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A challenge in the research of Social Networks is the large scale analysis of graphs. One of the most valuable metrics in the evaluation of graphs is betweenness-centrality. In this paper, we define an approximation of betweenness-centrality for the purpose of building a predictive model of Social Networks. The methodology presented describes a bounded distance approximation of betweenness-centrality designed for implementation within a parallel architecture. Through our proposed design pattern, we are able to leverage Big Data technologies to determine metrics in the context of ever expanding internet-based data resources.

Due to its predictive nature, Social Media has proved to be an important resource in support of the identification of trends. In Customer Relationship Management there is a need beyond trend identification which includes understanding the topics propagated through Social Networks. In this paper, we explore topic modeling by considering the techniques of Latent Dirichlet Allocation which is a generative probabilistic model for a collection of discrete data. We evaluate this technique from the perspective of classification as well as identification of noteworthy topics as it is applied to a filtered collection of Twitter messages. Experiments show that these methods are effective for the identification of sub-topics as well as to support classification within large-scale corpora.

Big Data has become a new source of opportunity in terms of Semantic Computing. Unlike traditional data sources, Big Data applications present their own unique challenges in order to appropriately harness the utility of open source frameworks including Apache Hadoop and design patterns such as Map - Reduce. Through these advances, Big Data has become ubiquitous across all areas of research allowing for new applications that were not possible earlier. The Second IEEE International Workshop on Semantic Computing with Big Data (ICSC-SCBD'15) provides a forum for researchers to present their latest research progress, exchange ideas and thoughts, and explore new research directions. It continues seeking high quality research contributions and encouraging significant work that addresses the major challenges in the semantics as applied in the context of Big Data.