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: Big data storage technologies: a survey 
Chapter Title:  Evolution of big data storage technologies 
1. Abstract of Cited Paper (BIB001):  In this paper, we examine a number of SQL and socalled "NoSQL" data stores designed to scale simple OLTP-style application loads over many servers. Originally motivated by Web 2.0 applications, these systems are designed to scale to thousands or millions of users doing updates as well as reads, in contrast to traditional DBMSs and data warehouses. We contrast the new systems on their data model, consistency mechanisms, storage mechanisms, durability guarantees, availability, query support, and other dimensions. These systems typically sacrifice some of these dimensions, e.g. database-wide transaction consistency, in order to achieve others, e.g. higher availability and scalability. 
2. Abstract of Cited Paper (BIB002):  Argumentation provides a sophisticated yet powerful mechanism for the formalization of commonsense reasoning in knowledge-based systems, with application in many areas of Artificial Intelligence. Nowadays, most argumentation systems build their arguments on the basis of a single, fixed knowledge base, often under the form of a logic program as in Defeasible Logic Programming or in Assumption-Based Argumentation. Currently, adding new information to such programs requires a manual encoding, which is not feasible for many real-world environments which involve large amounts of data, usually conceptualized as relational databases. ::: ::: This paper presents a novel approach to compute arguments from premises obtained from relational databases, identifying several relevant aspects. In our setting, different databases can be updated by external, independent applications, leading to changes in the spectrum of available arguments. We present algorithms for integrating a database management system with an argument-based inference engine. Empirical results and running-time analysis associated with our approach show that it provides a powerful alternative for efficiently achieving massive argumentation, taking advantage of modern DBMS technologies. We contend that our proposal is significant for developing new architectures for knowledge-based applications, such as Decision Support Systems and Recommender Systems, using argumentation as the underlying inference model. 
3. Abstract of Cited Paper (BIB003):  Big data refers to data volumes in the range of exabytes (1018) and beyond. Such volumes exceed the capacity of current on-line storage systems and processing systems. Data, information, and knowledge are being created and collected at a rate that is rapidly approaching the exabyte/year range. But, its creation and aggregation are accelerating and will approach the zettabyte/year range within a few years. Volume is only one aspect of big data; other attributes are variety, velocity, value, and complexity. Storage and data transport are technology issues, which seem to be solvable in the near-term, but represent longterm challenges that require research and new paradigms. We analyze the issues and challenges as we begin a collaborative research program into methodologies for big data analysis and design. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  Like all software systems, databases are subject to evolution as time passes. The impact of this evolution can be vast as a change to the schema of a database can affect the syntactic correctness and the semantic validity of all the surrounding applications. In this paper, we have performed a thorough, large-scale study on the evolution of databases that are part of larger open source projects, publicly available through open source repositories. Lehman?s laws of software evolution, a well-established set of observations on how the typical software systems evolve (matured during the last forty years), has served as our guide towards providing insights on the mechanisms that govern schema evolution. Much like software systems, we found that schemata expand over time, under a stabilization mechanism that constraints uncontrolled expansion with perfective maintenance. At the same time, unlike typical software systems, the growth is typically low, with long periods of calmness interrupted by bursts of maintenance and a surprising lack of complexity increase.

Literature Review:
In this section we discuss the technological shift from relational, well-structured databases to nonrelational, schema-less storage technologies. The drive and challenges due to big data are summarized. Moreover, the prominent features of storage technologies specified for big data are highlighted. Most commonly used big data storage technologies are also elaborated upon. Over past few decades, relational databases have been used as well-structured data management technologies. They have been recommended for performing data management operations on structured data BIB003 . Datasets such as the Internet Movie Database (IMDB, 2015) and MovieLens (MovieLens, 2015) are available for being manipulated using relational databases. Big data and emerging technologies like cloud computing allow data to be captured from interactive and portable devices in various formats BIB004 BIB005 . The data come with the new challenges of fast retrieval, real-time processing, and interpretation over a large volume  . Unfortunately, relational databases could not evolve as fast as big data. Moreover, the support to fault tolerance and complex data structures is not satisfactory for heterogeneous data BIB006 . Furthermore, the schema of relational databases does not support frequent changes  . Google, Amazon, and Facebook are some of the well-known web data repositories. Their processing and dynamic scalability requirements are beyond the capabilities of relational databases BIB001 . Thus, continuously growing data that come with heterogeneous structures need a better solution. We summarize the comparison between relational databases and big data storage systems in Table 1 using a SWOT analysis. Big data is essential for enterprises to predict valuable business outcomes. To meet the challenges of big data, NoSQL (not only SQL) databases have emerged as enterprise solutions. NoSQL databases overcome the problems of relational databases and offer horizontally scalable, flexible, highly available, accessible, and relatively inexpensive storage solutions  . Thus, NoSQL databases have become the mostly adopted technologies for storing big data. Unlike relational databases, these technologies offer support to a large number of users interacting with big data simultaneously. NoSQL databases are great in achieving consistency, fault tolerance, availability, and support to query BIB002 . They also guarantee some distinctive features over relational databases: scalability, availability, fault tolerance, consistency, and secondary indexing.