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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
A model and query language for temporal graph databases
Graph databases are becoming increasingly popular for modeling different kinds of networks for data analysis. They are built over the property graph data model, where nodes and edges are annotated with property-value pairs. Most existing work in the field is based on graphs were the temporal dimension is not considered. However, time is present in most real-world problems. Many different kinds of changes may occur in a graph as the world it represents evolves across time. For instance, edges, nodes, and properties can be added and/or deleted, and property values can be updated. This paper addresses the problem of modeling, storing, and querying temporal property graphs, allowing keeping the history of a graph database. This paper introduces a temporal graph data model, where nodes and relationships contain attributes (properties) timestamped with a validity interval. Graphs in this model can be heterogeneous, that is, relationships may be of different kinds. Associated with the model, a high-level graph query language, denoted T-GQL, is presented, together with a collection of algorithms for computing different kinds of temporal paths in a graph, capturing different temporal path semantics. T-GQL can express queries like “Give me the friends of the friends of Mary, who lived in Brussels at the same time than her, and also give me the periods when this happened”. As a proof-of-concept, a Neo4j-based implementation of the above is also presented, and a client-side interface allows submitting queries in T-GQL to a Neo4j server. Finally, experiments were carried out over synthetic and real-world data sets, with a twofold goal: on the one hand, to show the plausibility of the approach; on the other hand, to analyze the factors that affect performance, like the length of the paths mentioned in the query, and the size of the graph.
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
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
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
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
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
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