Generating Structured Database Queries Using Deeply-Bidirectional Natural Language Encodings

Tripathi, Rishabh; Sinha, Priyanshu; Balabantaray, Rakesh Chandra; Pujari, Sthita Pragyan; Thind, Prabhjit; Kar, Satyanarayan

doi:10.1109/icit48102.2019.00092

Cited by 1 publication

(1 citation statement)

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“…Automatic query generation. 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.…”

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.

View full text Add to dashboard Cite

show abstract

Generating Structured Database Queries Using Deeply-Bidirectional Natural Language Encodings

Cited by 1 publication

References 13 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

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