Jesús Pardillo

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.

Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses

The development of data warehouses begins with the definition of multidimensional models at the conceptual level in order to structure data, which will facilitate decision makers with an easier data analysis. Current proposals for conceptual multidimensional modelling focus on the design of static data warehouse structures, but few approaches model the queries which the data warehouse should support by means of OLAP (on-line analytical processing) tools. OLAP queries are, therefore, only defined once the rest of the data warehouse has been implemented, which prevents designers from verifying from the very beginning of the development whether the decision maker will be able to obtain the required information from the data warehouse. This article presents a solution to this drawback consisting of an extension to the object constraint language (OCL), which has been developed to include a set of predefined OLAP operators. These operators can be used to define platform-independent OLAP queries as a part of the specification of the data warehouse conceptual multidimensional model. Furthermore, OLAP tools require the implementation of queries to assure performance optimisations based on pre-aggregation. It is interesting to note that the OLAP queries defined by our approach can be automatically implemented in the rest of the data warehouse, in a coherent and integrated manner. This implementation is supported by a code-generation architecture aligned with model-driven technologies, in particular the MDA (model-driven architecture) proposal. Finally, our proposal has been validated by means of a set of sample data sets from a well-known case study.

Using ontologies for the design of data warehouses

Spanish Ministry of Education and Science funds Jesus Pardillo under the FPU grant AP2006-00332.Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.

A Conceptual Modeling Approach for OLAP Personalization

Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies. However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance. Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex. As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated. In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process. To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour. In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules. The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs. Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development.

Modelling ETL Processes of Data Warehouses with UML Activity Diagrams

Extraction-transformation-loading (ETL) processes play an important role in a data warehouse (DW) architecture because they are responsible of integrating data from heterogeneous data sources into the DW repository. Importantly, most of the budget of a DW project is spent on designing these processes since they are not taken into account in the early phases of the project but once the repository is deployed. In order to overcome this situation, we propose using the unified modelling language (UML) to conceptually model the sequence of activities involved in ETL processes from the beginning of the project by using activity diagrams (ADs). Our approach provides designers with easy-to-use modelling elements to capture the dynamic aspects of ETL processes.

A systematic review on the definition of UML profiles

This article reports a systematic review on the definition of UML profiles in the research literature. Several exploratory statistical analyses have been performed in order to characterise both the idiosyncrasy of UML profiles and how they are reported in the literature. This study uncovers the differences between presentation styles for behavioural and structural domains, and shows how UML profiles based on Class, Association, and Property structural metaclasses clearly outnumber any other kind. Also, this review reveals how half of the examined UML profiles merely extend the abstract syntax, without adding neither icons nor constraints. The main contribution of this study is therefore a clear picture of the state-of-the-art in UML profiling, together with a set of open questions regarding its future.

Integrating clustering data mining into the multidimensional modeling of data warehouses with UML profiles

Clustering can be considered the most important unsupervised learning technique finding similar behaviors (clusters) on large collections of data. Data warehouses (DWs) can help users to analyze stored data, because they contain preprocessed data for analysis purposes. Furthermore, the multidimensional (MD) model of DWs, intuitively represents the system underneath. However, most of the clustering data mining are applied at a low-level of abstraction to complex unstructured data. While there are several approaches for clustering on DWs, there is still not a conceptual model for clustering that facilitates modeling with this technique on the multidimensional (MD) model of a DW. Here, we propose (i) a conceptual model for clustering that helps focusing on the data-mining process at the adequate abstraction level and (ii) an extension of the unified modeling language (UML) by means of the UML profiling mechanism allowing us to design clustering data-mining models on top of the MD model of a DW. This will allow us to avoid the duplication of the time-consuming preprocessing stage and simplify the clustering design on top of DWs improving the discovery of knowledge.

Modelling Web-Based Systems Requirements Using WRM

In recent years, Web Information Systems (WIS) development projects have grown increasingly complex and critical for the smooth running of the organizations. However, recent studies reveal that a high percentage of web-based development projects miss the quality parameters required by stakeholders, most of the time due to an incorrect requirements management. In this paper, we try to increase the weight of requirements engineering activities in Web Engineering, and propose a web engineering requirements metamodel extension that can be smoothly integrated with existing web engineering proposals in order to reinforce the first phases of systematic web development. Our proposal is accompanied by a tool that, being developed as an Eclipse plug-in, can also be integrated with any existing web engineering methodology developed under this general framework.

Bridging the semantic gap in OLAP models: platform-independent queries

The development of data warehouses is based on a three-stage process that starts specifying both the static and dynamic properties of on-line analytical processing (OLAP) applications by means of an intuitive, semantically rich abstraction, namely the conceptual model. Then, developers design its logical counterpart where platform-specific details such as performance or storage are also considered. Nevertheless, it is well known the existence of a semantic gap between the conceptual and logical levels that decreases the feasibility of their mapping. In order to bridge this gap, we propose the use of conceptual OLAP queries, i.e., platform-independent, that can be automatically traced to their logical implementation in a coherent and integrated way. For this aim, in this paper, we focus on describing the specification of an OLAP algebra at the conceptual level by using the object-constraint language (OCL). Its operations are then translated into a particular OLAP system by using a model-driven architecture (MDA). The great advantage of our approach is that we allow analysts to query data warehouses without being aware of logical details.

Applying transformations to model driven data warehouses

In the past few years, several conceptual approaches have been proposed for the specification of the main multidimensional (MD) properties of the data warehouse (DW) repository. However, these approaches often fail in providing mechanisms to univocally and automatically derive a logical representation of the MD conceptual model. To overcome this limitation, we present an approach to align the MD modeling of the DW repository with the Model Driven Architecture (MDA) by formally defining a set of Query/View/Transformation (QVT) transformation rules which allow us to obtain a logical representation of the MD conceptual model in an automatic way. Finally, we show how to implement our approach in an MDA-compliant commercial tool.