Towards a New Approach to Empower Periodic Pattern Mining for Massive Data using Map-Reduce
Rami Ibrahim, M. Omair Shafiq · 2018
Recent applications like social networks and IoT are the main source of the massive amount of data generated every day. Time series data is a major form where data is sequenced and indexed by timestamps. Multiple data mining techniques are applied to discover the behavior of time series datasets, periodic pattern mining is one of them. Many sequential pattern mining algorithms were presented, some of them built suffix trees and performed early pruning while other algorithms used pattern-growth techniques such as projection. A few algorithms performed Apriori-based techniques where lattice trees were built and traversed. However, most algorithms suffer from time and space issues when mining large scale time series sequences. In our paper, we present a solution that utilizes advanced and sophisticated distributed systems such as MapReduce framework. It splits the original sequence and distributes its segments across thousands of nodes in the MapReduce infrastructure. We use different training datasets to evaluate both traditional pattern mining algorithms and our MapReduce solution. After analyzing our solution in terms of time complexity, efficiency and accuracy, we clarify the advantages of processing data segments using periodic pattern mining along with MapReduce framework.