Building a Data Warehouse step by step

Data warehouses have been developed to answer the increasing demands of quality information required by the top managers and economic analysts of organizations. Their importance in now a day business area is unanimous recognized, being the foundation for developing business intelligence systems. Data warehouses offer support for decision-making process, allowing complex analyses which cannot be properly achieved from operational systems. This paper presents the ways in which a data warehouse may be developed and the stages of building it.data warehouse, data mart, data integration, database management system, OLAP, data mining

Improving the Data Warehouse Architecture Using Design Patterns

Data warehousing is an important part of the enterprise information system. Business intelligence (BI) relies on data warehouses to improve business performance. Data quality plays a key role in BI. Source data is extracted, transformed, and loaded (ETL) into the data warehouses periodically. The ETL operations have the most crucial impact on the data quality of the data warehouse. ETL-related data warehouse architectures including structure-oriented layer architectures and enterprise-view data mart architecture were studied in the literature. Existing architectures have the layer and data mart components but do not make use of design patterns; thus, those approaches are inefficient and pose potential problems. This paper relays how to use design patterns to improve data warehouse architectures

The Impact of Data Quality Tagging on Decision Outcomes

Data quality tags provide information about the quality of data in databases to decision makers. This paper reports an experiment that examines the impact of data quality tagging about data accuracy on decision outcomes. Two decision strategies were explored: additive and elimination by attributes. The inclusion of data quality tagging information was found to impact decision outcomes for the elimination by attributes strategy but not for the additive strategy and had no impact on group consensus. This knowledge will be valuable for designers of data warehouses and decision support systems

Using Distributed Meta-information Systems to Maintain Web Data Quality

Maintaining the quality of data resources has been a continuing concern for information systems professionals. Over time techniques have been developed for maintaining the appropriate level of quality for individual databases, for data warehouses and for transaction processing systems. However, web-based systems lack the tools and procedures for data quality to be properly maintained. My dissertation seeks to develop methods that can be used to improve web-based data quality. It maps data quality dimensions, as identified in prior research [Wand et. al., 1996, and Wang et. al., 1995], to the web domain and then proposes methods that can help maintain each of these data quality dimensions

Data Cleaning: Problems and Current Approaches

We classify data quality problems that are addressed by data cleaning and provide an overview of the main solution approaches. Data cleaning is especially required when integrating heterogeneous data sources and should be addressed together with schema-related data transformations. In data warehouses, data cleaning is a major part of the so-called ETL process. We also discuss current tool support for data cleaning

Using Artificial Intelligence and Big Data-Based Documents to Optimize Medical Coding

Clinical information systems (CISs) in some hospitals streamline the data management from data warehouses. These warehouses contain heterogeneous information from all medical specialties that offer patient care services. It is increasingly difficult to manage large volumes of data in a specific clinical context such as quality coding of medical services. The document-based not only SQL (NoSQL) model can provide an accessible, extensive, and robust coding data management framework while maintaining certain flexibility. This paper focuses on the design and implementation of a big data-coding warehouse, and it also defines the rules to convert a conceptual model of coding into a document-oriented logical model. Using that model, we implemented and analyzed a big data-coding warehouse via the MongoDB database and evaluated it using data research mono- and multi-criteria and then calculated the precision of our model