SQL access and ANSI/ISO SQL and X/Open

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.

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.

Chapter 2 - Querying

It is noted that, even though SQL (structured query language) was invented in 1974, it did not become a standard until late 1986. Between 1974 and 1986, many vendors developed relational database systems based on the SQL language. Thus, even though many vendors support the SQL language, the implementations are not necessarily compatible because they were developed before an official standard was available. Additionally, users are requesting connectivity between relational databases on personal computers, minicomputers, and mainframe computers. The steps that are being taken by the DEC SQL Access Group to solve these problems are described. >

Use of computer aided management information systems in the food and beverage industry in Germany

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

This paper applies web services using the common information model (CIM) to exchange measurement data from the transformer terminal unit (TTU) to the distribution management system (ADMS) for the aging estimation of distribution transformers. An application programming interface (API) is designed by using web services such as simple object access protocol (SOAP) and representational state transfer (RESTful) for TTU measurement data exchange. The structured query language (SQL) and the not only SQL (NoSQL) databases are used to store historical TTU data for neural network (NN) model training. An optimization architecture of web service with the database is proposed to increase data exchange performance. The exchanged real-time TTU data is input into a trained NN model that can provide a reference aging factor for the utility crew to balance distribution transformer loading. Finally, the graphic user interface (GUI) is designed in this paper to display real-time information and achieve asset management of distribution transformers.

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.

chapter 7 - The basics of sql query code tuning

This paper discusses a practical approach to the use of Commercial-Off-the-Shelf (COTS) tools to implement Automatic Test Equipment (ATE) and Unit Under Test (UUT) signal-based models to support both new Test Program Set (TPS) development and TPS rehost activities. This modeling technique also provides a standard to which existing and future ATEs and UUTs can be modeled and used for analysis and comparisons. The signal-based model is implemented via a Structured Query Language (SQL) Relational Data Base Management System (RDBMS). COTS Rapid Application Development (RAD) tools, such as Microsoft/sup (R)/ Visual Basic/sup (R)/ Microsoft Access/sup (R)/ and PowerBuilder/sup (R)/, are employed to implement interfaces to the SQL database model and to provide the necessary algorithms for data comparison, model reuse and other manipulations of the model data. After models are established for both the ATE and UUTs, ATU/UUT compatibility analysis can be performed to identify any potential incompatibilities. This information can be used to optimize the selection of a target ATE for a selected group of UUTs. It can also be used to identify any signal deficiencies in the target ATE that require ATE enhancements or Interface Test Adapter (ITA) signal conditioning, to ensure UUT compatibility.

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.

With the maturity of network technology, the deterministic factor of a 'successful' web site does not as much depend on its connectivity to the Internet, rather the content of web pages plays a more important role. The inclusion of dynamic data, for example, has become an increasingly desirable feature of a web page. Web servers that support dynamic web pages are capable of retrieving data at the time of user requests. Rather than being statically included in the web page, these dynamically retrieved data may physically reside in various file systems or database servers across the Internet.JAVA, a new object-oriented programming language, has become a buzzword since its introduction by Sun Microsystems in 1994 [1, 2, 3]. It was claimed that JAVA, via its ability to embed applets in a web page, would make the content of a web page alive and dynamic. Data and information on a web page can now be updated dynamically when users request them.In this paper, we discuss our experience of developing JAVA applets that use various types of JDBC (JAVA Database Connectivity) drivers to connect to and then manipulate data stored in SQL (Structured Query Language) database servers. We first start with the discussion of general client/server model involving JDBC, and then introducing the procedure of setting up programming projects employing this approach in an undergraduate database course.

Databases have been always the most important topic in the study of information systems, and an indispensable tool in all information management systems. However, the extraction of information stored in these databases is generally carried out using queries expressed in a computer language, such as SQL (Structured Query Language). This generally has the effect of limiting the number of potential users, in particular non-expert database users who must know the database structure to write such requests. One solution to this problem is to use Natural Language Interface (NLI), to communicate with the database, which is the easiest way to get information. So, the appearance of Natural Language Interfaces for Databases (NLIDB) is becoming a real need and an ambitious goal to translate the user’s query given in Natural Language (NL) into the corresponding one in Database Query Language (DBQL). This article provides an overview of the state of the art of Natural Language Interfaces as well as their architecture. Also, it summarizes the main recent advances on the task of Natural Language Interfaces for databases.

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.

Detailed kinetic models are widely used in the field of combustion chemistry for the design and development of combustors, engines, fuels, and additives. Due to the proliferation of models and the increasingamount of data encapsulated within each model, ambiguities, discrepancies, and mistakes are ever present, leading to incorrect scientific conclusions and inaccurate predictions. Significant efforts have been made to improve the quality of published kinetic models via semi-automated cross-referencing tools, but there remains a need for the consolidation of models in a medium that supports their continued analysis, use, revision, and overall contribution. The relationships between resonance structures, isomers, species, species names, thermochemical parameters, chemical reactions, reaction rate expressions, and metadata, are complex, especially when many of these are re-used across different kinetic models. A Structured Query Language (SQL) database schema paired with a web application and RESTful API, implemented in a Python-based web framework, is proposed to store detailed kinetic models and expose a rich interface for data analysis, model comparison, and data contribution, all within a readable, extensible, and maintainable code base appropriate for an academic research setting.--Author's abstract