Agglomerative Approaches for Partitioning of Networks in Big Data Scenarios

Abstract

Big Data systems are often confronted with storage and processing-related issues. Nowadays, data in various domains is growing so enormously and so quickly that storage and processing are becoming the two key concerns in such large systems of data. In addition to the size, complex relationship within the data is making the system highly sophisticated. Such complex relationships are often represented as network of data objects. Parallel processing, external memory algorithms, and data partitioning are at the forefront of techniques to deal with the Big Data issues. This chapter discusses these techniques in relation to storage and processing of Big Data. The Big Data partitioning techniques, such as agglomerative approaches in particular, have been studied and reported. Network data partitioning or clustering is common to most of the network-related applications where the objective is to group similar objects based on the connectivity among them. Application areas include social network analysis, World Wide Web, image processing, biological networks, supply chain networks, and many others. In this chapter, we discuss the relevant agglomerative approaches. Relative advantages with respect to Big Data scenarios are also presented. The discussion also covers the impact on Big Data scenarios with respect to strategic changes in the presented agglomerative approaches. Tuning of various parameters of agglomerative approaches is also addressed in this chapter.