Natural Language to Structured Query Language using Elasticsearch for descriptive columns

Badhya, Sourabh S; Prasad, Akshar; Rohan, Shetty; Yashwanth, YS; N, Deepamala; Shobha, G

doi:10.1109/csitss47250.2019.9031030

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…As a result, there is a strong association between rating and the increase of product item kinds throughout the course of five years of transactions. Making the best choice for the sales product item type and maximizing decisionmaking inside the business unit can both be accomplished using this information [38].…”

Section: Figure 5 Rating Affected By Productmentioning

confidence: 99%

Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

Henderi,

Irawatia,

Indra

et al. 2023

HighTech. Innov. J.

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Big data collection involves enormous amounts of raw data. To boost the sustainability of corporate value and support business intelligence and decision-making systems, in-depth data analysis is necessary. The data storage, analysis, and visualization methods, as well as the discovery of patterns and linkages, all depend on extensive data analysis. This study aims to process datasets to learn things like how ratings impact market sales transactions and how much of an impact factor connected to consumers and items have on ratings. Elasticsearch and Kibana were used for the dataset processing. This study evaluated traits related to the test parameters using a variety of test procedures. The product is scored as a representation of the product types involved in the sales transaction, and the name is assessed as a reflection of the customer. Kibana and Elasticsearch, a full-text search engine, were used in this work to do extensive data analysis on data sets. It is a visualization tool that is employed in a controlled environment to evaluate how ratings impact market exchanges for electronic goods, and it offers suggestions. The study found a substantial relationship between electronic product sales on the Amazon marketplace from 2012 to 2018. It suggested the importance of buyer constituents as users and how different product categories relate to ratings in business transactions. Doi: 10.28991/HIJ-2023-04-03-09 Full Text: PDF

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Section: Figure 5 Rating Affected By Productmentioning

confidence: 99%

Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

Henderi,

Irawatia,

Indra

et al. 2023

HighTech. Innov. J.

View full text Add to dashboard Cite

show abstract

“…Yu [34] adopted the Transformer model [26] to semantically match query request statements and query conditions to improve the correctness of the automatically generated WHERE clause in SQL statements. Badhya [35] proposed Elastic search to convert natural language inputs into optimized SQL queries, which can be understood by different databases, such as MySQL, PostgreSQL and MongoDB.…”

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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“…Once these word are identified they used bi-gram algorithm to find correct attribute and table name from the database and create the final query to be displayed to user along with its output. "Natural Language to Structured Query Language using Elasticsearch for descriptive columns" [13] uses similar approach with changes made in word embeddings.…”

Section: F Modified Co-occurrence Matrix Techniquementioning

confidence: 99%

NLI-GSC: A Natural Language Interface for Generating SourceCode

Ansari¹,

Vora²

2022

IJACSA

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There are many different programming languages and each programming language has its own structure or way of writing the code, it becomes difficult to learn and frequently switch between different programming languages. Due to this reason, a person working with multiple programming languages needs to look at documentations frequently which costs time and effort. In the past few years, there have been significant increase in the amount of papers published on this topic, each providing a unique solution to this problem. Many of these papers are based on applying NLP concepts in unique configuration to get the desired results. Some have used AI along with NLP to train the system to generate source-code in specific language, and some have trained the AI directly without pre-processing the dataset with NLP. All of these papers face two problems: a lack of proper dataset for this particular application and each paper can convent natural language into only one specified programming language source-code. This proposed system shows that a language independent solution is a feasible alternate for writing source-code without having full knowledge about a programming language. The proposed system uses Natural Language Processing to convert Natural Language into programming language-independent pseudo code using custom Named Entity Recognition and save it in XML (eXtensible Markup Language) format which is an intermediate step. Then, using traditional programming, this system converts the generated pseudo code into programming language-dependent source-code. In this paper, another novel method has been proposed to create dataset from scratch using predefined structure that is filled with predefined keywords creating unique combination of training dataset.

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Natural Language to Structured Query Language using Elasticsearch for descriptive columns

Cited by 8 publications

References 2 publications

Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

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

NLI-GSC: A Natural Language Interface for Generating SourceCode

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