ABSTRACT
Challenges in Big Data analysis include data inconsistency, incompleteness, scalability, timeliness, and data security. The fundamental challenge is the existing computer architecture. For several decades, the latency gap between multicore CPUs and mechanical hard disks has increased each year, making the challenges of data-intensive computing harder to overcome (Hey et al. 2009). A systematic and general approach to these problems with a scalable architecture is required. Most of the Big Data is unstructured or of a complex structure, which is hard to represent in rows and columns. A good candidate for a large design space can efficiently solve the Big Data problem in different disciplines. This chapter highlights two specific objectives:
