An Empirical Evaluation of a Historical Data Warehouse

Computing is widely regarded as a scientific discipline that emphasizes on three different perspectives: mathematics, present in the development of formalisms, theories and algorithms; engineering, linked to the goal of making things better, faster, smaller, cheaper and, finally, the science that can be defined as the activity to develop general and predictive theories that allow these theories to be evaluated and tested. However, research in software engineering rarely describes explicitly its research paradigms and standards to assess the quality of its results. Due to a growing understanding in the computer science community that empirical studies are needed to improve processes, methods and tools for the development and maintenance of software, an emerging area in software engineering is developed: the Empirical Software Engineering. This subarea is one step down in the claims of scientificity but it aims to address this shortcoming. The objective of this work is to conduct an empirical corroboration for developing a method of a Historical Data Warehouse, the temporal data model and the associated query interface.Sociedad Argentina de Informática e Investigación Operativa (SADIO

An Empirical Evaluation of a Historical Data Warehouse

Computing is widely regarded as a scientific discipline that emphasizes on three different perspectives: mathematics, present in the development of formalisms, theories and algorithms; engineering, linked to the goal of making things better, faster, smaller, cheaper and, finally, the science that can be defined as the activity to develop general and predictive theories that allow these theories to be evaluated and tested. However, research in software engineering rarely describes explicitly its research paradigms and standards to assess the quality of its results. Due to a growing understanding in the computer science community that empirical studies are needed to improve processes, methods and tools for the development and maintenance of software, an emerging area in software engineering is developed: the Empirical Software Engineering. This subarea is one step down in the claims of scientificity but it aims to address this shortcoming. The objective of this work is to conduct an empirical corroboration for developing a method of a Historical Data Warehouse, the temporal data model and the associated query interface.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Diseño de un almacén de datos histórico en el marco del desarrollo de software dirigido por modelos

Un Decision Support System (DSS) asiste a los usuarios en el proceso de análisis de datos en una organización con el propósito de producir información que les permita tomar mejores decisiones. Los analistas que utilizan el DSS están más interesados en identificar tendencias que en buscar algún registro individual en forma aislada [HRU96]. Con ese propósito, los datos de las diferentes transacciones se almacenan y consolidan en una base de datos central denominada Data Warehouse (DW); los analistas utilizan esas estructuras de datos para extraer información de sus negocios que les permita tomar mejores decisiones [GHRU97]. Basándose en el esquema de datos fuente y en los requisitos de información de la organización, el objetivo del diseñador de un DSS es sintetizar esos datos para reducirlos a un formato que le permita, al usuario de la aplicación, utilizarlos en el análisis del comportamiento de la empresa. Dos tipos diferentes (pero relacionados) de actividades están presentes: el diseño de las estructuras de almacenamiento y la creación de consultas sobre esas estructuras. La primera tarea se desarrolla en el ámbito de los diseñadores de aplicaciones informáticas; la segunda, en la esfera de los usuarios finales. Ambas actividades, normalmente, se realizan con escasa asistencia de herramientas automatizadas.Eje: Tecnología Informática aplicada en educaciónRed de Universidades con Carreras en Informática (RedUNCI

An Empirical Evaluation of a Historical Data Warehouse

Abstract. Computing is widely regarded as a scientific discipline that emphasizes on three different perspectives: mathematics, present in the development of formalisms, theories and algorithms; engineering, linked to the goal of making things better, faster, smaller, cheaper and, finally, the science that can be defined as the activity to develop general and predictive theories that allow these theories to be evaluated and tested. However, research in software engineering rarely describes explicitly its research paradigms and standards to assess the quality of its results. Due to a growing understanding in the computer science community that empirical studies are needed to improve processes, methods and tools for the development and maintenance of software, an emerging area in software engineering is developed: the Empirical Software Engineering. This subarea is one step down in the claims of scientificity but it aims to address this shortcoming. The objective of this work is to conduct an empirical corroboration for developing a method of a Historical Data Warehouse, the temporal data model and the associated query interface

An Empirical Evaluation of a Historical Data Warehouse

Computing is widely regarded as a scientific discipline that emphasizes on three different perspectives: mathematics, present in the development of formalisms, theories and algorithms; engineering, linked to the goal of making things better, faster, smaller, cheaper and, finally, the science that can be defined as the activity to develop general and predictive theories that allow these theories to be evaluated and tested. However, research in software engineering rarely describes explicitly its research paradigms and standards to assess the quality of its results. Due to a growing understanding in the computer science community that empirical studies are needed to improve processes, methods and tools for the development and maintenance of software, an emerging area in software engineering is developed: the Empirical Software Engineering. This subarea is one step down in the claims of scientificity but it aims to address this shortcoming. The objective of this work is to conduct an empirical corroboration for developing a method of a Historical Data Warehouse, the temporal data model and the associated query interface.Sociedad Argentina de Informática e Investigación Operativa (SADIO

A Model-Driven Approach for Mobile Business Intelligence

The concept of Mobile Business Intelligence is nowadays gaining prominence in business markets. With the emergence and evolution of mobile technologies such as smartphones and tablets, the users gain the opportunity to analyze the corporate information, anywhere and anytime, based on charts, tables and dashboards. However, there is also the question of how to provide the freedom to the user to build its own analytical components. This work will address the problem of developing a hybrid mobile solution towards the Business Intelligence domain, offering monitoring services and simultaneously addressing the problem of user empowerment, with easy configuration and semi-automatic generation of analytical widgets. To provide such capacity to the user, the proposed solution is based on the design of a Domain Specific Modeling Language, aligned with the Model-Driven Development approach and inspired by the Product Lines principles. The last part of this work is dedicated to evaluate the language usability based on an empirical test, executed by a set of subjects with different backgrounds of specialization. In this sense, we define two groups: end users and domain experts. The goal is to determine the extent to which the prototype can be used to empower the end users. As support for the analysis we have extracted a set of measures, alongside with the final appreciation from the domain experts group, composed by people currently working on Business Intelligence

A probabilistic multidimensional data model and its applications in business management

This dissertation develops a conceptual data model that can efficiently handle huge volumes of data containing uncertainty and are subject to frequent changes. This model can be used to build Decision Support Systems to improve decision-making process. Business intelligence and decision-making in today\u27s business world require extensive use of huge volumes of data. Real world data contain uncertainty and change over time. Business leaders should have access to Decision Support Systems that can efficiently handle voluminous data, uncertainty, and modifications to uncertain data. Database product vendors provide several extensions and features to support these requirements; however, these extensions lack support of standard conceptual models. Standardization generally creates more competition and leads to lower prices and improved standards of living. Results from this study could become a data model standard in the area of applied decisions sciences. The conceptual data model developed in this dissertation uses a mathematical concept based on set theory, probability axioms, and the Bayesian framework. Conceptual data model, algebra to manipulate data, a framework and an algorithm to modify the data are presented. The data modification algorithm is analyzed for time and space efficiency. Formal mathematical proof is provided to support identified properties of model, algebra, and the modification framework. Decision-making ability of this model was investigated using sample data. Advantages of this model and improvements in inventory management through its application are described. Comparison and contrast between this model and Bayesian belief networks are presented. Finally, scope and topics for further research are described

Diseño de un Almacén de Datos Históricos en el marco del desarrollo de software dirigido por modelos

Un Decision Support System (DSS) asiste a los usuarios en el proceso de análisis de datos en una organización con el propósito de producir información que les permita tomar mejores decisiones. Los analistas que utilizan el DSS están más interesados en identificar tendencias que en buscar algún registro individual en forma aislada [HRU96]. Con ese propósito, los datos de las diferentes transacciones se almacenan y consolidan en una base de datos central denominada Data Warehouse (DW); los analistas utilizan esas estructuras de datos para extraer información de sus negocios que les permita tomar mejores decisiones [GHRU97]. Basándose en el esquema de datos fuente y en los requisitos de información de la organización, el objetivo del diseñador de un DSS es sintetizar esos datos para reducirlos a un formato que le permita, al usuario de la aplicación, utilizarlos en el análisis del comportamiento de la empresa. Dos tipos diferentes (pero relacionados) de actividades están presentes: el diseño de las estructuras de almacenamiento y la creación de consultas sobre esas estructuras. La primera tarea se desarrolla en el ámbito de los diseñadores de aplicaciones informáticas; la segunda, en la esfera de los usuarios finales. Ambas actividades, normalmente, se realizan con escasa asistencia de herramientas automatizadas. A partir de lo expresado anteriormente Identificamos, por consiguiente, tres problemas a resolver: a) la creación de estructuras de almacenamiento eficientes para la toma de decisión, b) la simplificación en la obtención de la información sobre esas estructuras para el usuario final y, c) la automatización, tanto del proceso de diseño de las estructuras de almacenamiento, como en la elaboración iterativa de consultas por parte del usuario de la aplicación. La solución propuesta es el diseño de una nueva estructura de almacenamiento que denominaremos Historical Data Warehouse (HDW) que combina, en un modelo integrado, un Historical Data Base (HDB) y un DW; el diseño de una interface gráfica, derivada del HDW, que permite realizar consultas en forma automática y, por último, el desarrollo de un método de diseño que engloba ambas propuestas en el marco del Model Driven Software Development (MDD).Facultad de Informátic

A Model-Driven Architecture based Evolution Method and Its Application in An Electronic Learning System

Software products have been racing against aging problem for most of their lifecycles, and evolution is the most effective and efficient solution to this problem. Model-Driven Architecture (MDA) is a new technique for software product for evolving development and reengineering methods. The main steps for MDA are to establish models in different levels and phases, therefore to solve the challenges of requirement and technology change. However, there is only a standard established by Object Management Group (OMG) but without a formal method and approach. Presently, MDA is widely researched in both industrial and research areas, however, there is still without a smooth approach to realise it especially in electronic learning (e-learning) system due to the following reasons: (1) models’ transformations are hard to realise because of lack of tools, (2) most of existing mature research results are working for business and government services but not education area, and (3) most of existing model-driven researches are based on Model-Driven Development (MDD) but not MDA because of OMG standard’s preciseness. Hence, it is worth to investigate an MDA-based method and approach to improve the existing software development approach for e-learning system. Due to the features of MDA actuality, a MDA-based evolution method and approach is proposed in this thesis. The fundamental theories of this research are OMG’s MDA standard and education pedagogical knowledge. Unified Modelling Language (UML) and Unified Modelling Language Profile are hired to represent the information of software system from different aspects. This study can be divided into three main parts: MDA-based evolution method and approach research, Platform-Independent Model (PIM) to Platform-Specific Model (PSM) transformation development, and MDA-based electronic learning system evolution. Top-down approach is explored to develop models for e-learning system. A transformation approach is developed to generate Computation Independent Model (CIM), Platform-Independent Model (PIM), and Platform-Specific Model (PSM); while a set of transformation rules are defined following MDA standard to support PSM’ s generation. In addition, proposed method is applied in an e-learning system as a case study with the prototype rules support. In the end, conclusions are drawn based on analysis and further research directions are discussed as well. The kernel contributions are the proposed transformation rules and its application in electronic learning system