A Comparison of Natural and Structured Query Languages

1978 ◽  
Vol 22 (1) ◽  
pp. 705-707
Author(s):  
Duane W. Small ◽  
Linda J. Weldon
Keyword(s):  
Natural Language ◽  
Query Language ◽  
Data Retrieval ◽  
Query Languages ◽  
Problem Structure ◽  
Computer Terminal ◽  
Structured Query Language ◽  
Data Search ◽  
Speed Advantage ◽  
The Ideal

It is often assumed that natural language would be the ideal user-oriented language for communicating with computers. However, languages structured to fit particular tasks may be easier to use. Twenty subjects solved a set of data retrieval problems on a computer terminal using English, and solved another set using SEQUEL, a structured query language. No differences in accuracy were observed. Problems were solved more quickly using SEQUEL, although only by those subjects whose English session preceded their SEQUEL session. The speed advantage of SEQUEL appeared primarily for problems concerned with structuring the data search, rather than for problems involving logical complexities in what was to be sought. The fact that the structured language provided advantages for those aspects of the task that were reflected in the language's syntax indicates that the conceptual aspects of language and problem structure, and not such general matters as length of commands, are responsible for the advantages of structured language.

Query Languages in NoSQL Databases

2015 ◽  
pp. 415-437 ◽  
Author(s):  
Maristela Holanda ◽  
Jane Adriana Souza
Keyword(s):  
Query Language ◽  
Data Retrieval ◽  
Query Languages ◽  
Nosql Databases ◽  
Different Types ◽  
Application Codes

This chapter aims to investigate how NoSQL (Not Only SQL) databases provide query language and data retrieval mechanisms. Users attest to many advantages in using the NoSQL databases for specific applications, however, they also report that querying and retrieving data easily continues to be a problem. The NoSQL operations require that, during the project, the queries must be thought of as built-in application codes. The authors intend to contribute to the investigation of querying, considering different types of NoSQL databases.

scholarly journals An Encoder-Decoder Framework Translating Natural Language to Database Queries

2018 ◽  
Author(s):  
Ruichu Cai ◽  
Boyan Xu ◽  
Zhenjie Zhang ◽  
Xiaoyan Yang ◽  
Zijian Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Natural Language ◽  
Machine Translation ◽  
Query Language ◽  
Empirical Evaluation ◽  
Data Retrieval ◽  
Semantic Features ◽  
State Management ◽  
The Neural Network ◽  
Grammar Rules

Machine translation is going through a radical revolution, driven by the explosive development of deep learning techniques using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In this paper, we consider a special case in machine translation problems, targeting to convert natural language into Structured Query Language (SQL) for data retrieval over relational database. Although generic CNN and RNN learn the grammar structure of SQL when trained with sufficient samples, the accuracy and training efficiency of the model could be dramatically improved, when the translation model is deeply integrated with the grammar rules of SQL. We present a new encoder-decoder framework, with a suite of new approaches, including new semantic features fed into the encoder, grammar-aware states injected into the memory of decoder, as well as recursive state management for sub-queries. These techniques help the neural network better focus on understanding semantics of operations in natural language and save the efforts on SQL grammar learning. The empirical evaluation on real world database and queries show that our approach outperform state-of-the-art solution by a significant margin.

Basics of SQL for Audit Data Retrieval and Analysis

2021 ◽  
Author(s):  
Nuriddin Tojiboyev ◽  
Deniz Appelbaum ◽  
Alexander Kogan ◽  
Miklos Vasarhelyi
Keyword(s):  
Query Language ◽  
Data Extraction ◽  
Data Retrieval ◽  
Business Environment ◽  
University Of Arkansas ◽  
Software Applications ◽  
Audit Data ◽  
Audit Evidence ◽  
Structured Query Language

The purpose of this teaching note 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. The note demonstrates data extraction using Microsoft (MS) Access, one of the simplest SQL compliant database software applications. We use a dataset fragment 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 teaching note can be used to prepare future auditors for the emerging data-rich and technology-driven business environment.

scholarly journals Novel Model for the Computation of Linguistic Hedges in Database Queries

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Bala Murugan Vanniappan
Keyword(s):  
Query Language ◽  
Query Languages ◽  
Application Layer ◽  
Data Repositories ◽  
Database Queries ◽  
Complex Queries ◽  
Fuzzy Query ◽  
Structured Query Language ◽  
Linguistic Hedges ◽  
Novel Model

Most query languages are designed to retrieve information from databases containing precise and certain data using precisely specified commands. Due to the advancements in various kinds of data repositories in the recent years, there is a steep increase in complex queries. Most of the complex Queries are uncertain and vague. The existing Structured Query Language exhibits its inefficiency in handling these complex Queries. This paper proposes a model to handle the complexities by using fuzzy set theory. In this model, the Fuzzy Query with linguistic hedges is converted into Crisp Query, by deploying an application layer over the Structured Query Language.

scholarly journals Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre

2021 ◽  
Vol 8 ◽  
Author(s):  
Benjamin Hunter ◽  
Sara Reis ◽  
Des Campbell ◽  
Sheila Matharu ◽  
Prashanthi Ratnakumar ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Query Language ◽  
Lung Nodule ◽  
Service Evaluation ◽  
Support Vector ◽  
Lung Nodules ◽  
Cancer Centre ◽  
Structured Query Language

Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation.Objective: To automate lung nodule identification in a tertiary cancer centre.Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients.Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy.Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.

Natural Language for Querying Geographic Databases

2021 ◽  
pp. 148-181
Author(s):  
Ghada Landoulsi ◽  
Khaoula Mahmoudi
Keyword(s):  
Natural Language ◽  
Query Language ◽  
Human Diseases ◽  
Temporal Data ◽  
Geographic Databases ◽  
Hazard Characterization ◽  
Wide Range ◽  
Structured Query Language ◽  
Geographic Data ◽  
Spatio Temporal

The amount of spatio-temporal data is growing as is its potential in improving several fields (such as hazard characterization and human diseases). Meanwhile, several problems have risen and concern specially retrieving, storing, and interpreting spatio-temporal phenomena. In fact, there is a need today to make the exploitation of this flood of information popularized for a wide range of users. Although this is not the case since now, generally managing such data requires specific skills, especially the structured query language (SQL) expertise. To profit a wide range of users from this technology, natural language is to be exploited to bridge the gap between non-expert users and geographic data exploitation. This is the scope of the chapter.

scholarly journals A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube

AI ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 720-737
Author(s):  
Fadi H. Hazboun ◽  
Majdi Owda ◽  
Amani Yousef Owda
Keyword(s):  
Natural Language ◽  
Data Warehouse ◽  
Language Processing ◽  
Relational Databases ◽  
Query Language ◽  
Response Speed ◽  
System Response ◽  
Complex Query ◽  
Structured Query Language ◽  
Self Service

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.

Sign in / Sign up

Export Citation Format

Share Document