which is difficult to achieve with traditional methods. Although there is a variety of ways to address this problem, most of them do not work satisfactorily. In this work, we propose an approach for NL2SQL using artificial neural networks embedded with pre-training and fine-tuning model, where we use XLNet as the encoding layer for the model, this method is able to better capture the hidden semantic information between natural language and table header of dataset. To make more use of the related information, the table header type information is enhanced in our model. In particular we introduce this information into the calculation of probability values at the output layer. The effectiveness of the proposed method is tested with good performance on WikiSql dataset, nearly 81% accuracy is achieved on the dev dataset.

关键词： Task analysis Mathematical model Databases Biological system modeling structured query language Natural languages Encoding

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Web Browser Extension Development of structured query language Injection Vulnerability Detection Using Long Short-Term Memory Algorithm

Web Browser Extension Development of Structured Query Langua...

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International Conference of Computer Science and Information Technology

作者： Togu Novriansyah Turnip Hotma Aruan Anita Lasmaria Siagian Leonardo Siagian Information Technology Study Program Institut Teknologi Del Toba Indonesia

ISBN: (纸本)9798350399080

structured query language Injection (SQLi) is a client-side application vulnerability that allows attackers to inject malicious SQL queries with harmful intents, including stealing sensitive information, bypassing authentication, and even executing illegal operations to cause more catastrophic damage to users on the web application. According to OWASP, the top 10 harmful attacks against web applications are SQL Injection attacks. Moreover, based on data reports from the UK's National Fraud Authority, SQL Injection is responsible for 97% of data exposures. Therefore, in order to prevent the SQL Injection attack, detection SQLi system is essential. The contribution of this research is securing web applications by developing a browser extension for Google Chrome using Long Short-Term Memory (LSTM), which is a unique kind of RNN algorithm capable of learning long-term dependencies like SQL Injection attacks. The results of the model will be deployed in static analysis in a browser extension, and the LSTM algorithm will learn to identify the URL that has to be injected into Damn Vulnerable Web Application (DVWA) as a sample-tested web application. Experimental results show that the proposed SQLi detection model based on the LSTM algorithm achieves an accuracy rate of 99.97%, which means that a reliable client-side can effectively detect whether the URL being accessed contains a SQLi attack or not.

关键词： Uniform resource locators structured query language Web pages Static analysis SQL injection Browsers Classification algorithms

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Reducing structured query language Injection Vulnerabilities Through Functional Programming Principles

Reducing Structured Query Language Injection Vulnerabilities...

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IEEE Southeastcon

作者： Michael Piscatello MBP Research Group Elizabethtown USA

Web application security breaches frequently involve exploiting structured query language (SQL) injection vulnerabilities introduced by weaknesses in the coding style. Software engineers commonly lack the expertise and experience in implementing the functional programming principles of immutability, using pure functions, and reducing side effects to prevent vulnerabilities in their code. This study explores the changes web application developers can implement following the functional programming principles of immutability, using pure functions, and reducing side effects to reduce the presence of SQL injection vulnerabilities in web applications. The solution presented in this study, design strategies, supports the nascent design theory that applying the functional programming principles of immutability, using pure functions, and reducing side effects can reduce the presence of SQL injection vulnerabilities in web applications. When applied to an intentionally vulnerable web application, the design strategies developed through this study demonstrated effectiveness in reducing SQL injection vulnerabilities from web applications. This study contributes to the practice of software development by giving web application developers a new way to secure their web Applications.

关键词： structured query language Codes SQL injection Software Encoding Application security Functional programming

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structured query language tool to identify untreated individuals with abdominal aortic aneurysms

Journal of Vascular Surgery: Vascular Insights

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Journal of Vascular Surgery: Vascular Insights 2024年 2卷 100111-100111页

作者： Brambora, Jenna, MD Roberts, Sophia H., MD Yin, Kanhua, MD, MPH Okereke, Ifeanyichukwu, MD Wanken, Zachary, MD, MS Droz, Nathan, MD Zayed, Mohamed A., MD, PhD, MBA Department of Surgery Section of Vascular Surgery Washington University School of Medicine St Louis MO CardioVascular Research Innovation in Surgery & Engineering Center Department of Surgery Washington University School of Medicine St Louis MO Veterans Affairs St Louis Health Care System St Louis MO Department of Radiology Washington University School of Medicine St Louis MO Division of Molecular Cell Biology Washington University School of Medicine St Louis MO Department of Biomedical Engineering McKelvey School of Engineering Washington University St Louis MO Department of Surgery Division of Surgical Sciences and Innovation Washington University School of Medicine St Louis MO

AbstractObjectiveDespite well-established guidelines for the diagnosis and surveillance of abdominal aortic aneurysms (AAAs), many patients are still not discovered until they have already sustained a rupture. It is estimated that as many as 50% of individuals with AAAs are undiagnosed until they present with a life-threatening rupture and hemorrhagic shock. structured query language (SQL) is a tool that can scan existing electronic medical record (EMR) systems to identify patients with specific characteristics or morbidities. Here we evaluate the use of SQL to determine whether it can identify untreated patients with AAA disease and other vascular comorbidities. MethodsAn SQL disease discovery query code (Medtronic) was developed based on the clinical criteria recommendations of the United States Preventative Services Task Force for AAA screening (male sex; age, 65-75 years; and former/current smoking history). The code was then integrated with 18 International Classification of Diseases, 10th Revision (ICD-10) diagnostic codes for AAA and AAA-related diseases. Over 130 ICD-10 or Current Procedural Terminology (CPT) codes for previous aortic repair were overlaid as an exclusion criterion. The query was applied to 1 year of patient data (June 2021 to June 2022) at our single-center, university-affiliated, regional referral medical center. Manual chart review was performed on identified patients to confirm the incidence of AAA disease (aortic diameter ≥3.0 cm), as well as the incidence of other vascular conditions such as hypertension, coronary artery disease (CAD), peripheral arterial disease (PAD), and/or carotid artery stenosis (CAS). ResultsThe SQL code identified a total of 457 patients (449 male, 8 female). Of these patients, 167 (36.5%) had confirmed AAAs with an average diameter of 4.2 cm (range, 3.0-9.7 cm), with a positive predictive value of 28.2%. Prior AAA repair, with either endovascular or open repair, was observed in 39 patients with AAAs (23.4%). Amo

关键词： Abdominal aortic aneurysm Preventative care structured query language

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Comparison of LLM Models and Strategies for structured query Construction from Natural language Queries

Comparison of LLM Models and Strategies for Structured Query...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Fernandez, Franklin Cardenoso Garcia, Robinson Luiz Souza Caarls, Wouter PUC-Rio Rio de Janeiro Brazil Petrobras Rio de Janeiro Brazil

ISBN: (纸本)9798350374575

The advent of large language models (LLM) has sharply increased the power of machine translation because of their remarkable success in many text processing tasks. This feature makes LLMs attractive options for specific tasks, such as translating natural language queries into keyword-based queries. Within this research, we compare the different techniques applied in LLMs for open and closed-source model options to facilitate decision-making for researchers and practitioners. Our findings present that while fine-Tuning is a highly recommended option, prompting techniques allow us to achieve similar performance with commercial tools;however, before any model selection, it is essential to consider the amount of data, computational resources, and data privacy. © 2024 IEEE.

关键词： structured query language

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An active inference strategy for prompting reliable responses from large language models in medical practice

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NPJ DIGITAL MEDICINE 2025年第1期8卷 1-10页

作者： Shusterman, Roma Waters, Allison C. O'Neill, Shannon Bangs, Marshall Luu, Phan Tucker, Don M. Brain Electrophysiol Lab Co Eugene OR 97401 USA Neurosom Inc Eugene OR 97401 USA Icahn Sch Med Mt Sinai New York NY USA

Continuing advances in Large language Models (LLMs) are transforming medical knowledge access across education, training, and treatment. Early literature cautions their non-determinism, potential for harmful responses, and lack of quality control. To address these issues, we propose a domain-specific, validated dataset for LLM training and an actor-critic prompting protocol grounded in active inference. A Therapist agent generates initial responses to patient queries, while a Supervisor agent refines them. In a blind validation study, experienced cognitive behavior therapy for insomnia (CBT-I) therapists evaluated 100 patient queries. For each query, they were given either the LLM's response or one of two therapist-crafted responses-one appropriate and one deliberately inappropriate-and asked to rate the quality and accuracy of each reply. The LLM often received higher ratings than the appropriate responses, indicating effective alignment with expert standards. This structured approach lays the foundation for safely integrating advanced LLM technology into medical applications.

关键词： structured query language

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