Query Builder with Horizontal Aggregations in Data Mining for Big Data Analysis

Big data has all types of data such as structured, unstructured, sensor, video and audio data. Generating the data set from those types of data is normally time consuming activity. The Big data analysis is gathering the data with respective data-mining processes. Many aggregating columns, joining tables, complex queries, and many combinations of sql queries are used in this process. The SQL aggregations result one column per aggregated group and so having the limitations to prepare data sets. This Query builder proposes three basic processes to formalize the horizontal aggregations: CASE: Overtaking programming the CASE construct. SPJ: these queries are based on standard relational algebra operators. PIVOT: few of the DBMS using this PIVOT operator to evaluate the large tables using the query evaluation methods. In the performance comparison of the CASE method to the PIVOT operator, CASE has more speed and quicker than the method SPJ. In general both methods exhibit linear scalability, whereas the SPJ method does not. Horizontal aggregation is the new class of functions which construct datasets using a de-normalized HA layout. This can be used in most of the data mining algorithms. © 2017, Institute of Advanced Scientific Research, Inc. All rights reserved.