Effective decision support systems (DSS) are quickly becoming key to businesses gaining a competitive advantage, and the effectiveness of these systems depends on the ability to construct, maintain, and extract information from data warehouses. While many still perceive data warehousing as a subdiscipline of management information systems...
The process of data mining relies on the effective implementation of data collection, warehousing and processing. Data mining can be used to describe a target data set, predict outcomes, detect fraud or security issues, learn more about a user base, or detect bottlenecks and dependencies. It can...
aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and e...
Data profiling is also be known asdata archeology,data assessment,data discoveryordata quality analysis. Organizations use data profiling at the beginning of a project to determine if enough data has been gathered, if any data can be reused or if the project is worth pursuing. The process of ...
At the crossing of disciplines as Information Systems, Management, Decision Support Systems, Data Mining, and Data Visualization, Business Intelligence (BI) is understood in very different ways by the multiple concerned actors. This chapter aims to offer to all of them an integrated view on multipl...
(OLAP) or data mining tool. That’s where business users hope to find meaningful patterns that nudge them toward good decisions. But a lot goes on behind the scenes to get clean, reliable data to the user. Unless that part of the process is done correctly, valid business decisions cannot...
Concurrency & Computation Practice & ExperienceJiang, F.; Leung, C.K. Mining interesting "following" patterns from social networks. In Proceedings of the International Conference on Data Warehousing and Knowledge Discovery, Munich, Germany, 2-4 September 2014; pp. 308-319....
natural language processing data warehousing clinical data warehouse artificial intelligence AI Introduction Background For >20 years, health data from patient care have been systematically archived in the form of electronic health records (EHRs) [1,2]. Databases have been created to gather both struct...
such as analysis of computer-based patient records,data warehousing tools,intelligent alarming,effective and efficient monitoring,and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis ...
This shows the Kimball data warehousing methodology, which is built around star schemas and data marts. There is also a hybrid approach to data warehouse design that includes aspects of both the top-down and bottom-up methods. Organizations that adopt a hybrid strategy often seek to combine the...