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Big Data has become ubiquitous across all areas of research allowing for new applications that were not possible earlier. Unlike software development relying on traditional data sources, Big Data applications present their own unique challenges to appropriately harness the utility of the Apache Hadoop architecture. In this paper, we introduce fundamental concepts of Hadoop and explore its usage as well as future direction. We also present our strategy for exploring the Hadoop architecture including addressing issues of scalability, customization of code and utilization of programming techniques.

The ever-increasing amount of information flowing through Social Media presents numerous opportunities for the generation of Business Intelligence. Challenges exist in the leveraging of these data sources due to their heterogeneity and unstructured content. This paper presents the application of Semantic Computing to Social Media for industrial application, focusing on topic identification and behavior prediction. The methodologies described can benefit many areas of an organization including support of marketing, customer service, engineering and public relations. Results demonstrate that business operations can be substantially enhanced through application of Semantic Computing to Social Media.

With rapidly expanding data collections becoming increasingly available, the application of Semantic Computing has become imperative to leverage this resource for industrial applications. This paper presents a survey of Semantic Computing in the area of Business Intelligence. We examine semantic analytical techniques and tools as applied for prediction analysis and decision support. We also define the role of Semantic Computing as applied in the context of Data Mining, Text Mining and Big Data Analytics. Additionally, we describe how business data is queried with Structured Natural Language as well as the use of On-Line Analytic Processing.