An intelligent automatic query generation interface for relational databases using deep learning technique

Sangeetha, J.; Hariprasad, Roopa

doi:10.1007/s10772-019-09624-7

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…In order to make database accessible interfaces more intelligent and capable of understanding natural language expressions, many researchers have explored the automatic generation of SQL language from natural language [29][30][31]. Sangeetha [32] focused on mapping spoken natural language into words forming the foundation of SQL by using a dictionary to record semantic sets for columns and tables. Speech recognition techniques are utilized to convert spoken language input into text, and semantic matching techniques are employed to convert natural language queries into SQL words [33].…”

Section: Ralated Workmentioning

confidence: 99%

A Method for Automatically Generating Join Queries Based on Relations-Attributes Distance Matrix over Data Lakes

2023

Teh. vjesn.

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Techniques for identifying joinable or unionable tables in data lakes can yield valuable information for data scientists. However, more than half of their working time is spent familiarizing themselves with the metadata and correlations of datasets. Simplifying the use of information in data lakes is crucial for enhancing their utilization. The existing solution of integrating correlated relations into a single large data table via full disjunction requires integration updating when either data or metadata changes, complicating data maintenance. This paper proposes a method for automatically generating join queries based on the distance matrix of relations and attributes in data lakes. The distance matrix only requires updating when metadata changes, simplifying data maintenance. Experimental results demonstrate that once the distance matrix is generated, the time required to generate the join queries is negligible. Compared to the existing solution, the time cost for executing join queries over correlated tables is nearly identical to that of selection queries over integrated tables. The results of these two queries are also the same, showcasing the effectiveness and efficiency of our method.

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Section: Ralated Workmentioning

confidence: 99%

A Method for Automatically Generating Join Queries Based on Relations-Attributes Distance Matrix over Data Lakes

2023

Teh. vjesn.

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“…Sangeetha, et al [22] used an advanced deep learning technique to develop an intelligent automatic query generator framework. The article covers how SQL is built on translating spoken natural language queries into words.…”

Section: A Query Based Aggregationmentioning

confidence: 99%

A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)

Kolarkar,

Kumar

2023

IJRITCC

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Historically, databases have been the most crucial issue in the study of information systems, and they constitute an essential part of all information management systems. Since, it complicated due to restricting the number of potential users, particularly non-expert database users who must comprehend the database structure to submit such queries. Natural language interface (NLI), the simplest method to retrieve information, is one possibility for interacting with the database. The transformation of a natural language query into a Structured Query (SQL) in a database is known as a "Natural Language Interface to Database" (NLIDB). This study uses NLIDB to handle the works performed under various aggregations with aggregation functions, a grouping phrase, and a possessing clause. This study carefully examines the numerous systematic aggregation approaches utilized in the NLIDB. This review provides extensive information about the many methods, including query-based, pattern-based, general, keyword-based NLIDB, and grammar-based systems, to extract data for a dissertation from a generic module for use in such systems that support query execution utilizing aggregations.

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Generation of Structured Query Language from Natural Language Using Recurrent Neural Networks

Kunhi¹,

Shetty²

2022

Lecture Notes in Networks and Systems

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An intelligent automatic query generation interface for relational databases using deep learning technique

Cited by 9 publications

References 15 publications

A Method for Automatically Generating Join Queries Based on Relations-Attributes Distance Matrix over Data Lakes

A Method for Automatically Generating Join Queries Based on Relations-Attributes Distance Matrix over Data Lakes

A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)

Generation of Structured Query Language from Natural Language Using Recurrent Neural Networks

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