Data mart

About: Data mart is a research topic. Over the lifetime, 559 publications have been published within this topic receiving 8550 citations.

Method and equipment for realizing clinical information sharing

Abstract: The application aims to provide a method and equipment for realizing clinical information sharing. Compared with the prior art, the method provided by the application comprises the following steps: acquiring basic clinical data of a plurality of data systems of a hospital, then establishing a corresponding clinical data warehouse according to the basic clinical data, and establishing a corresponding clinical data mart with different dimensionalities based on the clinical data warehouse. According to the method, integration and sorting of various data in the plurality of data systems of the hospital can be realized, the requirements of clinic, management, scientific research and the like on analysis and utilization of the data in the data systems of the hospital are met, and the overall competitive advantages of the hospital are improved.

Establishment of Water Environmental Data Mart

Abstract: The concepts of data warehouse, data mart, on-line analytical processing (OLAP) are introduced into the domain of environmental decision support system. A water environmental data mart (WEDM) is developed with a city's water environmental operation database as data source, and with star schema as data framework. Two main parts are included in WEDM, the extract-cleanse-transform-load tool, and the universal OLAP tool whose functions include SQL, classifying statistic, and data visualization. The WEDM can provide multidimensional, multi-leveling, integrated, dynamic, flexible querying and analyzing, which is not offered by previous environmental operation database system. Three examples show the functions of proposing decision support information of WEDM.

Dealing with slow-evolving fact: a case study on inventory data warehousing

Abstract: Data Warehousing for INventory management (DWIN) is a production project at Telcordia aimed at providing telecommunications service providers with decision support functions for inventory control and monitoring. In this paper, we report some interesting issues related to the design of the data warehouse. Specifically, we will discuss the issues of slow-evolving fact, transaction-oriented fact table, and large dimensions. We also propose the concept of virtual data cubes and show its usefulness. We address these issues through a data mart case study and present benchmarking results. Finally, based on the experiences learned, we discuss potential research issues that may benefit the data warehousing and OLAP practice.

Design and Implementation of an Enterprise Data Warehouse

Abstract: DESIGN AND IMPLEMTATION OF AN ENTERPRISE DATA WAREHOUSE Edward M. Leonard, B.S. Marquette University, 2011 The reporting and sharing of information has been synonymous with databases as long as there have been systems to host them. Now more than ever, users expect the sharing of information in an immediate, efficient, and secure manner. However, due to the sheer number of databases within the enterprise, getting the data in an effective fashion requires a coordinated effort between the existing systems. There is a very real need today to have a single location for the storage and sharing of data that users can easily utilize to make improved business decisions, rather than trying to traverse the multiple databases that exist today and can do so by using an enterprise data warehouse. The Thesis involves a description of data warehousing techniques, design, expectations, and challenges regarding data cleansing and transforming existing data, as well as other challenges associated with extracting from transactional databases. The Thesis also includes a technical piece discussing database requirements and technologies used to create and refresh the data warehouse. The Thesis discusses how data from databases and other data warehouses could integrate. In addition, there is discussion of specific data marts within the warehouse to satisfy a specific need. Finally, there are explanations for how users will consume the data in the enterprise data warehouse, such as through reporting and other business intelligence. This discussion also includes the topics of system architecture of how data from databases and other data warehouses from different departments could integrate. An Enterprise Data Warehouse prototype developed will show how a pair of different databases undergoes the Extract, Transform and Load (ETL) process and loaded into an actual set of star schemas then makes the reporting easier. Separately, an important piece of this thesis takes an actual example of data and compares the performance between them by running the same queries against separate databases, one transactional and one data warehouse. As the queries expand in difficulty, larger grows the gap between the actual recorded times of running that same query in the different environments.