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: User Requirement Analysis in Data Warehouse Design: A Review 
Chapter Title:  User requirement analysis approach 
1. Abstract of Cited Paper (BIB001):  A method to prepare fibers of nickel oxide and solid solutions of nickel and magnesium oxides by forming a liquid film of a reaction mixture containing at least a nickel salt and a suitable auxiliary salt; reacting the film mixture with water vapor to initiate fibrous growth at a temperature of from about 700 DEG C to 1200 DEG C; continually supplying a film of the mixture over the fibers to sustain growth; and separating the fibers from any residual mixture to thereby produce oxide fibers characterized by a resistance to high temperatures. 
2. Abstract of Cited Paper (BIB002):  Building a data warehouse is a very challenging issue because compared to software engineering it is quite a young discipline and does not yet offer well-established strategies and techniques for the development process. Current data warehouse development methods can fall within three basic groups: data-driven, goal-driven and user-driven. All three development approaches have been applied to the Process Warehouse that is used as the foundation of a process-oriented decision support system, which aims to analyse and improve business processes continuously. In this paper we evaluate all three development methodologies by various assessment criteria. The aim is to establish a link between the methodology and the requirement domain. 
3. Abstract of Cited Paper (BIB003):  Information requirements analysis for data warehouse systems differs significantly from requirements analysis for conventional information systems. Based on interviews with project managers and information systems managers, requirements for a methodological support of information requirements analysis for data warehouse systems are derived. Existing approaches are reviewed with regard to these requirements. Using the method engineering approach, a comprehensive methodology that supports the entire process of determining information requirements of data warehouse users, matching information requirements with actual information supply, evaluating and homogenizing resulting information requirements, establishing priorities for unsatisfied information requirements, and formally specifying the results as a basis for subsequent phases of the data warehouse development (sub)project has been proposed. The most important sources for methodology components were four in-depth case studies of information requirements analysis practices observed in data warehousing development projects of large organizations. In this paper, these case studies are presented and the resulting consolidated methodology is summarized. While an application of the proposed methodology in its entirety is still outstanding, its components have been successfully applied in actual data warehouse development projects. 
4. Abstract of Cited Paper (BIB004):  Abstract. A data warehouse (DW) is an integrated and historised collection of data generally used to make strategic decisions by means of online analytical processing techniques. Most of the existing DW evelopment tools used nowadays in the industry focuses on the structures for data storage, e.g. applying the star or snowflake schema. We believe that DW that better suit the needs of decision makers would be delivered by concentrating more on their requirements. So far, very few approaches have been proposed to elicit DW requirements. This paper proposes a method, called CADWA, for guiding DW analyst in the elicitation of decisions-makers requirements and in their operationalization into a DW model. CADWA shifts the focus from where information from on how they should be structured and why they are needed. To comply with current practice, the approach starts with the elicitation of highlevel requirements, reuses a set of data mart (DM) models and produces a model for the new DW. The paper presents each stage of the CADWA approach, and provides illustrations with an example inspired from a real case. 
5. Abstract of Cited Paper (BIB005):  The world of data warehousing has changed remarkably since the first edition of The Data Warehouse Lifecycle Toolkit was published in 1998. With this new edition, Ralph Kimball and his colleagues have refined the original set of Lifecycle methods and techniques based on their consulting and training experience. They walk you through the detailed steps of designing, developing, and deploying a data warehousing/business intelligence system. With substantial new and updated content, this second edition again sets the standard in data warehousing for the next decade. 
6. Abstract of Cited Paper (BIB006):  In this paper we address the issue of failure of data warehousing projects due to an inadequate requirement collection and definition process. We describe a framework that can help accomplish the objective of developing a business driven, actionable set of requirements. The framework consists of a series of steps that facilitate an effective data warehouse requirement collection and definition phase. The paper also addresses the difficult yet common issue of end-users' limited availability during the requirement collection process and proposes an approach that can serve to alleviate this problem.

Literature Review:
Requirement analysis is one of the important task to ensure successful data warehouse project BIB006 ,  , [15] . It collects and restructures base information that establish DW design and development of front-end application  . It also analyzes business requirement to assist estimation of project scope and leads to ensconce project planning. Sorting out requirement analysis phase properly and systematically will ensure effective DW implementation BIB003 . Basically, user requirement analysis approach can fall within four categories: data-driven, user-driven, goal-driven and mixed-driven approaches. Data-driven approach, also known as supply-driven approach applies bottom-up technique  , BIB001 . It was introduced by BIB002 with focus on underlying operational data source as a basis to established scope and functionality of DW BIB002 , BIB007 . Data-driven approach starts with identifying all available data within transactional data source and analyses it in order to generate data mapping. Data issues pertaining data overlapping, aligning ambiguous terms, and code mapping will be sorted out to ensure mapped data can be reengineered to build logical data schema. User requirements is determined by selecting portion of data available in data mapping BIB007 . Since main role in eliciting requirements plays by database administrator, it diminishes business user involvement. On the other hand, user driven approach determines information requirement with stress involvement of business users BIB004 ,  , BIB007 ,  . Similar with data driven approach, it applies bottom up technique that collect information from specific resources, compiles and restructures it before validate with business goals. This approach starts with determining requirements of different business user at tactical level. To elicit information requirements, project manager has to deal with approaches that facilitate user participations  . All user requirements shall be documented properly before integrate for DW requirements. Integrated information requirements will be mapped to available data source before designing logical data schema. The third approach is Goal-Driven approach that applies top-down technique. It requires high level of top management involvement and focuses on their needs to align data warehouse with corporate strategy and business objectives  . Goal driven approach starts with organizing top management interview. They will specify business goals and determines its priority. Each business goal will trigger business questions on how to measure its achievement. Prioritizing business questions will assist to explicitly define most important business question that will determine data elements and hierarchies. In order to design logical data schema, it is important to identify measurements characteristic of each business questions. This information then could be transformed into data model.  proposed early requirement model that apply goal driven approach  . However they had expand goal driven ideas by include the important of addressing agent in user requirement analysis phase. These three basic approaches have their strengths and weaknesses that will be discussed in Section III. However, to strengthen user requirement analysis approach, some Mixed-Driven Approaches being proposed.  proposed a combination of goal driven and data driven approach called GRAnD  . The whole process of defining user requirement is divides into three main phases that are organizational modelling, decisional modelling and mixed design framework. It starts with understanding organizational objectives by interviewing stakeholders and identifying fact and attribute from their objectives. Then, in second phase, decision maker will be interviewed to get picture of business goal from their point of view. These goals will be extended to get fact, dimension and measurement. The final phase is to map both rationale diagrams with data source and enhance it with hierarchy construction. The finding will go through final refinement process that will rearrange conceptual schema to ensure it meet user's need. Other approaches had mixed goal driven and user driven approach [3]  . BIB005 introduces CADWA approach BIB005 that starts eliciting requirements by studying informal needs expressed by decision makers and summarize it into strategic plan. End user will assist in mapping user requirement and extend it to more detail action plan to achieve macro and micro goals. Final plan will then be used to build conceptual model of Data Mart (DM). On the other hand,  classify goals into three levels; strategic, decision and information goals  . Strategic goals are subjective business goal from broad view of top management. It will be detailed out by end user into decision goals that are more specific and later into information goals that will distinguish which specific information could lead to business goal. An iteration of mixed approach that combine user driven and data driven approach had been proposed by Jukic & Nicolas(2010) BIB007 . User requirement phase starts with established end user analytical needs and consider its implication on DW. User requirements model will then be modified with neglecting unsupported requirements by available data and adding potential requirements that available from data source. The acquisition will then be discussed with end user and this process will be iterated until end user satisfies with the outcome. Another mixed approached that combined user driven and data driven suggested by BIB004 BIB004 . It starts with identifying target user and dominant application that could feed information to DW. The end user will specify their requirements and then arrange it in priority sequence. This process will continue repeatedly until end user satisfied with the outcome. The main difference between this approach and approach proposed by Jukic & Nicolas (2010) BIB007 is the stage of iteration process . Finally, the requirements will be mapped with available data in data source and been presented for priority review. Kaldeich & Oliveira e Sá (2004) incorporates business process within his approach with combination of data driven, user driven and goal driven approach [4] . The approach starts with analyzing the source data and produces the Entity Relationship (ER) diagrams. This approach investigates the business process and produces business process for current process (AS-IS model). In this approach end user will be interviewed and will produce a TO-BE business process model. Then the models (AS-IS and TO-BE models) will be integrated to form a new Integrate-Process-Diagram (IPD). The IPD is used as comprehensive DW requirements and will be verified through interview with top management to ensure it could lead to achieve business goals. Another mixed driven approach that combine all three approaches is triple driven approach  . However, this approach proposes to execute all three basic approaches at same time and integrate all the result at final stage. Using goal driven approach as guideline, project manager determines business goals. To ensure successfulness of DW implementation, project manager develops organization corporate strategy and identifies main business field within the organization. Top management then identifies Key Performance Indicator (KPI) that could help measure its achievement. At the same time at data driven stage, database administrator should identify data source that relevant with DW project and analyze it to produced subject oriented data schema. On the other hand, eliciting comprehensive requirements by interviewing end user and analyze business reports should be conducted. All documented information within all three stages is then merged to produce logical data schema for DW.