A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube
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 effe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/2/4/43 |
| _version_ | 1797506979939221504 |
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| author | Fadi H. Hazboun Majdi Owda Amani Yousef Owda |
| author_facet | Fadi H. Hazboun Majdi Owda Amani Yousef Owda |
| author_sort | Fadi H. Hazboun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:40:10Z |
| format | Article |
| id | doaj.art-04e9911325a745d5976cc35fb030b4e3 |
| institution | Directory Open Access Journal |
| issn | 2673-2688 |
| language | English |
| last_indexed | 2024-03-10T04:40:10Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj.art-04e9911325a745d5976cc35fb030b4e32023-11-23T03:24:37ZengMDPI AGAI2673-26882021-12-012472073710.3390/ai2040043A Natural Language Interface to Relational Databases Using an Online Analytic Processing HypercubeFadi H. Hazboun0Majdi Owda1Amani Yousef Owda2Department of Natural, Engineering and Technology Sciences, Arab American University, Ramallah P600, PalestineDepartment of Natural, Engineering and Technology Sciences, Arab American University, Ramallah P600, PalestineDepartment of Natural, Engineering and Technology Sciences, Arab American University, Ramallah P600, PalestineStructured 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.https://www.mdpi.com/2673-2688/2/4/43OLAP hypercubeSQLnatural language processingnatural language querydata warehouse |
| spellingShingle | Fadi H. Hazboun Majdi Owda Amani Yousef Owda A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube AI OLAP hypercube SQL natural language processing natural language query data warehouse |
| title | A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube |
| title_full | A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube |
| title_fullStr | A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube |
| title_full_unstemmed | A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube |
| title_short | A Natural Language Interface to Relational Databases Using an Online Analytic Processing Hypercube |
| title_sort | natural language interface to relational databases using an online analytic processing hypercube |
| topic | OLAP hypercube SQL natural language processing natural language query data warehouse |
| url | https://www.mdpi.com/2673-2688/2/4/43 |
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