Kavosh: an...

Abstract

The immense amount of data generated on a daily basis by various devices and systems necessitates a change in data analysis methods. As an important part of analytics, data mining methods require a paradigm shift to solve problems because the old methods cannot manage massive data. Association rule mining is a data mining algorithm used to solve various domain problems. Because of the immense volume of data, one-node solutions are no longer useful, and it is necessary to solve problems by using a distributed and shared-nothing architecture such as Map-Reduce. However, when association rule mining is transferred to these architectures, new problems appear. The main problems are lack of data locality and iteration support and process skewness. In this paper, a method is proposed that solves these problems. Kavosh converts data into a unified format that helps nodes perform their tasks independently without the need to exchange data with other nodes. In addition, the proposed method compresses input data to facilitate data management. Another advantage is the lack of process skewness because it is possible to allocate a predefined amount of data to each node. Kavosh omits iterations required for finding frequent itemsets by changing the Map-Reduce architecture. The proposed method is implemented using Hadoop, and the results are compared with open-source products in terms of three aspects: execution time, load balancing and data compression. The results show that Kavosh outperforms other methods in these aspects.