ADDITIONAL READING
Shabeera, T. P., & Madhu Kumar, S. D. (2015). Optimizing virtual machine allocation in MapReduce cloud for improved data locality. International Journal of Big Data Intelligence., 2(1), 2-8. doi:10.1504/IJBDI.2015.067563
Srivastava, U., & Gopalkrishnan, S. (2015). Impact of Big Data Analytics on Banking Sector: Learning for Indian Bank. Big Data. Cloud and Computing Challenges., 50, 643-652.
Tiwari, P. K., & Joshi, S. (2015). Data security for software as a service. International Journal of Service Science, Management, Engineering, and Technology, 6(3), 47-63.
Wahi, A. K., Medury, Y., & Misra, R. K. (2014). Social Media: The core of enterprise 2.0. International Journal of Service Science, Management, Engineering, and Technology, 5(3), 1-15. doi:10.4018/ijssmet.2014070101
Wahi, A. K., Medury, Y., & Misra, R. K. (2015). Big Data: Enabler or Challenge for Enterprise 2.0. International Journal of Service Science, Management, Engineering, and Technology, 6(2), 1-17. doi:10.4018/ijssmet.2015040101
KEY TERMS AND DEFINITIONS
Big Data: is the voluminous and complex collection of data that comes from different sources such as sensors, content posted on social media website, sale purchase transaction etc. Such voluminous data becomes tough to process using ancient processing applications.
Value: This refers to the intrinsic value that the data may possess, and must be discovered.
Variety: This refers to various types of structured, unstructured and semi- structured data types.
Velocity: This refers to the rate at which data is generated and received.
Volume: This refers to the amount of data been generated from different sources such as data logs from twitter, click streams of web pages and mobile apps, sensor- enabled equipment capturing data, etc.
