CLASSIFICATION COMPLEX QUERY SQL FOR DATA LAKE MANAGEMENT USING MACHINE LEARNING

Abstract

A query is a request for data or information from a database table or a combination of tables. It allows for a more accurate database search. SQL queries are divided into two types, namely, simple queries and complex queries. Complex SQL is the use of SQL queries that go beyond standard SQL by using the SELECT and WHERE commands. Complex SQL queries often involve the use of complex joins and subqueries, where the queries are nested in a WHERE clause. Complex SQL queries can be grouped into two types of queries, namely, Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) queries. In the implementation of complex SQL queries in the NoSQL database, a classification process is needed due to the varying data formats, namely, structured, semi-structured, and unstructured data. The classification process aims to make it easier for the query data to be organized by type of query. The classification method used in this research is the Naive Bayes Classifier (NBC) which is generally often used in text data, and the Support Vector Machine (SVM), which is known to work very well on data with large dimensions. The two methods will be compared to determine the best classification result. The results showed that SVM was 84.61% accurate in terms of classification, and comparatively, NBC was at 76.92%.