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Showing papers on "Data mart published in 2001"


Patent
26 Sep 2001
TL;DR: In this paper, post-processed payment transaction records of consumer and business payers are received into a multi-dimensional networked data mart from databases originating from a multitude of financial institutions and payment processors.
Abstract: Processed payment transaction records of consumer and business payers are received into a multi-dimensional networked data mart from databases originating from a multitude of financial institutions and payment processors. A post-processor linked to the data mart assigns all such transaction records with universal consumer and business expenditure categories used for payer financial management. Post-processed payment transaction records are indexed in the data mart by time, geography, and the universal consumer and business expenditure categories. Mathematical and analytical tools are applied to aggregated payment transaction records according to geographic, topographical, meteorological, chronological, demographic and other parameters. Endusers interact electronically with the data mart to view, create, synthesize and receive post-processed payment data for economic, investment, business, and marketing analysis.

273 citations


Patent
16 Jul 2001
TL;DR: In this paper, a method for logical view visualization of user behavior in a networked computer environment that includes sites that a user may visit and wherein the sites comprise pages that the user may view and/or resources that the users may request includes the step of collecting raw data representing user behavior which can include requesting resources, viewing pages and visiting sites by the user.
Abstract: A method for logical view visualization of user behavior in a networked computer environment that includes sites that a user may visit and wherein the sites comprise pages that the user may view and/or resources that the user may request includes the step of collecting raw data representing user behavior which can include requesting resources, viewing pages and visiting sites by the user. This raw data is then refined or pre-processed into page views and visit data and stored in a data mart. Pages are clustered into super pages, and page to super page mappings reflecting the relationship between pages and super pages are stored in the data mart. An automated clustering means is applied to the page view, visit and super page data in the data mart to discover clusters of visits to define super visits having visit behavior characteristics. The visit data stored in the data mart is then scored against the super visit clusters to classify visits into super visits according to visit behavior characteristics. A system is also provided.

188 citations


Patent
27 Sep 2001
TL;DR: In this paper, a data migration, data integration, data warehousing, and business intelligence system including a data storage model is provided that allows a business to effectively utilize its data to make business decisions.
Abstract: A data migration, data integration, data warehousing, and business intelligence system including a data storage model is provided that allows a business to effectively utilize its data to make business decisions. The system can be designed to include a number of data storage units including a data dock, a staging area, a data vault, a data mart, a data collection area, a metrics repository, and a metadata repository. Data received from a number of source systems moves through the data storage units and is processed along the way by a number of process areas including a profiling process area, a cleansing process area, a data loading process area, a business rules and integration process area, a propagation, aggregation and subject area breakout process area, and a business intelligence and decision support systems process area. Movement of the data is performed by metagates. The processed data is then received by corporate portals for use in making business decisions. The system may be implemented by an implementation team made up of members including a project manager, a business analyst, a system architect, a data modeler/data architect, a data migration expert, a DSS/OLAP expert, a data profiler/cleanser, and a trainer.

106 citations


Patent
07 Jun 2001
TL;DR: In this article, the authors present a method and apparatus for transporting data for a data warehousing application, where data is extracted from one or more source containing data having a standard data structure and is translated into data that contains meaningful business terms.
Abstract: A method and apparatus for transporting data for a data warehousing application. Data is extracted from one or more source containing data having a standard data structure and is translated into data that contains meaningful business terms. The translated data is then stored. In the present embodiment, an analytic business component is operable for extracting data from the source, translating the extracted data and for storing the translated data into a staging area. The translated data is then processed to obtain data having a common structure. In the present embodiment, a source adapter processes the translated data to obtain data having a common structure. The data having a common structure is then transformed into a format suitable for loading into a data mart. In the present embodiment, an analytic data interface receives the data having a common structure and transforms the data for loading into a data warehouse. The data is then stored in a data warehouse.

94 citations


Patent
25 Jun 2001
TL;DR: In this article, a method and system for performing real-time transformations of dynamically increasing databases is described, in which a session, identified as a real time session, is initialized.
Abstract: A method and system thereof for performing real time transformations of dynamically increasing databases is described. A session, identified as a real time session, is initialized. The real time session repeatedly executes a persistent (e.g., continually running) data transport pipeline of the analytic application. The data transport pipeline extracts data from a changing database, transforms the data, and writes the transformed data to storage (e.g., a data warehouse or data mart). The data transport pipeline is executed at the end of each time interval in a plurality of contiguous time intervals occurring during the real time session. The data transport pipeline remains running after it is executed, until the real time session is completed. Accordingly, new data are transformed in a timely manner, and processing resources are not consumed by having to repeatedly re-establish (re-initialize) the data transport pipeline.

36 citations


01 Jan 2001
TL;DR: This research aimed at defining the basic steps required for a correct design of a data warehouse design methodology, and considered the problem of conceptual design from the operational database as well as some relevant issues related to logical design.
Abstract: The statistic reports about data warehouse project failures state that a major cause lies in the absence of a structured design methodology. In this direction, our research is aimed at defining the basic steps required for a correct design. The goal of this demonstration is to present the main features of WAND, the prototype CASE tool we have implemented to support our methodology. WAND assists the designer in structuring a data mart, carries out conceptual design in a semi-automatic fashion, allows for a core workload to be defined on the conceptual scheme and carries out logical design to produce the data mart scheme. 1 . Motivation and overview Building a data warehouse (DW) for an enterprise is a huge and complex task, which requires an accurate planning aimed at devising satisfactory answers to organizational and architectural questions. There is substantial agreement on the fact that, when planning a DW, a bottom-up approach should be followed: the data mart playing the most strategic role for the enterprise should be identified and prototyped first in order to convince the final users of the potential benefits; other data marts are built progressively, to be finally integrated bottom-up into the global warehouse. Within this process, the phase of data mart design sometimes seems to be given relatively small importance. On the other hand, the statistic reports related to DW project failures state that a major cause lies in the absence of a global view of the design process: in other terms, in the absence of a structured design methodology [6]. We believe that a methodological framework for design is an essential requirement to ensure the success of complex projects. In this direction, our research is aimed at defining the basic steps required for a correct design. In particular, we considered the problem of conceptual design from the operational database as well as some relevant issues related to logical design. It is well-known among software engineers that devising a design methodology is almost useless, if no CASE tool to support it is provided. The goal of this demonstration is to present the main features of WAND ( Wa rehouse I ntegrated Designer), the prototype CASE tool we have implemented to support our methodology. WAND assists the designer in structuring a data mart; it carries out conceptual design in a semi-automatic fashion starting from the logical scheme of the operational database (read via ODBC), allows for a core workload to be defined on the conceptual scheme and carries out logical design to produce the data mart scheme. Both the Table I. The six phases in our DW design methodology. Step Input Output Involves Analysis of the operational database existing documentation (reconciled) database scheme designer; managers of the information system Requirement specification database scheme facts; preliminary workload designer; final users Conceptual design database scheme; facts; preliminary workload conceptual scheme designer; final users Workload refinement, conc. scheme validation conceptual scheme; preliminary workload validated conceptual scheme; workload designer; final users Logical design conceptual scheme; data volume; workload logical scheme designer Physical design logical scheme; target DBMS; workload physical scheme designer

25 citations


Journal ArticleDOI
Heeseok Lee1, Taehun Kim1, Jongho Kim1
TL;DR: A metadata-oriented data warehouse architecture that consists of seven components: legacy system, extracting software, operational data store, data warehouse, data mart, application, and metadata is proposed for better understanding of the architecture.
Abstract: Data warehouse is an intelligent store of data that can aggregate vast amounts of information. A metadata is critical for implementing data warehouse. Therefore, integrating data warehouse with its metadata offers a new opportunity to create a more adaptive information system. This paper proposes a metadata-oriented data warehouse architecture that consists of seven components: legacy system, extracting software, operational data store, data warehouse, data mart, application, and metadata. A taxonomy for dataflow and metaflow is proposed for better understanding of the architecture. In addition, a metadata schema is built within the framework of the seven components. The architecture with its metadata component is applied to a real-life data warehouse for a large medical center in order to illustrate its practical usefulness.

20 citations



Journal Article
TL;DR: The evolution and architecture of a data mart developed to address the modeling and analysis needs of healthcare operations analysts are described and it is shown that the data mart goes well beyond consolidating data from different sources by including a number of complex, precalculated fields, data structures, and function libraries that are specific to the needs of operations analysts.
Abstract: In this article we describe the evolution and architecture of a data mart developed to address the modeling and analysis needs of healthcare operations analysts. More specifically, the data mart is used in projects relating to demand analysis, forecasting, capacity planning, and service system design for a healthcare system consisting of a large tertiary care hospital and a smaller community hospital. The primary focus of the mart is on the detailed movement of inpatients through each hospital, although most component data tables include outpatient information such as emergency center visits, surgical cases, cardiac catheterization cases, and short-stay visits. We show that the data mart goes well beyond consolidating data from different sources by including a number of complex, precalculated fields, data structures, and function libraries that are specific to the needs of operations analysts. We discuss several outstanding and challenging design issues that should be of interest to the data warehouse vendor community.

9 citations


01 Jan 2001
TL;DR: This paper creates a data warehouse model for EMS services and gives the procedure of applying association rule mining based on it, and introduces theory of association rule in data mining, and analyze the characteristics of postal EMS service.
Abstract: Several algorithms in data mining technique have been studied recently, among which association is one of the most important techniques. In this paper, we introduce theory of association rule in data mining, and analyze the characteristics of postal EMS service. We create a data warehouse model for EMS services and give the procedure of applying association rule mining based on it. In the end, we give an example of the whole mining procedure. This EMS Data warehouse model and association rule mining technique have been applied in a practical Postal CRM System.

9 citations


Journal ArticleDOI
TL;DR: The appeal of the data mart strategy is that a mart can be built quickly, at relatively little cost and risk, while providing a service that meets the needs of users across the organization.
Abstract: Companies can build a data warehouse using a top-down or a bottom-up approach, and each has its advantages and disadvantages. With the top-down approach, a project team creates an enterprise data warehouse that combines data from across the organization, and end-user applications are developed after the warehouse is in place. This strategy is likely to result in a scaleable data warehouse, but like most large IT projects, it is time consuming, expensive, and may fail to deliver benefits within a reasonable timeframe. With the bottom-up approach, a project team begins by creating a data mart that has a limited set of data sources and that meets very specific user requirements. After the data mart is complete, subsequent marts are developed, and they are conformed to data structures and processes that are already in place. The data marts are incrementally architected into an enterprise data warehouse that meets the needs of users across the organization. The appeal of the data mart strategy is that a mart can be built quickly, at relatively little cost and risk, while providing a

Journal Article
TL;DR: A hosted clinical data mart for users of a web-enabled charting tool, targeting the solo or small group practice, which delivers immediate value to individual physicians who choose an electronic clinical documentation tool.
Abstract: Office-based physicians are often ill equipped to report aggregate information about their patients and practice of medicine, since their practices have relied upon paper records for the management of clinical information. Physicians who do not have access to large-scale information technology support can now benefit from low-cost clinical documentation and reporting tools. We developed a hosted clinical data mart for users of a web-enabled charting tool, targeting the solo or small group practice. The system uses secure Java Server Pages with a dashboard-like menu to provide point-and-click access to simple reports such as case mix, medications, utilization, productivity, and patient demographics in its first release. The system automatically normalizes user-entered clinical terms to enhance the quality of structured data. Individual providers benefit from rapid patient identification for disease management, quality of care self-assessments, drug recalls, and compliance with clinical guidelines. The system provides knowledge integration by linking to trusted sources of online medical information in context. Information derived from the clinical record is clinically more accurate than billing data. Provider self-assessment and benchmarking empowers physicians, who may resent "being profiled" by external entities. In contrast to large-scale data warehouse projects, the current system delivers immediate value to individual physicians who choose an electronic clinical documentation tool.

Patent
21 Jun 2001
TL;DR: In this article, the authors present a method and apparatus for transporting data for a data warehousing application, where data is extracted from one or more source containing data having a standard data structure and is translated into data that contains meaningful business terms.
Abstract: A method and apparatus for transporting data for a data warehousing application. Data is extracted from one or more source containing data having a standard data structure and is translated into data that contains meaningful business terms. The translated data is then stored. In the present embodiment, an analytic business component is operable for extracting data from the source, translating the extracted data and for storing the translated data into a staging area. The translated data is then processed to obtain data having a common structure. In the present embodiment, a source adapter processes the translated data to obtain data having a common structure. The data having a common structure is then transformed into a format suitable for loading into a data mart. In the present embodiment, an analytic data interface receives the data having a common structure and transforms the data for loading into a data warehouse. The data is then stored in a data warehouse.

Book ChapterDOI
01 Jan 2001
TL;DR: What is a data warehouse?
Abstract: What is a data warehouse? What kinds of data warehouses are there? And what about datamarts? These are just some of the questions answered in this white paper.

Patent
10 Jan 2001
TL;DR: In this paper, a hybrid learning system for searching an experimental space is presented, where a search engine is designed to use unsupervised learning techniques to select a set of evaluation points representing a corresponding set of experiments to be run, based on data from the data mart.
Abstract: A hybrid learning system is provided for searching an experimental space. A data mart is configured to acquire, store, and manipulate a set or meta-set of data including at least historical experimental data, descriptor data, and concurrent experimental data. A search engine is designed to use unsupervised learning techniques to select a set of evaluation points representing a corresponding set of experiments to be run, based on data from the data mart. A point evaluation mechanism provided with supervised learning modules which perform predictive processing based on the evaluation points selected by the search engine, and a scoring module performs a rating operation on outputs of the learning modules to rate the outputs of the learning modules from best to worst. The data mart search engine and point evaluation mechanism allow for a repetitive processing to refine an output of potential solutions without the requirement of continually running actual physical experiments.

Proceedings ArticleDOI
29 Oct 2001
TL;DR: This paper introduces the theory of association rule in data mining, creates a data warehouse model for EMS services and gives the procedure of applying association rule mining based on it, and gives an example of the whole mining procedure.
Abstract: Several algorithms in a data mining technique were studied previously, among which association was one of the most important ones. In this paper, we introduce the theory of association rule in data mining, and analyze the characteristics of the postal EMS service. We create a data warehouse model for EMS services and give the procedure of applying association rule mining based on it. Finally, we give an example of the whole mining procedure. This EMS-data warehouse model and association rule mining technique have been applied in a practical postal CRM system.

Proceedings ArticleDOI
03 Sep 2001
TL;DR: In this article, the authors present a case study in which their proposed process of modelling, called significant conceptual modelling, is applied to the business domain in a health care enterprise, and show significant conceptual modeling as a suitable approach to help business analysts in data mart modelling.
Abstract: Nowadays we are facing a social transition indicating a new reality for our lives and businesses: the competitive approach is globally presented. These "new" approaches show a challenge in modelling implicit knowledge from whatever available data and information, and in the use of that knowledge for timely decision making. To face this challenge organizations need information systems capable to support knowledge dissemination and management. We present a case study in which our proposed process of modelling, called significant conceptual modelling is applied to the business domain in a health care enterprise. We show significant conceptual modelling as a suitable approach to help business analysts in data mart modelling. The proposed process promotes a uniform representation of an organization's business rules allowing good quality results in knowledge discovery and management processes to support decision making processes.

Journal Article
TL;DR: This paper considers the problem of identifying the next data mart to construct and presents a tool based on Quality Function Deployment for use in the planning stages and shows the warehouse seen to evolve over time.
Abstract: In this paper we consider the construction of a dimensional data warehouse. The warehouse is built beginning with the first data mart and proceeding in an iterative manner constructing one mart at a time. In this way the warehouse is seen to evolve over time. This evolutionary process is necessary due to the complexity of data stores, relationships, transformations, and the processing involved. In this paper we consider the problem of identifying the next data mart to construct and present a tool based on Quality Function Deployment for use in the planning stages.

Patent
15 Feb 2001
TL;DR: In this paper, a method for analyzing high capacity distribution database using data mart is provided to supply a mapping method between the management data and a data mart which is designed to provide a logical view of the physically distributed data of a high-capacity database.
Abstract: PURPOSE: A method for analyzing high-capacity distribution database using data mart is provided to supply a mapping method between the management data and a data mart which is designed to provide a logical view of the physically distributed data of a high-capacity database. CONSTITUTION: In order to create a data mart for each category, the data is extracted from a distribution management DB(11) by an extractor/converter(12) that approaches the distribution management DB. The DB stores the data in accordance to the data classification and the DB converts its source data into totalized and standardized data. The converted data is physically loaded to a data mart's(13) table.

Patent
14 Dec 2001
TL;DR: In this article, a hybrid learning system for searching an experimental space is presented, where a search engine is designed to use unsupervised learning techniques to select a set of evaluation points representing a corresponding set of experiments to be run.
Abstract: A hybrid learning system is provided for searching an experimental space. A data mart is configured to acquire, store and manipulate at least historical experimental data, descriptor data, and concurrent experimental data. A search engine is designed to use unsupervised learning techniques to select a set of evaluation points representing a corresponding set of experiments to be run, based on data from the data mart. A point evaluation mechanism provided with supervised learning modules which perform predictive processing based on the evaluation points selected by the search engine, and a scoring module performs a rating operation on outputs of the learning modules to rate the outputs of the learning modules from best to worst. The data mart search engine and point evaluation mechanism allow for a repetitive processing to refine an output of potential solutions the requirement of continually running actual physical experiments.