Showing papers on "Data mart published in 2003"

Managing virtual data marts with metapointer tables

Abstract: Although Internet/intranet topology has radically evolved communications for information workers, the access and transfer of large volumes of data through this topology is still inefficient and is not inherently conducive to point-in-time recall. Because information managers make real time decisions based on relevant subsets of an enterprise data warehouse, data marts are required for optimized data accessibility and use. Storage virtualization, based on pointer systems, has introduced new instant copy functions which do not require that data be actually stored outside the data warehouse even though it is referenced in a variety of contexts (e.g., multiple data marts utilizing overlapping subsets of the data warehouse). The purpose of this paper is to describe a method enabling users to access relevant subsets of large data populations residing in different data warehouses. This method enables the creation of data marts for use by local decision support software without requiring the data mart data population to be stored locally. This will save storage space and data transmission resources, and simplify synchronization of the data mart and the warehouse when updates occur.

Relational Modeling and Data Warehouse Design

Abstract: From the Publisher: At last, a balanced approach to data warehousing that leverages the techniques pioneered by Ralph Kimball and Bill Inmon Since its groundbreaking inception, the approach to understanding data warehousing has been split into two mindsets: Ralph Kimball, who pioneered the use of dimensional modeling techniques for building the data warehouse, and Bill Inmon, who introduced the Corporate Information Factory and leads those who believe in using relational modeling techniques for the data warehouse. Mastering Data Warehouse Design successfully merges Inmon’s data ware- house design philosophies with Kimball’s data mart design philosophies to provide you with a compelling and complete overview of exactly what is involved in designing and building a sustainable and extensible data warehouse. Most data warehouse managers, designers, and developers are familiar with the open letter written by Ralph Kimball in 2001 to the data warehouse community in which he challenged those in the Inmon camp to answer some tough questions about the effectiveness of the relational approach. Cowritten by one of the best-known experts of the Inmon approach, Claudia Imhoff, this team of authors addresses head-on the challenging questions raised by Kimball in his letter and offers a how-to guide on the appropriate use of both relational and dimensional modeling in a comprehensive business intelligence environment. In addition, you’ll learn the authors’ take on issues such as: Which approach has been found most successful in data warehouse environments at companies spanning virtually all major industrial sectors The pros and cons of relational vs. dimensional modeling techniques so developers can decide on the best approach for their projects Why the architecture should include a data warehouse built on relational data modeling concepts The construction and utilization of keys, the historical nature of the data warehouse, hierarchies, and transactional data Technical issues needed to ensure that the data warehouse design meets appropriate performance expectations Relational modeling techniques for ensuring optimum data warehouse performance and handling changes to data over time * A cutting-edge response to Ralph Kimball's challenge to the data warehouse community that answers some tough questions about the effectiveness of the relational approach to data warehousing * Written by one of the best-known exponents of the Bill Inmon approach to data warehousing * Addresses head-on the tough issues raised by Kimball and explains how to choose the best modeling technique for solving common data warehouse design problems * Weighs the pros and cons of relational vs. dimensional modeling techniques * Focuses on tough modeling problems, including creating and maintaining keys and modeling calendars, hierarchies, transactions, and data quality Author Biography: CLAUDIA IMHOFF (CImhoff@Intelsols.com) is President and Founder of Intelligent Solutions, a leading consultancy on analytic CRM and BI technologies and strategies. She is a popular speaker, an internationally recognized expert, and coauthor of five books. NICHOLAS GALEMMO (ngalemmo@yahoo.com) was Information Architect at Nestle USA. He has twenty-seven years’ experience as a practitioner and consultant involved in all aspects of application systems design and development. He is currently an independent consultant. JONATHAN G. GEIGER (JGeiger@IntelSols.com) is Executive Vice President at Intelligent Solutions, Inc. In his thirty years as a practitioner and consultant, he has managed or performed work in virtually every aspect of information management.

The customer relationship management system using settlement history

Abstract: PURPOSE: A CRM(Customer Relationship Management) system using settlement particulars is provided to improve earnings largely by making a provider to operate and manage the proper contents, and plan an optimum marketing policy through an individual marketing function according to a customer analysis result. CONSTITUTION: A data warehouse(700) stores the settlement related data stored in a database of a direct sales shop(600) and a service provider(300), the customer information, and the goods information. A data mart(800) performs the analysis by using the information stored in the data warehouse(700). A basic statistics analyzer(910) analyzes the basic statistics needed for customer management. A database integrating/constructing tool(920) constructs the data warehouse(700) by integrating the settlement information, the customer information, and the goods information, and constructs and controls the data mart(800). A contents management part(930) manages the contents of the provider(200). A customer analysis/management tool(950) analyzes and predicts the sales change by using the data generated from the data mart(800), and analyzes and manages a customer group.

The Effect of Organizational Attributes on the Adoption of Data Mining Techniques in the Financial Service Industry: An Empirical Study in Taiwan

Abstract: In this paper, we open up the organizational attributes that significantly influence the adoption of data mining (DM) technique for financial service industry. The technique of factor analysis was employed to explore the features and multivariate data analysis technique t-test to investigate the hypotheses. Based on the data collected from medium-to large-sized firms, the empirical results confirmed that the organizational size, attitude of data resource, and style of decision-making significantly influence the DM adoption. In addition, it was found that the DM adoption did not significantly affected by the types of both marketing orientation and information orientation in terms of organizational culture. Research implications were also discussed in this research. 1. Introduction Information Technology (IT) has been extensively used in a multitude of applications within various industries, in particular the enhancement of organizational intelligence and decision-making. Many studies have addressed that the organizational features play a fairly important role in the adoption of IT [Thong et al., 1995; Fletcher et al., 1996; Fink, 1998, Chengalur-Smith et al., 1999, Cabrera et al., 2001; Dewett et al., 2001]. These features mainly include size, culture, competition, specialization, functional differentiation, and external integration. While a variety of studies looking at the relation of organizational features and IT adoption have presented that a noteworthy one showed significantly in some specific conditions, but not in all cases, a particular technique of Data Mining (DM) is hardly ever revealed, and thus becomes the motivation of this research. DM with a descriptive and predictive ability can elicit patterns that are not predictive, but meaningful and decision-supportable in historical data [Fayyad et al., 1996, 1997; Chen et al., 1996]. Basically, the DM mainly consists of five major phases: data collection, data cleaning, data mining, knowledge formulization and knowledge application. The data collection deals primarily with gathering the concerned data such as bank transactions, retailer transactions, Web shopping transactions, etc. The data cleaning is concerned with the consistency of multi-typed datasets, elimination of redundant attributes, refinement and reconstruction of collected datasets, and discretisation of continuous contexts. The DM returns the outputs that entail association, classification, regression, clustering, or summarization. The knowledge reorganization is conducted in the phase of formulization while practical use in the application. Data mining is one of the important techniques of IT and has been employing in support of management decisions via the discovery of patterns in large databases [Bigus et al., 1996; Chen et al., 1996; Fayyad et al., 1996, 1997; Han et al., 1998; Han et al., 1999]. Pitta (1998) highlights the DM as an important tool that marketers can rely on to reveal patterns in databases while emphasizing the marketing one-to-one strategy. More importantly, the applications in various areas of business depicted in literature in the past few years have also witnessed the increased use of DM. Referable works can be viewed in hotel data mart [Sung et al., 1998], personal bankruptcy prediction [Donato et al., 1999], customer service support [Hui et al., 2000], and the special issue edited by Kohavi et al., [2001] of an underlying journal. Bigus (1996) and Adriaans et al. (1996) also provides a fundamental concept for the applicability of DM in business problems covering marketing segmentation, customer ranking, real estate pricing, sales forecasting, customer profiling, and prediction of bid behavior of pilots. It is believed that many industries have been adopting DM as an important management tool to help management decisions. However, it may be more relevant for the DM adoption if an industry can produce tremendous transaction data through organizational activities. …

The Corporate Information Factory

Abstract: Early computer applications spawned structured applications. Structured applications led to online applications. Online applications processed business transactions and became essential to the day to day running of the corporation. As these transaction processing applications aged, they became legacy applications. Corporations thought they had a foundation for information with their transaction processing applications, but they were wrong. The many transaction processing applications were unintegrated, did not store historical data to any great extent, and stored data in technology difficult to access. The result was a great thirst for information. The thirst for information from legacy applications fostered the data warehouse environment. The data warehouse environment focussed on granular, integrated, historical data that was easy to access and was able to be reshaped in as many ways as the corporation needed to see the data. From the foundation of the data warehouse evolved the corporate information factory, where there are many different components each of which serves a different community and purpose.

A Data Mart Approach for Monitoring Web Services Usage and Evaluating Quality of Services.

Abstract: Web servers log data constitute an important resource to track users’ activity within a Web site, especially when organized and integrated with other sources of data on the so called clickstream data marts. These data marts support flexible and complex analyses, which are the basis to obtain great understanding of customer behavior and achieve efficient operation of electronic business initiatives. With the proliferation of Web services as a new trend to enterprise application integration and interoperability, it seems natural to use a similar approach to monitor services usage and to evaluate quality of services. This paper presents a data mart proposition generated from Web services log data that can reveal usage patterns on Web services giving a highly improved understanding of behavior and service provider performance.

A web-based ERP data mining system for decision making

Abstract: Most of the enterprises having finished, one after the other, in the automation of transaction processing, the key issue for the future competitiveness of enterprises will be the automation of decision-making processing and forecast processing. To complement the deficiency of the forecast flow in enterprise resource planning (ERP) systems, we propose here a data searching architecture that can be built on the ERP system while meeting the individual needs of enterprises and providing real time accurate forecasts on changes in the future market. The present paper is based on the transaction flow processing power of ERP. It integrates the database and online analytical processing (OLAP) technologies, transaction and decision making flows of ERP, then uses data searching technology to integrate decision making and forecast flows. The data-searching engine is the actual design of a categorising module. This research can finish within an hour the analyses and forecasts, which would take the traditional information technology system about three weeks to process the mock analyses. This makes decision making easier for entrepreneurs and thus enables them to handle well the always-changing market and establish themselves firmly. This research applies the ERP data searching system on the IC testing industry. Using the ERP data searching system in this study to investigate the testing status of the testing machine, resolutions are found pinpointing the factors that cause the high frequency of machine down. The results of, according to the actual experiment, the tested testing machine shows an average improvement rate of 83% over the number of machines down.

Shifting From Legacy Systems to a Data Mart and Computer Assisted Information Resources Navigation Framework.

Abstract: Computer Assisted Information Resources Navigation (CAIRN) was specified, in the past, as a framework that allows the end-users to import and store full text and multimedia documents and then retrieve information using Natural Language or field based queries. Our CAIRN system is a general tool that has focused on medical information covering the needs of physicians. Today, concepts related to Data Mining and Data Marts have to be incorporated into such a framework. In this paper a CAIRN-DAMM (Computer Assisted Medical Information Resources Navigation & Diagnosis Aid Based On Data Marts & Data Mining) environment is proposed and discussed. This integrated environment offers: document management, multimedia documents retrieval, a Diagnosis–aid subsystem and a Data Mart subsystem that permits the integration of legacy system’s data. The diagnosis is based on the International Classification of Diseases and Diagnoses, 9th revision (ICD-9). The document collection stored in the CAIRN-DAMM system consists of data imported from the Hospital Information System (HIS), laboratory tests extracted from the Laboratory Information System (LIS), patient discharge letters, ultrasound, CT and MRI images, statistical information, bibliography, etc. There are also methods permitting us to propose, evaluate and organize in a systematic way uncontrolled terms and to propose relationships between these terms and ICD9 codes. Finally, our experience from the use of the tool for creating a Data Mart at the ARETEION University Hospital is presented. Experimental results and a number of interesting observations are also

Data mining for human resource information systems

Abstract: This chapter focuses on the potential contributions that Data Mining (DM) could make within the Human Resource (HR) function in organizations. We first provide a basic introduction to DM techniques and processes and a survey of the literature on the steps involved in successfully mining this information. We also discuss the importance of data warehousing and datamart considerations. An examination of the contrast between DM and more routine statistical studies is given, and the value of HR information to support a firm's competitive position and organizational decision-making is considered. Examples of potential applications are outlined in terms of data that is ordinarily captured in HR information systems.

Metrics for data warehouse quality

Abstract: This chapter proposes a set of metrics to assess data warehouse quality. A set of data warehouse metrics is presented, and the formal and empirical validations that have been done with them. As we consider that information is the main organizational asset, one of our primary duties should be assuring its quality. Although some interesting guidelines have been proposed for designing "good" data models for data warehouses, more objective indicators are needed. Metrics are a useful objective mechanism for improving the quality of software products and also for determining the best ways to help professionals and researchers. In this way, our goal is to elaborate a set of metrics for measuring data warehouse quality which can help designers in choosing the best option among more than one alternative design.

Data mining for the health system pharmacist

Abstract: Data mining is an application of computer technology that allows pharmacists to analyze the large amounts of clinical and administrative data related to patient care that health care organizations accumulate Data mining is the process of taking the raw material (data) from the data warehouse and converting it to information that can be used by decision makers Online analytical processing (OLAP) is the tool that accomplishes the data mining process This article introduces the concept of data mining and related technologies, including the data warehouse, data mart, and OLAP - concepts that are closely related to one another The benefits of applying data mining to pharmacy practice, with a focus on the point of care, are discussed Successful data mining often requires an enterprise-wide information system as a prerequisite Data can then be collected electronically at all points in the health care supply chain A strategic view of the information flow in an acute care setting is provided To further illustrate the application of data mining, several examples of OLAP integration are given In addition, some unique data sources and a pharmacy specific reporting technology are described

The Emerging Knowledge Management Paradigm: Some Organizational and Technical Issues

Abstract: This paper addresses the expectations, organizational implications, and information processing requirements, of the emerging knowledge management paradigm. A brief discussion of the enablement of the individual through the wide-spread availability of computer and communication facilities, is followed by a description of the structural evolution of organizations, and the architecture of a computer-based knowledge management system. The author discusses two trends that are driven by the treatment of information and knowledge as a commodity: increased concern for the management and exploitation of knowledge within organizations; and, the creation of an organizational environment that facilitates the acquisition, sharing and application of knowledge. Tracing the evolution of the structure of organizations, the author concludes that the web-like features of the Network Model are most conducive to the promotion of knowledge management principles, even though this model does have liabilities that require careful monitoring. The paper further discusses in some detail the architecture of a knowledge management system that consists of a lower integrated data layer and an upper information layer. Attention is drawn to the need of the data layer to include not only archived summary data as found in Data Warehouses and Data Marts, but also near real-time operational data with convenient access provided by Data Portals. An important distinction is drawn between data-centric and information-centric software environments in terms of software with an internal information model capable of supporting agents with automatic reasoning capabilities. The paper concludes with a brief description of the mechanisms through which a Web-Services environment provides access to distributed data sources, as well as heterogeneous data-centric and information-centric software applications.

Data Warehouse Technology and Its Application

Abstract: In this paper,the fundamental concept,systematic architecture and direction of development are demonstrated emphatically.Data source and its integration,central data warehouse,data mart,OLAP server and analyzer of the data warehouse are analysed in detail together with a example project.

Data Mart Development and Dynamic Model Instantiation

Abstract: OLAP can be used for two distinct investigations in decision support applications: 1. Data Exploration and Analysis, and 2. Model Instantiation and Investigation.