Extending BigTim Distributed Clustering Platform to Support Mobile Devices

The majority of present clustering algorithms are designed to work in a sequential manner processing offline data on a single machine. Different approaches were considered for parallelizing the existing clustering algorithms to run in a parallel manner for increasing the size of the dataset that can be processed by a computational system. This paper suggests an extended architecture of the BigTim platform such that to be capable of running clustering algorithms in a distributed manner on mobile devices. The paper presents an enhanced version of the BigTim platform, which will run clustering algorithms on a cluster of devices composed by mobile devices (mobile phones and tablets) and computers connected through a network and working together in a MapReduce manner.