You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review on Business Intelligence and Big Data 
Chapter Title:  Data Collection 
1. Abstract of Cited Paper (BIB001):  The CAP theorem asserts that any networked shared-data system can have only two of three desirable properties. However, by explicitly handling partitions, designers can optimize consistency and availability, thereby achieving some trade-off of all three. The featured Web extra is a podcast from Software Engineering Radio, in which the host interviews Dwight Merriman about the emerging NoSQL movement, the three types of nonrelational data stores, Brewer's CAP theorem, and much more.

Literature Review:
It is about retrieving data from data generating objects and environments. Log files are automatically created when some events like clicks, inserts, updates occur. Log generation is a widely used data collection method and they are generated in various formats. For example, website logs can be in public log file format (NCSA), expanded log format (W3C) or IIS format (Microsoft). Sensors are playing more role in social and industrial life. A sensor senses some physical events like vibration, pressure, humidity, velocity, sound, electric current etc., converts them into digital format and pushes through transmission media. This media can be wired fiber or twisted cable as well as wireless waves. In RDBMS world disk units are used as storage resources for servers rather than local disks. But the storage systems designed for RDBMS are insufficient to store big data. New techniques and architectures are needed for big data storage. Big data storage is about storing and managing big data in persistent storages. A big data storage should handle very large amounts of data and keep scaling to keep up with growth and provide the input/output operations per second (IOPS) necessary for analytics tools  . With the CAP theorem, Brewer BIB002 suggests that any networked shared-data or distributed system can meet only two of following three requirements at the same time: Consistency (C) makes sure there is a single up-to-date copy of the data, but in distributed systems data is partitioned among servers in multiple pieces. C is for assuring multiple copies of data are identical. Anyone who reaches database will always see the latest state of data BIB001 . Availability (A) makes sure that users can reach, read and change data. Partition tolerance (P) is durability for network failures in distributed systems. Not only Structured Query Language (NoSQL) supporters use CAP theorem as an argument against RDBMS. Having distributed nature, NoSQL database developers can't ignore partition tolerance, so P is the first one to choose. There remains C and A. While NoSQL developers generally prefer A to C; RDBMS developers use ACID (Atomicity, Consistency, Isolation, and Durability) which focus on C. But in a recent article, Brewer BIB002 suggests that CAP theorem is misunderstood in some ways so that there is no need to totally sacrifice C or A. By explicitly handling partitions, it is possible to optimize consistency and availability, thereby achieving some trade-off of all three.