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:  Big Databases 
1. Abstract of Cited Paper (BIB001):  With the development of the Internet and cloud computing, there need databases to be able to store and process big data effectively, demand for high-performance when reading and writing, so the traditional relational database is facing many new challenges. Especially in large scale and high-concurrency applications, such as search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. In this case, NoSQL database created. This paper describes the background, basic characteristics, data model of NoSQL. In addition, this paper classifies NoSQL databases according to the CAP theorem. Finally, the mainstream NoSQL databases are separately described in detail, and extract some properties to help enterprises to choose NoSQL. 
2. Abstract of Cited Paper (BIB002):  Your guide to the world and technology of NoSQL Overview First hand, detailed information about NoSQL technology Learn the differences between NoSQL and RDBMS and where each is useful Understand the various data models for NoSQL Compare and contrast some of the popular NoSQL databases on the market Think outside the box; learn to design, construct, and implement using NoSQL In Detail Relational databases have been used for decades, and in the last few years NoSQL has been a growing choice for large-scale web applications. Non-relational databases provide the scale and speed that you may need for your application. To switch you must know the options available, the advantages and drawbacks, and scenarios which it is suited to the most and where it should be avoided at all costs. Getting Started with NoSQL is a from-the-ground up guide that takes you from the very first steps to a real-world NoSQL application. It provides you with a step-by-step approach to design and implement a NoSQL application that will help you make clear decisions on database choices and database model choices. The book is suited for a developer, an architect, as well as a CTO. This book is a comprehensive guide to working with NoSQL. You will learn to make key decisions, and to design and implement NoSQL applications. You will learn about NoSQL jargon, data models, and databases on the market. The case studies and comparisons presented will help you to make a decision on whether or not to use NoSQL, and if so which model and product to use. This book is an indispensable resource for you to have in your library. You will learn everything you need to know about understanding and working with NoSQL and how to implement an application with the correct NoSQL for you. What you will learn from this book Discover what NoSQL is and what it is not Master the jargon and vocabulary around NoSQL Understand the pros and cons of NoSQL Learn where to implement NoSQL and where not to Know the differences between various NoSQL data models Explore advantages and limitations of popular NoSQL databases Comprehend the differences between the NoSQL databases available How to write code specific to data models and NoSQL databases Discover the correct way of thinking for designing entities with NoSQL Approach As with all Packt books this guide will use practical examples to impart information to the reader. Who this book is written for Getting Started with NoSQL is a great resource for someone starting with NoSQL and an indispensable guide for technology decision makers. It is assumed that you have a background in RDBMS modeling and SQL and have had exposure to at least one of the programming languages Java or JavaScript. 
3. Abstract of Cited Paper (BIB003):  It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as dataintensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing.

Literature Review:
As relational databases fall short of big data requirements, there needed to seek alternative technologies. As an alternative to RDBMS, NoSQL databases are the most prominent candidates to meet big data requirements. NoSQL databases offer asolution for big data requirements which are hard to solve with RDBMS such as schema flexibility, complex queries, data update, and scalability. RDBMS is quite inflexible to take into account unplanned future needs. Especially updating schema for new design demands is not easy because of constraint, integrity, and normalization restrictions. But all of these don't mean RDBMS and NoSQL do the opposite things or RDBMS is totally useless. NoSQL offers high storage size and performance and high availability at the expense of losing the ACID (Atomic, Consistent, Isolated, Durable) trait of RDBMS in exchange for the weaker BASE (Basic Availability, Soft state, Eventual consistency) feature BIB001 . Although there is no official classification of NoSQL databases BIB001 classification can make by following: (1) key-value stores, (2) document databases, (3) column-oriented databases, and (4) graph stores BIB004  BIB002  BIB003 .