Juan Trujillo

A UML profile for multidimensional modeling in data warehouses

The multidimensional (MD) modeling, which is the foundation of data warehouses (DWs), MD databases, and On-Line Analytical Processing (OLAP) applications, is based on several properties different from those in traditional database modeling. In the past few years, there have been some proposals, providing their own formal and graphical notations, for representing the main MD properties at the conceptual level. However, unfortunately none of them has been accepted as a standard for conceptual MD modeling.In this paper, we present an extension of the Unified Modeling Language (UML) using a UML profile. This profile is defined by a set of stereotypes, constraints and tagged values to elegantly represent main MD properties at the conceptual level. We make use of the Object Constraint Language (OCL) to specify the constraints attached to the defined stereotypes, thereby avoiding an arbitrary use of these stereotypes. We have based our proposal in UML for two main reasons: (i) UML is a well known standard modeling language known by most database designers, thereby designers can avoid learning a new notation, and (ii) UML can be easily extended so that it can be tailored for a specific domain with concrete peculiarities such as the multidimensional modeling for data warehouses. Moreover, our proposal is Model Driven Architecture (MDA) compliant and we use the Query View Transformation (QVT) approach for an automatic generation of the implementation in a target platform. Throughout the paper, we will describe how to easily accomplish the MD modeling of DWs at the conceptual level. Finally, we show how to use our extension in Rational Rose for MD modeling.

An MDA approach for the development of data warehouses

Different modeling approaches have been proposed to overcome every design pitfall of different data warehouse (DW) components. However, most of them offer partial solutions that deal only with isolated aspects of the DW and do not provide developers with an integrated and standard framework for designing all DW relevant components, such as ETL processes, data sources, DW repository and so on. To overcome this problem, this paper describes how to align the whole DW development process with a Model Driven Architecture (MDA) framework. We then focus on describing one part of it: an MDA approach for the development of the DW repository, because it is the cornerstone of any DW system. Therefore, we describe how to build the different MDA models for the DW repository by using an extension of the Unified Modeling Language (UML) and the Common Warehouse Metamodel (CWM). Transformations between models are also clearly and formally established by using the Query/View/Transformation (QVT) language. Finally, a case study is provided to exemplify the benefits of our MDA framework.

A UML Based Approach for Modeling ETL Processes in Data Warehouses

Data warehouses (DWs) are complex computer systems whose main goal is to facilitate the decision making process of knowledge workers. ETL (Extraction-Transformation-Loading) processes are responsible for the extraction of data from heterogeneous operational data sources, their transformation (conversion, cleaning, normalization, etc.) and their loading into DWs. ETL processes are a key component of DWs because incorrect or misleading data will produce wrong business decisions, and therefore, a correct design of these processes at early stages of a DW project is absolutely necessary to improve data quality. However, not much research has dealt with the modeling of ETL processes. In this paper, we present our approach, based on the Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of these ETL processes. We provide the necessary mechanisms for an easy and quick specification of the common operations defined in these ETL processes such as, the integration of different data sources, the transformation between source and target attributes, the generation of surrogate keys and so on. Another advantage of our proposal is the use of the UML (standardization, ease-of-use and functionality) and the seamless integration of the design of the ETL processes with the DW conceptual schema.

A model-driven goal-oriented requirement engineering approach for data warehouses

The development of a data warehouse has been traditionally guided by an in-depth analysis of the underlying operational data sources, thus overlooking an explicit development phase in which information requirements of decision makers are addressed. This scenario has prompted that the deployed data warehouse often fails in delivering the expected support of the decision making process. To overcome this problem, we propose to use the i* modeling framework and the model driven architecture (MDA) in order to describe (i) how to model goals and information requirements for data warehouses, and (ii) how to derive a conceptual multidimensional model that provides the required information to support the decision making process.

Data Mapping Diagrams for Data Warehouse Design with UML

In Data Warehouse (DW) scenarios, ETL (Extraction, Transformation, Loading) processes are responsible for the extraction of data from heterogeneous operational data sources, their transformation (conversion, cleaning, normalization, etc.) and their loading into the DW. In this paper, we present a framework for the design of the DW back-stage (and the respective ETL processes) based on the key observation that this task fundamentally involves dealing with the specificities of information at very low levels of granularity including transformation rules at the attribute level. Specifically, we present a disciplined framework for the modeling of the relationships between sources and targets in different levels of granularity (including coarse mappings at the database and table levels to detailed inter-attribute mappings at the attribute level). In order to accomplish this goal, we extend UML (Unified Modeling Language) to model attributes as first-class citizens. In our attempt to provide complementary views of the design artifacts in different levels of detail, our framework is based on a principled approach in the usage of UML packages, to allow zooming in and out the design of a scenario.

Extending the UML for Multidimensional Modeling

Multidimensional (MD) modeling is the foundation of data warehouses, MD databases, and On-Line Analytical Processing (OLAP) applications. In the past few years, there have been some proposals for representing the main MD properties at the conceptual level providing their own notations. In this paper, we present an extension of the Unified Modeling Language (UML), by means of stereotypes, to elegantly represent main structural and dynamic MD properties at the conceptual level. We make use of the Object Constraint Language (OCL) to specify the constraints attached to the defined stereotypes, thereby avoiding an arbitrary use of these stereotypes. The main advantage of our proposal is that it is based on a well-known standard modeling language, thereby designers can avoid learning a new specific notation or language for MD systems. Finally, we show how to use these stereotypes in Rational Rose 2000 for MD modeling.

A survey on summarizability issues in multidimensional modeling

The development of a data warehouse (DW) system is based on a conceptual multidimensional model, which provides a high level of abstraction in accurately and expressively describing real-world situations. Once this model is designed, the corresponding logical representation must be obtained as the basis of the implementation of the DW according to one specific technology. However, even though a good conceptual multidimensional model is designed underneath a DW, there is a semantic gap between this model and its logical representation. In particular, this gap complicates an adequate treatment of summarizability issues, which in turn may lead to erroneous results of data analysis tools. Research addressing this topic has produced only partial solutions, and individual terminology used by different parties hinders further progress. Consequently, based on a unifying vocabulary, this survey sheds light on (i) the weak and strong points of current approaches for modeling complex multidimensional structures that reflect real-world situations in a conceptual multidimensional model and (ii) existing mechanisms to avoid summarizability problems when conceptual multidimensional models are being implemented.

Reconciling requirement-driven data warehouses with data sources via multidimensional normal forms

Successful data warehouse (DW) design needs to be based upon a requirement analysis phase in order to adequately represent the information needs of DW users. Moreover, since the DW integrates the information provided by data sources, it is also crucial to take these sources into account throughout the development process to obtain a consistent reconciliation of data sources and information needs. In this paper, we start by summarizing our approach to specify user requirements for data warehouses and to obtain a conceptual multidimensional model capturing these requirements. Then, we make use of the multidimensional normal forms to define a set of Query/View/Transformation (QVT) relations to assure that the conceptual multidimensional model obtained from user requirements agrees with the available data sources that will populate the DW. Thus, we propose a hybrid approach to develop DWs, i.e., we firstly obtain the conceptual multidimensional model of the DW from user requirements and then we verify and enforce its correctness against data sources by using a set of QVT relations based on multidimensional normal forms. Finally, we provide some snapshots of the CASE tool we have used to implement our QVT relations.

Applying MDA to the development of data warehouses

Different modeling approaches have been proposed to overcome every design pitfall of the development of the different parts of a data warehouse (DW) system. However, they are all partial solutions which deal with isolated aspects of the DW and do not provide designers with an integrated and standard method for designing the whole DW (ETL processes, data sources, DW repository and so on). On the other hand, the Model Driven Architecture (MDA) is a standard framework for software development that addresses the complete life cycle of designing, deploying, integrating, and managing applications by using models in software development. In this paper, we describe how to align the whole DW development process to MDA. Then, we define MD2A (MultiDimensional Model Driven Architecture), an approach for applying the MDA framework to one of the stages of the DW development: multidimensional (MD) modeling. First, we describe how to build the different MDA artifacts (i.e. models) by using extensions of the Unified Modeling Language (UML). Secondly, transformations between models are clearly and formally established by using the Query/View/Transformation (QVT) approach. Finally, an example is provided to better show how to apply MDA and its transformations to the MD modeling.

Multidimensional Modeling with UML Package Diagrams

The Unified Modeling Language (UML) has become the de facto standard for object-oriented analysis and design, providing different diagrams for modeling different aspects of a system. In this paper, we present the development of multidimensional (MD) models for data warehouses (DW) using UML package diagrams. In this way, when modeling complex and large DW systems, we are not restricted to use flat UML class diagrams. We present design guidelines and illustrate them with various examples. We show that the correct use of the package diagrams using our design guidelines will produce a very simple yet powerful design of MD models. Furthermore, we provide a UML extension by means of stereotypes of the particular package items we use. Finally, we show how to use these stereotypes in Rational Rose 2000 for MD modeling.