A system to transform natural language queries into SQL queries

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

Today, relational databases are widely used in information systems. SQL (structured query language) is taught extensively in universities and professional schools across the globe as a programming language for its data management and accesses. Previously, we have studied a web-based programming learning assistant system (PLAS) to help novice students learn popular programming languages by themselves through solving various types of exercises. For SQL programming, we have implemented the grammar-concept understanding problem (GUP) and the comment insertion problem (CIP) for its initial studies. In this paper, we propose an SQL Query Description Problem (SDP) as a new exercise type for describing the SQL query to a specified request in a MySQL database system. To reduce teachers’ preparation workloads, we integrate a generative AI-assisted SQL query generator to automatically generate a new SDP instance with a given dataset. An SDP instance consists of a table, a set of questions and corresponding queries. Answer correctness is determined by enhanced string matching against an answer module that includes multiple semantically equivalent canonical queries. For evaluation, we generated 11 SDP instances on basic topics using the generator, where we found that Gemini 3.0 Pro exhibited higher pedagogical consistency compared to ChatGPT-5.0, achieving perfect scores in Sensibleness, Topicality, and Readiness metrics. Then, we assigned the generated instances to 32 undergraduate students at the Indonesian Institute of Business and Technology (INSTIKI). The results showed an average correct answer rate of 95.2% and a mean SUS score of 78, which demonstrates strong initial student performance and system acceptance.

The DBMS applications are becoming increasingly popular in the scientific community to support the interactive exploration of huge data. A result of heavy usage has also lead to a lot of tavles generated in the warehouse. This has tremendously increased the need for recommendation system & various tools for the user as user is not capable to explore such huge data by using Structured Query language. This motivates to turn towards the idea of query recommendation system which will assist the technical & non-technical users to generate top-N query recommendations as per their needs. In this survey paper, the focus is given on opportunities that exist in domain of query recommendation, brief description of recommendation outline and techniques. Along with this, the terminology to identify the similarity and comparison of fragment based approach & matrix factorization approach is also explored. The Database Management Systems are used to organize, maintain and retrieve the huge amount of data. To acquire the essential data the user need to interact with the database by taking the support of declarative Structured Query Language (SQL). Most of times, it is observed that user faces the difficulties in interacting with the database by using SQL query language as the database have hundreds of schema's and thousands of attributes. This problem has been solved with the support of Database Management Applications such as SAP, Easy Query, and Microsoft Access. These applications involve the user interaction and manual editing of the SQL query components but when the user is unacquainted about the underlying database structure then it becomes difficult for the user to formulate the query. To formulate the query, the research work has been done (1) and that results into the query assistant tool which helps the user to either formulate the query or to recommend the relevant query items based on formulated query. This work is the reflection of the web recommendation techniques those are used in gaining the on-line marketing intelligence. The web recommendation techniques and algorithms have their strong relation with the Information Retrieval Science (IR). In IR, the recommendation techniques and algorithms are used to retrieve the most relevant top K documents as per the formulated query. Most of times the Business Verticals have their own off-line database and the user will be interested in exploration of the database and faces the difficulties. Therefore, it may also possible to extend the idea specified by the authors (1) to provide the Query Recommendation Assistant Tool that enable the user to formulate and refine the SQL query. This take off to cataloged view on how to make the imperative use of IR and the web recommendation techniques to generate and recommend the top K relevant query objects by identifying the similarities among the SQL query fragments. This becomes the novel approach that handles crucial responsibility in proposing the assistant tool to retrieve data over the database. The rest of the paper is organized as follows: section-2 describes the related work and the section-3 concentrates on recommendation outline. The section-4 describes the recommendation techniques and similarity metrics those have been used in finding the similarity among the query fragments. The section-5 focuses on terminology used to calculate weights of items required for finding similarity. The section-6 states the comparison between the fragment based approach and matrix factorization approach.

Most of existing studies on parsing natural language (NL) for constructing structured query language (SQL) do not consider the complex structure of database schema and the gap between NL and SQL query. In this paper, we propose a schema-aware neural network with decomposing architecture, namely HSRNet, which aims to address the complex and cross-domain Text-to-SQL generation task. The HSRNet models the relationship of the database schema with a hierarchical schema graph and employs a graph network to encode the information into sentence representation. Instead of end-to-end generation, the HSRNet decomposes the generation process into three phases. Given an input question and schema, we first choose the column candidates and generate the sketch grammar of the SQL query. Then, a detail completion module fills the details based on the column candidates and the corresponding sketch. We demonstrate the effectiveness of our hierarchical schema representation by incorporating the information into different baselines. We further show that the decomposing architecture significantly improves the performance of our model. Evaluation of Spider benchmark shows that the hierarchical schema representation and decomposing architecture improves our parser result by 14.5% and 4.3% respectively.

The purpose of this instructional resource is to explain how Structured Query Language (SQL) queries can help auditors to independently retrieve properly formatted data as audit evidence or for further analyses. It demonstrates data extraction using Microsoft (MS) Access, one of the simplest SQL-compliant database software applications. We use a dataset fragment that is extracted from the publicly available enterprise datasets provided by Walton College (University of Arkansas) to run SQL queries as a part of audit investigations. Data extraction is the first step of Extract, Transform, and Load (ETL) and may be time-consuming. We demonstrate how SQL queries can assist with this task, thereby allowing the auditor to begin analysis sooner. This instructional resource can be used to prepare future auditors for the emerging data-rich and technology-driven business environment.

As most organizations and companies depend on the database to process confidential information, database security has received considerable attention in recent years. In the database security category, access control is the selective restriction of access to the system or information only by the authorized user. However, access control is difficult to prevent information leakage by structured query language (SQL) statements created by internal attackers. In this paper, we propose a hybrid anomalous query access control system to extract the features of the access behavior by parsing the query log with the assumption that the DBA has role-based access control (RBAC) and to detect the database access anomalies in the features using the particle swarm optimization (PSO)-based CNN-LSTM network. The CNN hierarchy can extract important features for role classification in the vector of elements that have converted the SQL queries, and the LSTM model is suitable for representing the sequential relationship of SQL query statements. The PSO automatically finds the optimal CNN-LSTM hyperparameters for access control. Our CNN-LSTM method achieves nearly perfect access control performance for very similar roles that were previously difficult to classify and explains important variables that influence the role classification. Finally, the PSO-based CNN-LSTM networks outperform other state-of-the-art machine learning techniques in the TPC-E scenario-based virtual query dataset.

The performance of diagnostic tests crucially depends on the disease prevalence, test sensitivity, and test specificity. However, these quantities are often not well known when tests are performed outside defined routine lab procedures which make the rating of the test results somewhat problematic. A current example is the mass testing taking place within the context of the world-wide SARS-CoV-2 crisis. Here, for the first time in history, laboratory test results have a dramatic impact on political decisions. Therefore, transparent, comprehensible, and reliable data is mandatory. It is in the nature of wet lab tests that their quality and outcome are influenced by multiple factors reducing their performance by handling procedures, underlying test protocols, and analytical reagents. These limitations in sensitivity and specificity have to be taken into account when calculating the real test results. As a resolution method, we have developed a Bayesian calculator, the Bayes Lines Tool (BLT), for analyzing disease prevalence, test sensitivity, test specificity, and, therefore, true positive, false positive, true negative, and false negative numbers from official test outcome reports. The calculator performs a simple SQL (Structured Query Language) query and can easily be implemented on any system supporting SQL. We provide an example of influenza test results from California, USA, as well as two examples of SARS-CoV-2 test results from official government reports from The Netherlands and Germany-Bavaria, to illustrate the possible parameter space of prevalence, sensitivity, and specificity consistent with the observed data. Finally, we discuss this tool’s multiple applications, including its putative importance for informing policy decisions.

Abstract: Structured Query Language (SQL) is a important query language for storing, manipulating and retrieving the data from database. So every individual needs the skill to access the data and analyze it. Since SQL is used while designing most of the softwares in the industry, students should have in-depth knowledge of firing the queries on the database. So the mobile app for the SQL is developed. This app contains SQL material, practice part to run the query, test, question bank and videos related to SQL topics. In this study, step by step process is given to access each part of this mobile app. The research question is “Whether an android mobile app for the course SQL is useful and helps students to understand this language by going through the notes & PPTs and practicing the SQL query through the practice section given in app?” Also the feedback is given to know the perception of students about this app. Keywords: SQL, Android Mobile Application, Quiz, Likert’s scale, Feedback

Chapter 2 - Querying

The Structured Query Language (SQL) is the most widely used declarative language for accessing relational databases, and an essential topic in introductory database courses in higher learning institutions. Despite the intuitiveness of SQL, formulating and comprehending written queries can be confusing, especially for undergraduate students. One major reason for this is that the simple syntax of SQL is often misleading and hard to comprehend. A number of tools have been developed to aid the comprehension of queries and to improve the mental models of students concerning the underlying logic of SQL. Some of these tools employed visualisation and animation in their approach to aid the comprehension of SQL. This paper presents an interactive comprehension aid based on visualisation, specifically designed to support the SQL SELECT statement, an area identified in the literature as problematic for students. The visualisation tool uses visual specifications depicting SQL operations to build queries. This is expected to reduce the cognitive load of a student who is learning SQL. We have shown with an online survey that adopting visual specifications in teaching systems assist students in attaining a richer learning experience in introductory database courses.

The performance of diagnostic tests crucially depends onthe diseaseprevalence, test sensitivity, and test specificity. However, these quantities are often not well known when tests are performed outside defined routine labprocedureswhich make the rating of the test results somewhat problematic. A current example isthe masstesting taking place within the context of the world-wide SARS-CoV-2 crisis. Here, for the first time in history, laboratory test resultshavea dramatic impact on political decisions. Therefore, transparent, comprehensible, and reliable data is mandatory. It isin the nature of wetlab tests that their quality and outcome are influenced by multiple factors reducing their performance by handling procedures, underlying test protocols,and analytical reagents. These limitations in sensitivity and specificityhave tobetaken into accountwhen calculatingthe realtest results. As a resolution method, we have developed a Bayesian calculator, the Bayes Lines Tool (BLT), foranalyzingdisease prevalence, test sensitivity, test specificity, and, therefore, true positive, false positive, true negative,and false negative numbers from official test outcome reports. The calculator performs a simple SQL (Structured Query Language) query and can easily be implementedonany system supporting SQL. We providean example of influenza test results from California, USA, as well astwoexamples of SARS-CoV-2 test results from official government reports from The Netherlands andGermany-Bavaria, to illustrate the possible parameter space of prevalence, sensitivity, and specificity consistent with the observed data. Finally, wediscussthis tool's multiple applications, including its putative importance for informing policy decisions.

We present the online MultiDark Database – a Virtual Observatory‐oriented, relational database for hosting various cos‐mological simulations. The data is accessible via an SQL (Structured Query Language) query interface, which also allows users to directly pose scientific questions, as shown in a number of examples in this paper. Further examples for the usage of the database are given in its extensive online documentation. The database is based on the same technology as the Millennium Database, a fact that will greatly facilitate the usage of both suites of cosmological simulations. The first release of the MultiDark Database hosts two 8.6 billion particle cosmological N‐body simulations: the Bolshoi (250 h–1 Mpc simulation box, 1 h–1 kpc resolution) and MultiDark Run1 simulation (MDR1, or BigBolshoi, 1000 h–1 Mpc simulation box, 7 h–1 kpc resolution). The extraction methods for halos/subhalos from the raw simulation data, and how this data is structured in the database are explained in this paper. With the first data release, users get full access to halo/subhalo catalogs, various profiles of the halos at redshifts z = 0–15, and raw dark matter data for one time‐step of the Bolshoi and four time‐steps of the MultiDark simulation. Later releases will also include galaxy mock catalogs and additional merger trees for both simulations as well as new large volume simulations with high resolution. This project is further proof of the viability to store and present complex data using relational database technology. We encourage other simulators to publish their results in a similar manner. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Puzzle 45 - Pepperoni pizza

A Web-Based Perioperative Dashboard as a Platform for Anesthesia Informatics Innovation.

Copyright © 2019 by author(s) and Scientific Research Publishing Inc. The performance and reliability of converting natural language into structured query language can be problematic in handling nuances that are prevalent in natural language. Relational databases are not designed to understand language nuance, therefore the question why we must handle nuance has to be asked. This paper is looking at an alternative solution for the conversion of a Natural Language Query into a Structured Query Language (SQL) capable
\nof being used to search a relational database. The process uses the natural language concept, Part of Speech to identify words that can be used to identify database tables and table columns. The use of Open NLP based grammar files, as well as additional configuration files, assist in the translation from natural language to query language. Having identified which tables and which columns contain the pertinent data the next step is to create the SQL statement.