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:  Introduction 
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):  Quantitive evaluations show that many data warehouse projects fail at an alarming rate, wasting all the time, money, and effort spent on them. Cost overruns and schedule delays are attributed in large part, to problems associated with requirements. Therefore, a crucial success factor for the implementation of data warehouse systems is efficient requirements management. In this paper we introduce the easyREMOTE (easy Requirements Modeling Technique for Data WareHouses) requirements management approach, which facilitates an incremental and iterative development of complete, correct, feasible, necessary, prioritized, unambiguous, and verifiable data warehouse requirements. As a foundation we present a requirements specification template with consideration of different stakeholder perspectives and the typical characteristics of data warehouse systems. We discuss traditional requirements management activities (change control, version control, status tracking, requirements tracing) in the context of data warehouse environments and extend these traditional activities to support more efficient requirements documentation and visibility. 
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:
The past few decades had shown explosive of products and services related to data warehousing   .This is as consequences of most large companies worldwide had established Data Warehouse (DW) as one of their main component in information system environment. DW has become the standard tools and essential component of decision support system BIB005 , [4] . In addition, it has been successfully deployed in many industries such as manufacturing, retail business, financial services, transportation, telecommunication, utilities and healthcare  to support decision making. DW is built to improve decision making system BIB002 BIB001 by providing consistent, timely, subject oriented information at the required level of detail BIB004 .It produces concise analysis to assist decision maker and increases organization corporeal performance. DW is a collection of decision support technologies, aimed at enabling knowledge worker to make better and faster decision  . It is the heart of Business Intelligence (BI) System and contains all the information integrated from heterogeneous source into multidimensional schema to enhance data access for analysis and decision making. Data from various sources go through tremendous Extract, Transform and Loading (ETL) process that clean, transform and modify data so that could match multidimensional schema in DW. High volume of data from multiple sources causes high probabilities of errors and anomalies. Therefore, data needs to be transformed, scrubbed and audited before they are loaded into DWs. There are unique criteria in DW that differentiate it with database management system (DBMS). DW is a subject oriented, integrated, time varying and non-volatile collection of data BIB001 . It contains historical data and those stored data will not be either deleted or changed. Analyzing historical, summarized and consolidated data could lead to precious information such as trend and behavioral correlation. The uniqueness of DWs features cause differences in DW modeling and design. For example in DBMS, redundancy should be eliminated to enhance business process while in DW the existence of redundancy is required to enhance DW performance. However, keeping all historical data that had rarely changed is costly since it requires big data space. To encounter this problem, BIB006 proposed slowly changing dimension method to increase its efficiency BIB006 . The unique features of DW had exhorted different DW methodologies and design. In DW lifecycle, one of most critical phase that might influence all other phases in DW development is the Requirement Analysis phase. It is a process of gathering requirement from end user. Since DW end users might vary, it is important to identify accurate end users that will represent requirements in different point of views. The predominant objective of this phase is to identify organization goals and elaborate requirements that could measure organization performance. However, DW projects are inherently risky and some of them failed in the implementation. Studies shows that more that 80% of DW project fail to fulfill business goals  , BIB003 . There are few reasons that contribute to its failure; some of them are related to user requirement analysis. BIB006 stated that requirement definition phase is paramount and impact almost every decision in DW project BIB006 . Most of failed DW projects execute poor requirement definition phase and some of them skip this phase  . They tend to focus on technical issues and not paid enough attention to requirement analysis  . Groggy method of conducting requirement definition analysis will lead to unstable design process  and poor communication between IT people and decision maker BIB005  . With lack of higher-level requirements vision  , it is difficult to ensure the project will meet business goal. Furthermore, incomplete or inconsistence requirement lead developer to incorrect assumption BIB007 . In other hand, with tremendous amount of users requirements from different levels and categories cause big challenge to specify important requirement that could lead to business goal  . DW itself can be discussed in many aspects, however in this paper we will concentrate on user requirement approach in data warehouse design. This paper had been organized as follows. Section II presents the user requirement analysis approach. Section III discusses the advantage and disadvantage of available approach. It also discusses comparison of proposed mixed approach towards organization environment, technical team skills and analysis outcome. Finally, Section IV present conclusion and future work on user requirement analysis approach for data warehouse.