Application of Data Warehouse in Real Life: State-of-the-art Survey from User Preferences’ Perspective

Abstract

Abstract-In recent years, due to increase in data complexity and manageability issues, data warehousing has attracted a great deal of interest in real life applications especially in business, finance, healthcare and industries. As the importance of retrieving the information from knowledge-base cannot be denied, data warehousing is all about making the information available for decision making. Data warehouse is accepted as the heart of the latest decision support systems. Due to the eagerness of data warehouse in real life, the need for the design and implementation of data warehouse in different applications is becoming crucial. Information from operational data sources are integrated by data warehousing into a central repository to start the process of analysis and mining of integrated information and primarily used in strategic decision making by means of online analytical processing techniques (OLAP). Despite the applications of data warehousing techniques in number of areas, there is no comprehensive literature review for it. This survey paper is an effort to present the applications of data warehouse in real life. It focuses to help the scholars knowing the analysis of data warehouse applications in number of domains. This survey provides applications, case studies and analysis of data warehouse used in various domains based on user preferences.

Business Intelligence and Data Warehouse in Agrarian Sector: A Bibliometric Study

“…The need for technologies to optimize decision making processes in organizations is increasing, as large volumes of data are generated daily and the requirement for useful strategic information is growing (Shahid et al, 2016). In a similar context of other kind of organizations, rural producers, farmers and professionals in the agrarian sector also need to subsidize their decisions, to optimize productivity or reduce the risks and uncertainties inherent in their activities.…”

“…The DW, together with ETL (Extraction, Transformation and Load) process, help in organizing, integrating and cleaning the data, as well as facilitating the use of data visualization tools and knowledge extraction, such as data mining tools, online analytical processing (OLAP), reporting, ad-hoc queries, among others. DW is widely used in different sectors, such as: government, business, financial, health, industry, education, agribusiness, among others Shahid et al, 2016).…”