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EXPLAINING DATA WAREHOUSE DATA TO BUSINESS USERS- A MODEL-BASED APPROACH TO BUSINESS
"... Data Warehouse systems today represent a single source of information for analyzing the development and results of an enterprise organization in a changing environment. The data in the data warehouse describes events and statuses of business processes, products and services, goals and organizational ..."
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Data Warehouse systems today represent a single source of information for analyzing the development and results of an enterprise organization in a changing environment. The data in the data warehouse describes events and statuses of business processes, products and services, goals and organizational units, and generally mirrors every aspect of the structure and behavior of the organization. Business users are accustomed to their own vocabularies and concepts, and data interpretation is greatly improved by knowledge about context. Surprisingly, information about the relationship between the data warehouse data and the organization is not made available to the data warehouse users or even recorded in a suitable way. In this paper, we present an approach that uses enterprise models and modeling techniques to record the at present mainly implicit knowledge about this relationship. We use models to derive Business Metadata, which forms an additional level of abstraction on top of the data-oriented data warehouse structure. Business metadata makes context knowledge easily accessible and improves the data interpretation for the business users.
XML VIEWS, PART III An UML Based Design Methodology for XML Views
"... Abstract: Object-Oriented (OO) conceptual models have the power in describing and modelling real-world data semantics and their inter-relationships in a form that is precise and comprehensible to users. Today UML has established itself as the language of choice for modelling complex enterprises info ..."
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Abstract: Object-Oriented (OO) conceptual models have the power in describing and modelling real-world data semantics and their inter-relationships in a form that is precise and comprehensible to users. Today UML has established itself as the language of choice for modelling complex enterprises information systems (EIS) using OO techniques. Conversely, the eXtensible Markup Language (XML) is fast emerging as the dominant standard for storing, describing and interchanging data among various enterprises systems and databases. With the introduction of XML Schema, which provides rich facilities for constraining and defining XML content, XML provides the ideal platform and the flexibility for capturing and representing complex enterprise data formats. Yet, UML provides insufficient modelling constructs for utilising XML schema based data description and constraints, while XML Schema lacks the ability to provide higher levels of abstraction (such as conceptual models) that are easily understood by humans. Therefore to enable efficient business application development of large-scale enterprise systems, we need UML like models with rich XML schema like semantics. To address such issue, in this paper, we proposed a generic, semantically rich view mechanism to conceptually model and design (using UML) XML domains to support data modelling of complex domains such as data warehousing and e-commerce systems. Our approach is based on UML and UML stereotypes to design and transform XML views. 1
Conceptual Design of an XML-View Driven, Global XML FACT Repository for XML Document Warehouses
"... ©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other wo ..."
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©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Metrics for data warehouse conceptual models understandability
, 2006
"... Due to the principal role of Data warehouses (DW) in making strategy decisions, data warehouse quality is crucial for organizations. Therefore, we should use methods, models, techniques and tools to help us in designing and maintaining high quality DWs. In the last years, there have been several app ..."
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Due to the principal role of Data warehouses (DW) in making strategy decisions, data warehouse quality is crucial for organizations. Therefore, we should use methods, models, techniques and tools to help us in designing and maintaining high quality DWs. In the last years, there have been several approaches to design DWs from the conceptual, logical and physical perspectives. However, from our point of view, none of them provides a set of empirically validated metrics (objective indicators) to help the designer in accomplishing an outstanding model that guarantees the quality of the DW. In this paper, we firstly summarise the set of metrics we have defined to measure the understandability (a quality subcharacteristic) of conceptual models for DWs, and present their theoretical validation to assure their correct definition. Then, we focus on deeply describing the empirical validation process we have carried out through a family of experiments performed by students, professionals and experts in DWs. This family of experiments is a very important aspect in the process of validating metrics as it is widely accepted that only after performing a family of experiments, it is possible to build up the cumulative knowledge to extract useful measurement conclusions to be applied in practice. Our whole empirical process showed us that several of the proposed metrics seems to be practical indicators of the understandability of conceptual models for DWs.
1160 CAiSE'06 Doctoral Consortium Bridging the Gap between Data Warehouses and Organizations
"... Abstract. Data Warehouse (DWH) systems are used by decision makers for performance measurement and decision support. Currently the main focus of the DWH research field is not as much on the interaction of the DWH with the organization, its context and the way it supports the organization’s strategic ..."
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Abstract. Data Warehouse (DWH) systems are used by decision makers for performance measurement and decision support. Currently the main focus of the DWH research field is not as much on the interaction of the DWH with the organization, its context and the way it supports the organization’s strategic goals, as on database issues. The aim of my thesis is to emphasize and describe the relationship between the DWH and the organization with conceptual models, and to use this knowledge to support data interpretation with business metadata. 1 Problem Statement and Research Question Data Warehouse (DWH) systems represent a single source of information to analyze the development and results of an organization[1]. Measures such as the number of transactions per customer or the increase of sales during a promotion are used to recognize warning signs and to decide on future investments with regard to the strategic goals of the organization. Currently, the main focus of the DWH research field is on database issues,
Bridging the Gap between Data Warehouses
"... Data Warehouse (DWH) systems are used by decision makers for performance measurement and decision support. Currently the main focus of the DWH research field is not as much on the interaction of the DWH with the organization, its context and the way it supports the organization's strategic ..."
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Data Warehouse (DWH) systems are used by decision makers for performance measurement and decision support. Currently the main focus of the DWH research field is not as much on the interaction of the DWH with the organization, its context and the way it supports the organization's strategic goals, as on database issues. The aim of my thesis is to emphasize and describe the relationship between the DWH and the organization with conceptual models, and to use this knowledge to support data interpretation with business metadata.
Conceptual Design of an XML FACT Repository for Dispersed XML
"... ©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other wo ..."
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©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
en Images et Systèmes d'information
"... Data warehouses and OLAP systems help to interactively analyze huge volume of data. This data, extracted from transactional databases, frequently contains spatial information which is useful for decision-making process. Integration of spatial data in multidimensional models leads to the concept of S ..."
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Data warehouses and OLAP systems help to interactively analyze huge volume of data. This data, extracted from transactional databases, frequently contains spatial information which is useful for decision-making process. Integration of spatial data in multidimensional models leads to the concept of SOLAP (Spatial OLAP). Using a spatial measure as a geographical object, i.e. with geometric and descriptive attributes, raises problems regarding the aggregation operation in its semantic and implementation aspects. This paper shows the requirements for a multidimensional spatial data model and presents a multidimensional data model which is able to support complex objects as measures, inter-dependent attributes for measures and aggregation functions, use of ad-hoc aggregation functions and n to n relations between fact and dimension, in order to handle geographical data, according to its particular nature in an OLAP context.
Towards Development of Solution for Business Process-Oriented Data Analysis
"... Abstract—This paper proposes a modeling methodology for the development of data analysis solution. The Author introduce the approach to address data warehousing issues at the at enterprise level. The methodology covers the process of the requirements eliciting and analysis stage as well as initial d ..."
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Abstract—This paper proposes a modeling methodology for the development of data analysis solution. The Author introduce the approach to address data warehousing issues at the at enterprise level. The methodology covers the process of the requirements eliciting and analysis stage as well as initial design of data warehouse. The paper reviews extended business process model, which satisfy the needs of data warehouse development. The Author considers that the use of business process models is necessary, as it reflects both enterprise information systems and business functions, which are important for data analysis. The Described approach divides development into three steps with different detailed elaboration of models. The Described approach gives possibility to gather requirements and display them to business users in easy manner. Keywords—Data warehouse, data analysis, business process management. I.
EXPLAINING DATA WAREHOUSE DATA TO BUSINESS USERS- A MODEL-BASED APPROACH TO BUSINESS
"... Data Warehouse systems today represent a single source of information for analyzing the development and results of an enterprise organization in a changing environment. The data in the data warehouse describes events and statuses of business processes, products and services, goals and organizational ..."
Abstract
- Add to MetaCart
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Data Warehouse systems today represent a single source of information for analyzing the development and results of an enterprise organization in a changing environment. The data in the data warehouse describes events and statuses of business processes, products and services, goals and organizational units, and generally mirrors every aspect of the structure and behavior of the organization. Business users are accustomed to their own vocabularies and concepts, and data interpretation is greatly improved by knowledge about context. Surprisingly, information about the relationship between the data warehouse data and the organization is not made available to the data warehouse users or even recorded in a suitable way. In this paper, we present an approach that uses enterprise models and modeling techniques to record the at present mainly implicit knowledge about this relationship. We use models to derive Business Metadata, which forms an additional level of abstraction on top of the data-oriented data warehouse structure. Business metadata makes context knowledge easily accessible and improves the data interpretation for the business users.
