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"Query languages"
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High performance SQL server : the go faster book
Design and configure SQL Server instances and databases in support of high-throughput applications that are mission-critical and provide consistent response times in the face of variations in user numbers and query volumes. Learn to configure SQL Server and design your databases to support a given instance and workload. You'll learn advanced configuration options, in memory technologies, storage and disk configuration, and more, all toward enabling your desired application performance and throughput.
Book
NLINQ: A natural language interface for querying network performance
Artificial Intelligence is finding increased applications in communication networks. In particular, the field of text-to-Structured Query Language (SQL) translation has great potential to improve customer experience by allowing the querying of network performance databases using natural language. Such adoption, however, is challenging, in general. On one hand, live production systems may have databases with non-semantic table and column names, which makes natural language parsing and text-to-SQL translation difficult. On the other hand, noisy input texts may lead to the generation of incorrect queries. Moreover, inaccurate transcription of speech input into text may further aggravate the problem. Motivated by these aspects, we investigate the problem of natural language-based querying of network performance databases used by Wireless Mesh Networks (WMNs). In particular, we fine-tune a state-of-the-art model to translate natural language questions into appropriate SQL queries. In order to mitigate the problem of non-semantic names, we generate database views with semantic column names, based on the existing tables. In addition, we make domain-specific corrections in the text in order to help generate accurate queries. We also design the Natural Language Interface for Network Query (NLINQ) prototype for a real-life industrial WMN solution. The results of the performance evaluation indicate that natural language text can be translated into SQL queries with an accuracy of 89.021–92.663%, on average. Moreover, the average turnaround time of NLINQ ranges between 1.263–2.013 seconds. The results indicate that NLINQ is suitable for real-time, interactive querying of network performance databases.
Journal Article
Learning PHP, MySQL & JavaScript : with jQuery, CSS & HTML5
\"In this [book] web designers will learn how to use the technologies [presented in this book] and pick up web programming practices along the way--including how to optimize websites for mobile devices\"-- Amazon.com.
Book
SamQL: a structured query language and filtering tool for the SAM/BAM file format
Background
The Sequence Alignment/Map Format Specification (SAM) is one of the most widely adopted file formats in bioinformatics and many researchers use it daily. Several tools, including most high-throughput sequencing read aligners, use it as their primary output and many more tools have been developed to process it. However, despite its flexibility, SAM encoded files can often be difficult to query and understand even for experienced bioinformaticians. As genomic data are rapidly growing, structured, and efficient queries on data that are encoded in SAM/BAM files are becoming increasingly important. Existing tools are very limited in their query capabilities or are not efficient. Critically, new tools that address these shortcomings, should not be able to support existing large datasets but should also do so without requiring massive data transformations and file infrastructure reorganizations.
Results
Here we introduce SamQL, an SQL-like query language for the SAM format with intuitive syntax that supports complex and efficient queries on top of SAM/BAM files and that can replace commonly used Bash one-liners employed by many bioinformaticians. SamQL has high expressive power with no upper limit on query size and when parallelized, outperforms other substantially less expressive software.
Conclusions
SamQL is a complete query language that we envision as a step to a structured database engine for genomics. SamQL is written in Go, and is freely available as standalone program and as an open-source library under an MIT license,
https://github.com/maragkakislab/samql/
.
Journal Article
Enhancing text-to-structured query language translation for seamless electronic medical record access
Traditional models for natural language-to-SQL translation in Electronic Medical Record (EMR) systems struggle with understanding medical terminology, handling complex queries, and bridging the syntax-semantics gap, leading to scalability and accuracy issues. Advanced solutions like Large Language Model (LLM) based approaches address these challenges by leveraging deep learning and domain-specific training to enhance performance and usability. Hence, this article introduces an advanced medical Text-to-Structured Query Language (SQL) paradigm that simplifies accessing EMRs by translating natural language queries into SQL commands. This model is built on the advanced Code-T5 (Text-to-Text Transfer Transformer) architecture, further enhanced with Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA) techniques; it effectively addresses the challenges posed by the complexity of traditional SQL queries enabling seamless access to critical healthcare data. The innovation of the proposed model lies in its exceptional performance across multiple evaluation metrics. It achieves a Bilingual Evaluation Understudy (BLEU) score of 81.68, significantly outperforming leading models like T5, Fine-Tuned Language Net (FLAN) T5, and Bidirectional and Auto-Regressive Transformers (BART) while excelling in Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, underscoring its proficiency in generating semantically accurate and coherent SQL queries. Furthermore, the proposed model attains a high token-level F1-score, ensuring a balanced precision and recall and a Jaccard similarity score of 0.83, surpassing T5, Flan T5, and BART. The proposed model excels in handling complex medical queries, bridging natural language and SQL to empower data-driven decisions and advance medical informatics.
Journal Article
A Semantic Learning-Based SQL Injection Attack Detection Technology
Over the years, injection vulnerabilities have been at the top of the Open Web Application Security Project Top 10 and are one of the most damaging and widely exploited types of vulnerabilities against web applications. Structured Query Language (SQL) injection attack detection remains a challenging problem due to the heterogeneity of attack loads, the diversity of attack methods, and the variety of attack patterns. It has been demonstrated that no single model can guarantee adequate security to protect web applications, and it is crucial to develop an efficient and accurate model for SQL injection attack detection. In this paper, we propose synBERT, a semantic learning-based detection model that explicitly embeds the sentence-level semantic information from SQL statements into an embedding vector. The model learns representations that can be mapped to SQL syntax tree structures, as evidenced by visualization work. We gathered a wide range of datasets to assess the classification performance of the synBERT, and the results show that our approach outperforms previously proposed models. Even on brand-new, untrained models, accuracy can reach 90% or higher, indicating that the model has good generalization performance.
Journal Article
Visual query languages to design complex queries: a systematic literature review
Structured query language (SQL) is a widely used language for accessing both relational and non-relational databases. SQL is the standard form of access in relational databases, while in non-relational databases, SQL is becoming increasingly available and consolidating itself as an access interface for querying data in cluster environments. Despite its declarative syntax, the specification of SQL queries is not a trivial task, even for experts, because some queries demand complex constructs (i.e., subqueries, joins, set operations, conditional expressions, grouping restrictions, and recursion). Visual query languages (VQLs) are an alternative to reduce this complexity. However, although several VQLs have been proposed, they are not widely used in practice. By identifying and analyzing the support provided by VQLs that make it possible to design complex SQL queries, this study collected evidence that helps discover the strengths and weaknesses of each VQL, providing useful feedback for other research initiatives that seek to propose improved VQLs. For this purpose, a systematic literature review was carried out. After analyzing 22 relevant studies and performing 462 inspections, this review points to the need for more expressive VQLs, computer-aided software engineering (CASE) tools available to end users, and more rigorous evaluations to investigate the VQL syntax and semantics.
Journal Article
Integrating Relational Structure to Heterogeneous Graph for Chinese NL2SQL Parsers
The existing models for NL2SQL tasks are mainly oriented toward English text and cannot solve the problems of column name reuse in Chinese text data, description in natural language query, and inconsistent representation of data stored in the database. To address this problem, this paper proposes a Chinese cross-domain NL2SQL model based on a heterogeneous graph and relative position attention mechanism. This model introduces relational structure information defined by the expert to construct initial heterogeneous graphs for database schemas and natural language questions. The heterogeneous graph is pruned based on natural language questions, and the multi-head relative position attention mechanism is used to encode the database schema and natural language questions. The target SQL statement is generated using a tree-structured decoder with predefined SQL syntax. Experimental results on the CSpider dataset demonstrate that our model better aligns database schema with natural language questions and understands the semantic information in natural language queries, effectively improving the matching accuracy of Chinese multi-table SQL statement generation.
Journal Article
A Naive approach: Translation of Natural Language to Structured Query Language
A database is a major source of information which plays an important role in our life. Information retrieval from the database requires formulating a querythatisunderstandablebythecomputerinordertoproducedesiredoutput. Generally, databases work with structured query language (SQL). But a naive user usually unfamiliar with the structured query language as well as structure of the table in the database. Hence, it becomes very difficult for the naïve-user to collect the desired information. This paper provides a solution to this problem and it enables users to retrieve information through natural language, such as English language. Being able to access information from the database by using natural language bridges the man-machine gap. Tokenization, lexical analysis, syntactic analysis, semantic analysis, and other complex stages are all involved in converting a natural language query into a SQL query. The purpose of this paper is to translate natural language queries into Structure Query Language queries, allowing non-technical people to get connected to databases and to gather the required information.
Journal Article
A systematic survey of LLM-based text-to-SQL: methodologies, security vulnerabilities, and future challenges
Text-to-Structured Query Language (SQL) systems, which allow users to query databases using natural language, have advanced significantly with the rise of Large Language Models (LLMs). While this progress has boosted accuracy, it has also introduced serious security and practical deployment challenges that existing literature has not systematically analyzed. In this article, we systematically review the existing literature to examine various approaches, identify security vulnerabilities, discuss design trade-offs, and outline future challenges. Our goal is to offer a comprehensive overview. Our analysis shows that modern approaches fall into two main categories: prompt engineering on proprietary models and fine-tuning open-source models. Regarding security, using the Open Worldwide Application Security Project (OWASP) Top 10 framework, we identify critical threats such as Prompt Injection (P2SQL), data poisoning to create backdoors, and inference attacks. The analysis reveals that current defense mechanisms are not effective enough against these attacks. We also highlight the strategic trade-off between the superior accuracy of proprietary models and the control, security, and cost-effectiveness of open-source models. This survey provides a systematic analysis of security vulnerabilities in LLM-based Text-to-SQL systems, concluding that current countermeasures are inadequate. Our findings point to an urgent research direction: developing systems that are not only accurate but also robust, efficient, and fundamentally secure for reliable real-world deployment.
Journal Article