Contents I. Introduction
Big data has received increasing attention for its broad research and application prospects. Aiming at the fast-growing volume of digital data, the Obama Administration announced a “Big Data Research and Development Initiative” in March of 2012 [1]. By improving the ability to extract knowledge and insights from large and complex collections of digital data, the initiative promises to help solve some the US-based nation's most pressing challenges. The cost of acquiring and analyzing big data has ballooned, with financial institutions reckoned to cost $28 billion in 2012 on financial data only. Big data refers to huge datasets that are difficult to acquire, store, search, visualize, and analyze [2]. Because the volumes and complexity of data are immense and growing quickly, we are addressing the challenges of what has come to be known as “Big Data”. Big data representatively involves the following types of data [3]:
Traditional enterprise data: includes customer information from customer relationship management (CRM) systems, transactional ERP data, Web store transactions, and general ledger data.
Machine-generated/sensor data: includes Call Detail Records (CDR), weblogs, smart meters, sensors, logs and trading systems data.
Social data: includes data from micro-blogging sites like Twitter, and social media platforms like Facebook, etc.
