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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
Ultimate Azure Data Engineering
Embark on a comprehensive journey into Azure data engineering with \"Ultimate Azure Data Engineering\". Starting with foundational topics like SQL and relational database concepts, you'll progress to comparing data engineering practices in Azure versus on-premises environments. Next, you will dive deep into Azure cloud fundamentals, learning how to effectively manage heterogeneous data sources and implement robust Extract, Transform, Load (ETL) concepts using Azure Data Factory, mastering the orchestration of data workflows and pipeline automation. The book then moves to explore advanced database design strategies and discover best practices for optimizing data performance and ensuring stringent data security measures. You will learn to visualize data insights using Power BI and apply these skills to real-world scenarios. Whether you're aiming to excel in your current role or preparing for Azure data engineering certifications, this book equips you with practical knowledge and hands-on expertise to thrive in the dynamic field of Azure data engineering.
eBook
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
Retrieval-augmented Chinese text-to-SQL generation for conversational bibliographic search
To overcome the limitations of current bibliographic search systems, such as low semantic precision and inadequate handling of complex queries, this study introduces a novel conversational search framework for the Chinese bibliographic domain. Our approach makes several contributions. We first developed BibSQL, the first Chinese Text-to-SQL dataset for bibliographic metadata. Using this dataset, we built a two-stage conversational system that combines semantic retrieval of relevant question-SQL pairs with in-context SQL generation by large language models (LLMs). To enhance retrieval, we designed SoftSimMatch, a supervised similarity learning model that improves semantic alignment. We further refined SQL generation using a Program-of-Thoughts (PoT) prompting strategy, which guides the LLM to produce more accurate output by first creating Python pseudocode. Experimental results demonstrate the framework’s effectiveness. Retrieval-augmented generation (RAG) significantly boosts performance, achieving up to 96.6% execution accuracy. Our SoftSimMatch-enhanced RAG approach surpasses zero-shot prompting and random example selection in both semantic alignment and SQL accuracy. Ablation studies confirm that the PoT strategy and self-correction mechanism are particularly beneficial under low-resource conditions, increasing one model’s exact matching accuracy from 74.8% to 82.9%. While acknowledging limitations such as potential logic errors in complex queries and reliance on domain-specific knowledge, the proposed framework shows strong generalizability and practical applicability. By uniquely integrating semantic similarity learning, RAG, and PoT prompting, this work establishes a scalable foundation for future intelligent bibliographic retrieval systems and domain-specific Text-to-SQL applications.
Journal Article
Is ChatGPT a Good Geospatial Data Analyst? Exploring the Integration of Natural Language into Structured Query Language within a Spatial Database
With recent advancements, large language models (LLMs) such as ChatGPT and Bard have shown the potential to disrupt many industries, from customer service to healthcare. Traditionally, humans interact with geospatial data through software (e.g., ArcGIS 10.3) and programming languages (e.g., Python). As a pioneer study, we explore the possibility of using an LLM as an interface to interact with geospatial datasets through natural language. To achieve this, we also propose a framework to (1) train an LLM to understand the datasets, (2) generate geospatial SQL queries based on a natural language question, (3) send the SQL query to the backend database, (4) parse the database response back to human language. As a proof of concept, a case study was conducted on real-world data to evaluate its performance on various queries. The results show that LLMs can be accurate in generating SQL code for most cases, including spatial joins, although there is still room for improvement. As all geospatial data can be stored in a spatial database, we hope that this framework can serve as a proxy to improve the efficiency of spatial data analyses and unlock the possibility of automated geospatial analytics.
Journal Article
A Question‐Aware Few‐Shot Text‐to‐SQL Neural Model for Industrial Databases
Intelligent question answering over industrial databases is a challenging task due to the multicolumn context and complex questions. The existing methods need to be improved in terms of SQL generation accuracy. In this paper, we propose a question‐aware few‐shot Text‐to‐SQL approach based on the SDCUP pretrained model. Specifically, an attention‐based filtering approach is proposed to reduce the redundant information from multiple columns in the industrial database scenario. We further propose an operator semantics enhancement method to improve the ability of identifying complex conditions in queries. Experimental results on the industrial benchmarks in the fields of electric energy and structural inspection show that the proposed model outperforms the baseline models across all few‐shot settings.
Journal Article
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
Speech-to-SQL: toward speech-driven SQL query generation from natural language question
Speech-based inputs have been gaining significant momentum with the popularity of smartphones and tablets in our daily lives, since voice is the most popular and efficient way for human–computer interaction. This paper works toward designing more effective speech-based interfaces to query the structured data in relational databases. We first identify a new task named
Speech-to-SQL
, which aims to understand the information conveyed by human speech and directly translate it into structured query language (SQL) statements. A naive solution to this problem can work in a cascaded manner, that is, an automatic speech recognition component followed by a text-to-SQL component. However, it requires a high-quality ASR system and also suffers from the error compounding problem between the two components, resulting in limited performance. To handle these challenges, we propose a novel end-to-end neural architecture named
SpeechSQLNet
to directly translate human speech into SQL queries without an external ASR step. SpeechSQLNet has the advantage of making full use of the rich linguistic information presented in speech. To the best of our knowledge, this is the first attempt to directly synthesize SQL based on common natural language questions in spoken form, rather than a natural language-based version of SQL. To validate the effectiveness of the proposed problem and model, we further construct a dataset named
SpeechQL
, by piggybacking the widely used text-to-SQL datasets. Extensive experimental evaluations on this dataset show that SpeechSQLNet can directly synthesize high-quality SQL queries from human speech, outperforming various competitive counterparts as well as the cascaded methods in terms of exact match accuracies. We expect speech-to-SQL would inspire more research on more effective and efficient human–machine interfaces to lower the barrier of using relational databases.
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
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