Database And Data Warehousing Technologies

Data Warehouse is a type of data organization on which decision-making systems are based. The data storage in a data warehouse system typically needs to fully cover one or more business areas (e.g., procurement, sales), and the data in the data warehouse needs to be comprehensive, meaning that it should be fully integrated with the internal data organization. These data records must include a longer period of time (five, ten or more years) because this longer time periodical analysis is very significant for every business. Smaller data warehouses that include data from only one business area are called sectorial warehouses, or data marts.

The data warehouse is designed for managers, but also for everyone performing various analytical tasks, such as monitoring and reporting operations based on different business rules and carried out by placing directed queries and analysis of the results obtained; analysis and diagnosis jobs based on skills, performing an iterative analysis and discovery of the information needed; and planning and simulation operations based on knowledge, performing modeling and execution of a drafted model. The usual system design splits the database and the data warehouse, where the database is concerned with transactions and queries, while the warehouse enables analytical reports and a decision support system. Since the data warehouse is oriented towards business analysis, its storage does not need to be promptly updated as is the case with the data in the associated relational database. Today, with hardware advancements and the advanced design of database management systems, the database and the data warehouse can be stored on the same data server (in the same data center). Such database management systems can separately handle the transactions and the analytical reports. The main users of data warehouse systems are big businesses (banks, telecoms, big retailers, e-commerce portals) and their potential use cases include consumers’ behavior prediction, product recommendations, fraud detection, future trends envisioning etc. Data warehouses are also used in medical/genomic research studies, since biomedical questions are usually complex, and numerous valuables and heterogeneous data are increasingly available to answer these questions. Data warehouses have also been applied to public institutions that offer e-government services, processing spatial data mostly collected from Geographical Information Systems (GIS). This edition covers topics from data warehouse technology aspects, as well as applications of data warehouses in the business sector, bio-medical domain and the public sector. Section 1 focuses on data warehouse technology, describing data warehouse design using process-oriented requirement analysis, data registration of domain names in the data warehouse construction task, comparative study of data warehouse design approaches, base analysis techniques in data qualities framework for data warehouses, and critical factors influencing the adoptions of a data warehouse. Section 2 focuses on data warehouse applications in the business sector, describing essential elements to support decision-making process in industries, data warehouse design for the detection of fraud in the supply chain, data load manifestation in process chains in SAP business warehouse, and big data usage in the marketing information system. Section 3 focuses on data warehouse applications in the bio-medical domain, describing building a health care data warehouse for cancer diseases, building an osteoporosis data warehouse in the osteoporosis community, complex queries on an integrative genomic and proteomic data warehouse, Truncatulix – a data warehouse for the legume community, and approaches to access biological data sources. Section 4 focuses on data warehouse applications in the public sector, describing data warehouse for leishmaniosis in Marrakech City, data warehouse technology and application in data center design for e-government, data warehouse use of geographic information system for ornamentals, and gossip management at universities using the big data warehouse model.

Jovan Pehcevski obtained his PhD in Computer Science from RMIT University in Melbourne, Australia in 2007. His research interests include big data, business intelligence and predictive analytics, data and information science, information retrieval, XML, web services and service-oriented architectures, and relational and NoSQL database systems. He has published over 30 journal and conference papers and he also serves as a journal and conference reviewer. He is currently working as a Dean and Associate Professor at European University in Skopje, Macedonia.