Showing papers on "Data mart published in 1998"

Application of data mining tools to hotel data mart on the Intranet for database marketing

Abstract: Data mining, which is also referred to as knowledge discovery in databases, is the process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions. In this paper, we present the data mining process from data extraction to knowledge interpretation and data mining tasks, and corresponding algorithms. Before applying data mining techniques to a real-world application, we build a data mart on the enterprise Intranet. RFM (recency, frequency, and monetary) data extracted from the data mart are used extensively for our analysis. We then propose a new marketing strategy that fully utilizes the knowledge resulting from data mining.

Present and future directions in data warehousing

Abstract: Many large organizations have developed data warehouses to support decision making. The data in a warehouse are subject oriented, integrated, time variant, and nonvolatile. A data warehouse contains five types of data: current detail data, older detail data, lightly summarized data, highly summarized data, and metadata. The architecture of a data warehouse includes a backend process (the extraction of data from source systems), the warehouse, and the front-end use (the accessing of data from the warehouse). A data mart is a smaller version of a data warehouse that supports the narrower set of requirements of a single business unit. Data marts should be developed in an integrated manner in order to avoid repeating the "silos of information" problem.An operational data store is a database for transaction processing systems that uses the data warehouse approach to provide clean data. Data warehousing is constantly changing, with the associated opportunities for practice and research, such as the potential for knowledge management using the warehouse.

90 days to the data mart

Abstract: From concept to deployment in just 13 weeks! A proven approach to building a fully functional data mart quickly, efficiently, inexpensively. From one of the leading data warehousing experts, here is a proven approach to building a departmental data mart in 13 weeks. Rather than provide a cookbook describing how to build a particular type of data mart using a specific set of tools, bestselling author Alan Simon provides you with a detailed blueprint that can be used to construct virtually any type of data mart. Following a brief overview of key concepts and terms, Simon launches into a week-by-week action plan, beginning with the initial planning and project definition stages in Weeks 1 and 2, and ending with acceptance testing, fine-tuning, and deployment in Week 13. The approach outlined works equally well for both ROLAP and MOLAP data marts, regardless of the products or technologies you use to build your mart. Whether your goal is to provide basic querying reporting, OLAP, EIS, data mining, or any combination of these, you'll find everything you need to get the job done right, in record time, and within budget, including: A proven approach to building a data mart in 90 days Step-by-step guidelines to all organizational, procedural, and technical steps involved Numerous checklists that help you make sure all the bases are covered Expert advice on how to make the most of particular technologies and products. VISIT OUR WEBSITE AT www wiley.com/compbooks/

Scaling an expert system data mart: more facilities in real-time.

Abstract: Clinical Data Repositories are being rapidly adopted by large healthcare organizations as a method of centralizing and unifying clinical data currently stored in diverse and isolated information systems. Once stored in a clinical data repository, healthcare organizations seek to use this centralized data to store, analyze, interpret, and influence clinical care, quality and outcomes. A recent trend in the repository field has been the adoption of data marts--specialized subsets of enterprise-wide data taken from a larger repository designed specifically to answer highly focused questions. A data mart exploits the data stored in the repository, but can use unique structures or summary statistics generated specifically for an area of study. Thus, data marts benefit from the existence of a repository, are less general than a repository, but provide more effective and efficient support for an enterprise-wide data analysis task. In previous work, we described the use of batch processing for populating data marts directly from legacy systems. In this paper, we describe an architecture that uses both primary data sources and an evolving enterprise-wide clinical data repository to create real-time data sources for a clinical data mart to support highly specialized clinical expert systems.

On Business Intelligence Systems

Abstract: Business enterprises today face unprecedented competitive pressures, as the pace of advances in computer and information technologies has become maddeningly fast and the standard of living and life styles of consumers have undergone significant changes. Enterprises can gain competitive edges if they can set their business strategies for winning new customers, retaining existing customers, and reducing the cost of doing business better. One promising way is for enterprises to base these strategies on business intelligence that can be analyzed and deduced from the vast amounts of data at their disposal. Today, advances in information technology have made available such technologies as data mining, online analytical processing, and data warehousing. It is now possible to construct business intelligence systems using these technologies. In this paper, we will describe what business intelligence systems are, examine the enabling technologies, and outline several key issues that need to be given serious considerations in successfully building and using business intelligence systems.

Data modeling for improving performance of data mart

Abstract: Data warehouse is a technology of potentially enormous worth to an enterprise. It is defined as a subject oriented, integrated, time-variant, and nonvolatile collection of data in support of the management decision-making process. Data mart is basically another form of a data warehouse, but it is a decentralized subset of data found in the data warehouse. Although there are many pros and cons about the usefulness of the data mart, it must be a powerful, rapidly constructed, tool for delivering business information. Instead of using traditional data model in order to build a data mart on the enterprise Intranet, we present another way of modeling the data mart, which allows only one record for each customer to improve processing performance although many transactions for a customer might be happening over time. To do so, we have to do some operations, e.g., number crunching on the fields in each record. We then develop a real world application doing data mining tasks using the data mart.

The study, design, and implementation of Data mart functions in Windows environments

Abstract: Like other businesses in industry, there is a;need to have a software system to give managers and other deGision­ making executives easy access to up-to-date operating information. This information includes inventory; bookkeeping, and product maintenance activities between central/headguarters and branch stores. The traditional way to prpvide information to those who make critical decisions is to build a centralized data warehouse utilizing various legacy systems. This project intends to explore the feasibility of using a data mart^ structure to expedite the decision-making process and to implement key functions of such a design. This would provide users with critical, timely, and accessible decision-making information. The task of this ; project is to build the communication channel within the enterprise management territory and to provide crucial marketing business intelligence in a timely fashion. The purpose of this project is to develop a Decision Making Database System(Dl^S) that utilizes Object Linking and Embedding(OLE) technologies to simulate how to integrate existing data from many different formats and Ipcatibns into data mart can be regarded as a special kind Of data warehouse in which a summarized, highly focused pprtion of