Heterogeneous Multi core processors for improving the efficiency of Market basket analysis algorithm in data mining

TL;DR

This paper explores how heterogeneous multi-core processors can enhance the efficiency of the Market Basket Analysis algorithm by redesigning its implementation within the Map/Reduce framework, specifically using Hadoop.

Contribution

It proposes a new MB Scheduler for heterogeneous multi-core processors to optimize task execution and improve data mining performance in a parallel computing environment.

Findings

Heterogeneous cores improve processing efficiency.

Dynamic core switching enhances performance.

Optimized power and processing capabilities achieved.

Abstract

Heterogeneous multi core processors can offer diverse computing capabilities. The efficiency of Market Basket Analysis Algorithm can be improved with heterogeneous multi core processors. Market basket analysis algorithm utilises apriori algorithm and is one of the popular data mining algorithms which can utilise Map/Reduce framework to perform analysis. The algorithm generates association rules based on transactional data and Map/Reduce motivates to redesign and convert the existing sequential algorithms for efficiency. Hadoop is the parallel programming platform built on Hadoop Distributed File Systems(HDFS) for Map/Reduce computation that process data as (key, value) pairs. In Hadoop map/reduce, the sequential jobs are parallelised and the Job Tracker assigns parallel tasks to the Task Tracker. Based on single threaded or multithreaded parallel tasks in the task tracker, execution is carried out in the appropriate cores. For this, a new scheduler called MB Scheduler can be developed. Switching between the cores can be made static or dynamic. The use of heterogeneous multi core processors optimizes processing capabilities and power requirements for a processor and improves the performance of the system.