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

In the present scenario, every user is not familiar with the use of Structured Query Language (SQL). So, user is not able to understand or write complex queries in SQL. An enhanced application with intelligent interface is needed to improve communication between the naive user and the databases application. The database is an efficient structure for handling data. To understand the structure of the database, a user has to learn SQL. The non-expert users who are not familiar with the use of SQL, need a system by which the user can interact with the database in their natural language. The system must have the ability to understand the natural language and interact with the database accordingly. In this research, an improved system with three-tier architecture is developed. Pattern matching and semantic matching techniques are used to develop the system which transforms natural language into SQL queries. The SQL query is generated with the production rules and the predefined data dictionary. The predefined data dictionary contains semantics for attributes and relation among attributes. The input given by the user is transformed by the system into SQL query by passing through steps like tokenization, escape word removal, classification of elements and query formation. Finally, the output is in the form of a SQL query. The results are compared with the existing system. The results given by the proposed system are better than the existing system. The proposed system has better recall value, accuracy, and precision in comparison to existing systems.

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.

Extraction of knowledge data from knowledge database using natural language query is a difficult task. Different types of natural language processing (NLP) techniques have been developed to handle this knowledge data extraction task. This paper proposes an automated query-response model termed Extended Automated Knowledge Provider System (EAKPS) that can manage various types of natural language queries from user. The EAKPS uses combination based technique and it can handle assertive, interrogative, imperative, compound and complex type query sentences. The algorithm of EAKPS generates structure query language (SQL) for each natural language query to extract knowledge data from the knowledge database resident within the EAKPS. Extraction of noun or noun phrases is another issue in natural language query processing. Most of the times, determiner, preposition and conjunction are prefixed to a noun or noun phrase and it is difficult to identify the noun/noun phrase with prefix during query processing. The proposed system is able to identify these prefixes and extract exact noun or noun phrases from natural language queries without any manual intervention.

SummaryThe Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation.To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.

Traditionally, data used in OLAP (online analytical processing) have been limited to the contents of the data warehouse of a company. However, the needs for analysis are often more demanding and data are needed from different sources. In this article, we study how the semantics of data sources can be described to allow combining data from several sources into an OLAP cube. We apply Semantic Web technologies for defining an OWL/RDF ontology for OLAP data sources and OLAP cubes. These definitions are then utilised in OLAP cube formation by posing an OWL/RDF ontology-based query against them. We use Grid technologies to enhance the efficiency of processing and ensuring security. Our primary interest is in the cube construction (i.e., ETL process), and we assume that standard OLAP methods can be used for the actual analysis. Our tests show that the proposed approach can speed up the construction of an OLAP cube for ad hoc queries by supporting a high-level query language and reducing the amount of required data.

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.

In recent past, the bookstores generate more data such as customer details, transaction details, book details, etc. for analyzing them to make data-driven decisions. These data are stored in a centralized database. In centralized database system, the data are stored in a single site or location and that are managed. The major drawback is that if the centralized system gets crashed the entire data are lost and also it makes access bottleneck when more users need to access the database. In order to overcome these limitations, the distributed database system is used to store the business data by locating them in different databases that are located in different sites in different location. The distributed database maintains the replication and duplication that ensure the availability and fault tolerance on the data. The distributed system also provides the high accessibility to the users. Thus, this paper presents a distributed database for a bookstore to improve availability, fault tolerance, and accessibility. This distribution also maintains the transparency and supports the database properties such as atomicity, consistency, inheritance and durability (ACID). Furthermore, this distributed database is designed with four sites and each site keeps the bookstore data for access and analyses. This distributed database is constructed and accessed using structured query language (SQL) with MySQL relational database management system (RDBMS). It also provides the security and work even in offline mode. Keywords: distributed databases; query processing; relational database management; structured query language; bookstore database;

6 - The Basics of Efficient SQL

Radio Frequency Identification (RFID) technology is used in many applications for monitoring object movement. The use of RFID in supply chain management systems enables to track the movement of products from suppliers to warehouses, store backrooms, and eventually points of sale. The vast amount of data resulting from the proliferation of RFID readers and tags poses challenges for data management and analytics. RFID data warehousing can enhance data quality and consistency, and give great potential benefits for Online Analytical Processing (OLAP) applications. Traditional data warehouses are built primarily on relational database management systems. However, the size of RFID data being collected and analyzed in the industry for business intelligence is growing rapidly, making traditional warehousing solutions prohibitively expensive. Hive is an open-source data warehousing solution built on top of Hadoop which is a popular Big Data computing framework. This paper presents alternative RFID data warehouse designs which can handle a large amount of RFID data and support a variety of OLAP queries. The proposed approaches are implemented on Hive and evaluated for query performance in cloud computing environment.

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

Most of commercial websites provide customers with menu-driven navigation and keyword search. However, these inconvenient interfaces increase the number of mouse clicks and decrease customers' interest in surfing the websites. To resolve the problem, we propose an information retrieval assistant using a natural language interface in online sales domains. The information retrieval assistant has a client-server structure; a system connector and a NLP (natural language processing) server. The NLP server performs a linguistic analysis of users' queries with the help of coordinated NLP agents that are based on shallow NLP techniques. After receiving the results of the linguistic analysis from the NLP server, the system connector interacts with outer information provision systems such as conventional information retrieval systems and relational database management systems according to the analysis results. Owing to the client-server structure, we can easily add other information provision systems to the information retrieval assistant with trivial modifications of the NLP server. In addition, the information retrieval assistant guarantees fast responses because it uses shallow NLP techniques. In the preliminary experiment, as compared to the menu-driven system, we found that the information retrieval assistant could reduce the bothersome tasks such as menu selecting and mouse clicking because it provides a convenient natural language interface.

The article describes the methodology developed for a data warehousing process developed in a Chilean University. The purpose of the process is to generate indicators of academic productivity. For this a methodology is elaborated integrating diverse approaches techniques and methodologies such as: specification of information requirements relational modeling combined development model of Kimball and Hefesto proposals process of extraction-transformation-load (ETL) with a validation phase of indicators and integrated and interactive visualizations for the multidimensional analysis of the indicators with the use of dashboards. In this process three elements incorporated that in the opinion of the development team favored the success and effectiveness of the development of the solution. Firstly how to specify key performance indicators (KPI) in detail through the development of a template based on those used to specify information requirements in a software process. Second to explain the incorporation of a validation phase of the KPI obtained in the ETL process based on SQL (Structured Query Language) and the comparison with the data of the operational systems. Also in this ETL process is established as necessary and sufficient the use of a temporary repository as an area for data integration through a relational database. Thirdly integrated visualizations configured with an On-Line Analytical Processing (OLAP) tool which interactively display the related indicators for the same activity under analysis. Because of this data warehousing process you get a business intelligence platform based on a DataMart model with two schemes one star and another snowflake. The first contains the indicators of teacher productivity and the second those of scientific productivity which satisfy the specifications of the KPI and defined in agreement with the end users.

Multiple criteria decision making usually means selection of the best objects or their parameters which best meet conditions or criteria. Human decision making often involves also uncertain and vague criteria. The terms as a short distance, a high building or long time are commonly used in human speech. Nevertheless, it is not necessary to describe them exactly. In case of decision making based on lot of data and multiple criteria, data are usually stored in databases and a query language is used for handling and querying them. In this case, the criteria should be defined in a formal computer language. The standard for querying data in relational databases is the Structured Query Language (SQL). All objects and phenomena, and everything people do, is experience in space and time. Space and time, therefore, should be used as a framework for querying and reasoning about information stored in database systems. Databases, in which spatial and temporal data types are included and functions for their handling are supported, are called the spatio-temporal databases. To spatio-temporal query a database, an extension of standard SQL to support spatial and temporal data is needed. Uncertain spatio-temporal queries are not yet standardized and they are a current topic of research. For uncertain queries creation and expression of uncertain decision criteria, development of new methods and techniques is required. One of the most widely used approach to model, analyse and process uncertain and vague data is fuzzy set theory. Therefore, in this paper, we propose a new way of application fuzzy set theory to spatio-temporal querying. For the case study realisation, the open source PostgreSQL database system extended by the PostGIS we have used. The implementation of the proposed principles of fuzzy set theory to spatio-temporal querying databases can bring an opportunity for the efficient decision making based on multiple uncertain criteria.

447 Background: Mortality from bladder cancer (BC) increases exponentially once it invades the muscle. At the population level, accurate delineation of these patients is challenging. Methods: To develop and validate a natural language processing (NLP) model for automatically identifying muscle-invasive BC (MIBC) patients, aiding in population-based BC research. All patients with a CPT code for transurethral resection of bladder tumor (TURBT) (N = 76,060) were selected from the Department of Veterans Affairs (VA) Corporate Data Warehouse database. A sample of 600 patients (with 2,337 full-text notes) who had TURBT and confirmed pathology results were selected for NLP model development (500 patients) and validation (100 patients). Muscle-invasion (yes/no), unknown, or no cancer, were confirmed by detailed chart review of pathology notes. The NLP performance was assessed by calculating the sensitivity, positive predictive value (PPV), and overall accuracy at the individual note and patient levels. Results: In the validation cohort, the NLP model had overall accuracy of 88% and 92% at the note and patient levels. Specifically, PPV and specificity for predicting muscle-invasion on note level were 83% and 70%, respectively. The model classified non-muscle invasive BC (NMIBC) with 98% sensitivity at both the note and patient levels. Although the sensitivity for MIBC was 70% for note-level determination, the sensitivity was 86% when evaluated at the patient level. When applying the model to 71,200 patients VA-wide, the model classified 13,642 (19%) as having MIBC and 47,595 (66%) as NMIBC. The NLP model was able to identify invasion status for 96% TURBT patients at the population level. Inherent limitations include relatively small training set given the size of the VA population. Conclusions: This NLP model for identifying muscle-invasion at the population level had high accuracy. The NLP model may be a practical and accurate tool for efficiently identifying BC invasion status and may potentially aid in population-based BC research in the VA.

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.