Designing secure data warehouses by using MDA and QVT

The Data Warehouse (DW) design is based on multidimensional (MD) modeling which structures information into facts and dimensions. Due to the confidentiality of the data that it stores, it is crucial to specify security and audit measures from the early stages of design and to enforce them throughout the lifecycle. Moreover, the standard framework for software development, Model Driven Architecture (MDA), allows us to define transformations between models by proposing Query/View/Transformations (QVT). This proposal permits the definition of formal, elegant and unequivocal transformations between Platform Independent Models (PIM) and Platform Specific Models (PSM). This paper introduces a new framework for the design of secure DWs based on MDA and QVT, which covers all the design phases (conceptual, logical and physical) and specifies security measures in all of them. We first define two metamodels with which to represent security and audit measures at the conceptual and logical levels. We then go on to define a transformation between these models through which to obtain the traceability of the security rules from the early stages of development to the final implementation. Finally, in order to show the benefits of our proposal, it is applied to a case study.This work has been partially supported by the METASIGN project (TIN2004-00779) from the Spanish Ministry of Education and Science, of the Regional Government of Valencia, and by the QUASIMODO and MISTICO projects of the Regional Science and Technology Ministry of Castilla-La Mancha (Spain)

A UML framework for OLAP conceptual modeling

Data warehouses are used by organizations around the world to store huge volumes of historical data. Ultimately, the purpose of the warehouse is to allow decision makers to assess both the history and, more importantly, the future of the organization. In practice, the capacity to make meaningful decisions is further supported through the use of Online Analytical Processing (OLAP) applications that provide more sophisticated representations of the warehouse data. In order to do this, OLAP systems rely on a multidimensional conceptual data model that represents the core elements of the data warehouse, as well as the relationships between them. Currently, there is no definitive conceptual model for this kind of environment. It is therefore quite difficult for data warehouse designers to express the kinds of complex analytical requirements which arise in real-world situations. In this thesis, we propose a robust and flexible conceptual model that can be used to represent multi-dimensional OLAP domains. Specifically, we present a profile extension of the Unified Modeling Language (UML) that consists of a set of stereotypes, constraints and tagged values that elegantly represent multi-dimensional properties at the conceptual level. We also make use of the Object Constraint Language (OCL) to ensure the correctness and completeness of the specification, thereby avoiding an arbitrary use of the basic components. Furthermore, we demonstrate how the new OLAP profile is utilized in MagicDraw, one of the leading UML development tools. The end result is an OLAP Modeling Environment (OME) that should significantly reduce development time, as well as improving the quality of the analytical interface for the end user

Business intelligence and nosocomial infection decision making

Nosocomial infection prevention in healthcare units it is very important to improve patient’s well-being and safety. This prevention can be done by manipulating and analysing real data to identify critical processes and areas inside the healthcare unit, and monitoring indicators generated from data. The main goal of this paper is to evaluate the applicability of the Business Intelligence tools and concepts to healthcare and their performance as a Clinical Decision Support System, analyzing the evolution of nosocomial infection in the Centro Hospitalar do Porto, by defining a set of indicators that can help the nosocomial infection management and inducing Data Mining models to predict the occurrence of nosocomial infections (sensitivity of 91%). A Business Intelligence system composed by the presentation of a set of indicators and a Data Mining part capable of predict the occurrence of infection can provide important information to support healthcare professionals in their decisions.(undefined

Project Cost Plan Forecasting in Marine Services Industry

This thesis investigates difficulties of project cost planning for a global organization in marine services industry. The research was conducted with co-operation with the organization, and re-quired datasets were gathered from organization’s data warehouse. Forecasting of a cost plan is a difficult task where the intention is to predict the unknown risks and wrong estimations in cost plan causes difficulties and uncertainty when risks arise. The main objective of this thesis is to find common fundamental characteristics for positive and negative cost overruns affecting the cost plans and investigate how to better identify risky project char-acteristics for more accurate cost planning. Previous research with the exact same objectives in the same industry area are in few, if any. However, similar previous research states forecast and planning difficulties. Methods of this study was a combination of both qualitative and quantitative research methods. The data was gathered from the enterprise data warehouse and the selected dataset consisted of around 2500 projects started in marine services industry from beginning of 2018 to end of 2020. For better investigation of this dataset, a data-driven multi-dimensional data model was created where a regressor model was created to forecast new cost plan values. In the research it was highlighted that the risky project characteristics can be identified to certain segments, customer countries, vessel types, product types and project managers, and there are some correlations between these different risky characteristics. These findings are impactful as it is crucial for business to identify possible risky types of projects where the negative cost over-runs are highly likely.Tämä tutkimus tutkii projektin kustannussuunnittelun vaikeuksia globaalissa organisaatiossa. Tutkimus toteutettuun yhteistyössä organisaation kanssa ja tutkimukseen tarvittava tieto kerättiin organisaation tietovarastosta. Projektin kustannussuunnitelman ennustaminen on vaikea tehtävä jossa tarkoituksena on ennustaa tuntemattomia riskejä. Väärät ennusteet aiheuttavat vaikeuksia sekä epäselvyyksiä kun riskit nousevat. Tämän tutkimuksen päätarkoituksena on tutkia ja löytää yhteisiä tekijöitä projekteissa, joissa kustannukset ovat joka ylittyneet positiivisesti tai negatiivisesti alkuperäisestä suunnitelmasta. Näiden perusteella tutkitaan miten voitaisiin paremmin tunnistaa projektien riskitekijöitä, jotta saavutettaisiin parempi projektin kustannussuunnitelman ennuste. Aiempaa tutkimusta samoilla tavoitteilla on jonkin verran, mutta näissä tutkimuksissa on myös todettu kustannussuunnitelmien vaikeuksia. Tässä tutkimuksessa käytettiin sekä laadullista että määrällistä tutkimustapaa. Tutkimuksen data kerättiin organisaation tietovarastosta ja projektien määrä oli noin 2500 vuosilta 2018-2020 asti. Tutkimusta varten rakennettiin multi-dimensionaalinen datamalli, johon rakennettiin regressiomalli ennustamaan uudet kustannussuunnitelmat projekteille. Tutkimuksessa korostettiin tiettyjen riskitekijöiden yhtenäisyyttä tietyissä projekteissa. Näitä olivat esimerksi laivasegmentti, laivatyyppi, asiakasmaa, tuotetyyppi sekä projektipäällikkö. Näiden tekijöiden välillä löytyi jonkin verran korrelaatiota riskitapauksissa. Tämän tutkimuksen tulokset ovat merkittäviä, sillä liiketoiminalle on erityisen tärkeää osata tunnistaa mahdolliset riskitekijät, jotka aiheuttavat negatiivisia kustannuksien ylityksiä

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

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)