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Decision makers spend significant time and effort interpreting information derived from large multidimensional databases; data are usually represented by several dashboard diagrams. The Complexity Design (CoDe) methodology provides a technique modeling graphical reports on data extracted by a data warehouse, where the charts composing the dashboard diagrams are integrated with a visual representation of the logical relationships among them. The generated visualizations (CoDe Graphs) are automatically obtained by connecting dashboard diagrams through graphical conceptual links. After analyzing the state of the art regarding the evaluation of graphical representation comprehensibility, we propose a classification of those evaluation approaches and evaluate the comprehensibility of CoDe Graphs concerning dashboard reports through a controlled experiment, involving 47 participants. Results show that CoDe Graphs reduce participants effort while improving effectiveness and efficiency in comprehension tasks.

Using a data warehouse method, data from several heterogeneous and dispersed operating systems (OLTP) is retrieved, converted, and put into a centralized repository. It is primarily used to process queries and thoroughly analyze data that is important to decision-makers. Therefore, it is crucial to make this data available as soon as possible. Here we have the concept of the Materialize perspective. Data warehouses often store their given data as a collection of materialized perspectives. The most difficult part is deciding which views should be realized and quickly with less expensive functions. This paper presents, Multi-Objective Discrete Genetic Particle Swarm Optimization (DGPSO) based Materialized View Selection. Using DGPSO (discrete genetic operator based particle swarm optimization), the top-k views from a multidimensional lattice are chosen. Among the various objective functions in the proposed method, response costs, management costs, current query processing costs, and past query processing costs. The DGPSO-based mineralization view selection algorithm is able to choose views of higher quality for materialization, as demonstrated by a comparison between it and the basic view selection algorithm based on testing.

Based on the historical data of a commercial blast furnace (BF), the evaluation and prediction models for the BF comprehensive operating status were established by big data mining methods. Firstly, based on the data resources of the data warehouse of BF ironmaking, clean data were obtained by processing the original data with the problem of null values, outlier data, and blowing-down operations data. Then, the AHP_EWM_TPOSIS evaluation model was built with the combined weight of AHP and EWM and the improved TOPSIS algorithm. Finally, the model evaluation results were verified with the actual production situation, and the comprehensive matching rate reached 94.49%, indicating that the model can accurately judge the comprehensive operating status of BF. The evaluation result was the target parameter for building the BF comprehensive operating status prediction model. The results showed that the stacking model achieved better results than the base models in all indicators. The accuracy index of the deviation between the predicted value and the actual value within ±0.05 reached 94.50%, which meets the practical needs of BF production. The evaluation and prediction models provided timely and accurate furnace condition information to the operators in the BF smelting process, which promoted the long-term stable operation of the BF condition.

In the last few decades, precision medicine became a highly weighted subject as the risk of side effects grew with the increasing drug use. The mass of evolving pharmacogenetic data invites to establish a preemptive genotyping approach and the integration of those molecular data into the workflow of an existing hospital information system to improve the safety of drug therapy. The newly introduced software module GraphSAW2-DWHBuilder creates the graph-based data warehouse. For that, molecular data from various sources is assembled in a graph database. Furthermore, an update strategy was developed to ensure that only the latest version of the data is used. To eliminate redundancies and to guarantee efficient querying, a mapper strategy was established. Another new module named GraphSAW2-Check is embedded in an existing drug therapy safety workflow used in hospitals. It contains methods for the pharmacogenetic check, which access the molecular data warehouse of the GraphSAW2-DWHBuilder and execute queries on the graph. These queries contain the drugs and the molecular data of interest and search for connections that represent toxic associations between them. If a connection is found, a warning will be issued to the medical practitioner, who can then reconsider their prescription. The results reveal the added value of molecular data integration to the drug therapy safety. In den letzten Jahrzehnten wurde die Präzisionsmedizin zu einem hoch gewichteten Thema, da das Risiko von Nebenwirkungen mit dem zunehmenden Medikamentenkonsum zunahm. Die stetig zunehmenden pharmakogenetischen Forschungsdaten ermöglichen einen präventiven Ansatz zur Genotypisierung im Krankenhaus und die Integration dieser molekularen Daten in den Medikationscheck eines bestehenden Krankenhausinformationssystems zu etablieren, um die Sicherheit der Arzneimitteltherapie zu verbessern. Das neu eingeführte Softwaremodul GraphSAW2-DWHBuilder erstellt das graphbasierte Data Warehouse. Dazu werden molekulare Daten aus verschiedenen Quellen in einer Graphdatenbank zusammengeführt. Darüber hinaus wurde eine Strategie entwickelt, die die Daten aktuell hält, um sicherzustellen, dass für den pharmakogenetischen Medikationscheck nur die neueste Version der Forschungsdaten verwendet wird. Um Redundanzen zu eliminieren und eine effiziente Abfrage zu gewährleisten, wurde zudem eine Mapper-Strategie beim Erstellen der Graphdatenbank etabliert. Ein weiteres Modul namens GraphSAW2-Check ist als Plugin für einen bestehenden Medikationscheck entwickelt worden, der in Krankenhäusern bereits verwendet wird. Dieser pharmakogenetische Check greift auf das molekulare Data Warehouse des GraphSAW2-DWHBuilder zu und führt Datanbankabfragen auf dem Graphen aus. Diese Abfragen enthalten die Medikamente und molekularen Daten der PatientIn und suchen nach Verbindungen zwischen ihnen, die beispielsweise Toxizität zwischen dem Medikament und der Genvariante repräsentieren. Wenn ein solcher Zusammenhang festgestellt wird, wird dem ärztlichen Fachpersonal eine Warnung angezeigt, mithilfe derer die Entscheidung bezüglich der Verschreibung überdacht werden kann.

Aircraft operations in the terminal area rely heavily on voice communications between pilots and air traffic controllers. This paper proposes a novel aircraft trajectory simulation framework by guiding the trajectory simulation following the voice command from controllers. Bayesian model selection is used for checking pilot compliances to controller commands with observed trajectories. This framework is named as Voice Communication-Augmented Simulation. The goal of the proposed study is to enable accurate trajectory predictions. The framework can act as a computer assistant for controllers to monitor pilot compliances and ensure safe operations. The proposed method is tested and validated with actual trajectory data from Sherlock Data Warehouse. The tests showed that the proposed framework can accurately simulate and monitor the flight level change of aircraft and update the approach procedure.

There is a vast collection of data for consumers due to tremendous development in digital marketing. For their ads or for consumers to validate nearby services which already are upgraded to the dataset systems, consumers are more concerned with the amount of data. Hence there is a void formed between the producer and the client. To fill that void, there is the need for a framework which can facilitate all the needs for query updating of the data. The present systems have some shortcomings by a vast number of information that each time lead to decision tree-based approach. A systematic solution to the automated incorporation of data into a Hadoop distributed file system (HDFS) warehouse (Hadoop file system) includes a data hub server, a generic data charging mechanism and a metadata model. In our model framework, the database would be able to govern the data processing schema. In the future, as a variety of data is archived, the datalake will play a critical role in managing that data. To order to carry out a planned loading function, the setup files immense catalogue move the datahub server together to attach the miscellaneous details dynamically to its schemas.

Social network analysis (SNA) is a process of mapping and analyzing interactions between people in social media, including crime. SNA can be used to determine network structure and relationships between criminal network actors through the social media used. After initial investigation, this study used the hashtag "Hayat Tahrir al-Sham" and the phrase in Arabic to crawl the data. Business intelligence is a concept and method of how we can improve the quality of decision-making in a business based on data-based systems. By using SNA terms such as density, centrality, diameter, and reciprocity. Business intelligence can be built. With the combination of SNA and BI, groups affiliated with these groups can be identified. The research also identified 10 accounts that were considered to play an important role in collaborative communication, the flow of funds, and the dissemination of radical propaganda. By finding these 10 accounts, there are many investigations that can be carried out in accordance with standard operating procedures at the authorities

Nowadays, to implement modern big data architectures, companies opt for different technology stacks, which are either open-source or offered by certain vendors on the market, such as Google, Microsoft, Amazon and others. Several factors influence the selection of aby given stack, whereby the cost of stack usage as well as the human resources factor often appear to be the most important ones. As a cost-efficient alternative, it is possible to use open-source technologies, such as Apache stack, but this also comes with certain implications and compromises. On the other hand, modern big data architectures sometimes include separated storage and compute layers, with a different technology solution used on each layer (or even within the same layer) and the ultimate goal of storing variously structured data and their efficient analysis. In this paper, we have presented an example of a two-layered IT architecture optimized for big data storage and analysis and we have shown the tools and solutions used in the selected three technology stacks (Google, Amazon, Apache) that can be used to implement such an architecture. We have analyzed the properties of individual technology stacks and reviewed on the benefits and challenges of using a specific stack

Background Post-stroke heart rate (HR) and heart rate variability (HRV) changes have been proposed as outcome predictors after stroke. We used data lake-enabled continuous electrocardiograms to assess post-stroke HR and HRV, and to determine the utility of HR and HRV to improve machine learning-based predictions of stroke outcome. Methods In this observational cohort study, we included stroke patients admitted to two stroke units in Berlin, Germany, between October 2020 and December 2021 with final diagnosis of acute ischemic stroke or acute intracranial hemorrhage and collected continuous ECG data through data warehousing. We created circadian profiles of several continuously recorded ECG parameters including HR and HRV parameters. The pre-defined primary outcome was short-term unfavorable functional outcome after stroke indicated through modified Rankin Scale (mRS) score of > 2. Results We included 625 stroke patients, 287 stroke patients remained after matching for age and National Institute of Health Stroke Scale (NIHSS; mean age 74.5 years, 45.6% female, 88.9% ischemic, median NIHSS 5). Both higher HR and nocturnal non-dipping of HR were associated with unfavorable functional outcome (p Conclusions Our data suggest that a lack of circadian HR modulation, specifically nocturnal non-dipping, is associated with short-term unfavorable functional outcome after stroke, and that including HR into machine learning-based prediction models may lead to improved stroke outcome prediction.

Rezultat diplomske naloge bo uporabljen za pregled in optimizacijo sql skript, ki delajo z velikimi podatki, posebaj bo namenjena podjetjem, ki se ukvarjajo s poslovno inteligenco bolj natančno razširitvijo fukncionalnosti Microsoftovih programov Microsoft Dynamics AX in NAV. V okviru procesa pride do obdelave podatkov in procesiranja le teh na na treh glavnih področjih Stage, Datawarehouse in pa analitika. Za procesiranje se uporabljajo sql skripte. Moja naloga bo beležiti čas izvajanja in število vnosov na določen čas za vsako od teh skript. Te podatke bom pripeljal v Power BI, kjer jih bom prikazal po kategorijah. Potrebno bo dobro razumevanje platforme Dynamics in pa potreb podjetja za optimalno poročilo kar se tiče uporabljenih tabel in pa optimalne vizualizacije po kategorijah. Naloga bo znatno pripomogla pri hitrem pregledu zgodovine izvajanja teh skript, saj se bo s tem lažje ugotovilo ali kakšna skripta ne deluje pravilno oziroma ali potrebuje kakšno optimizacijo, prav tako pa se bo dalo videti kje je procesiranje padlo oziroma katere tabele se niso sprocesirale. The result of the thesis will be used to review and optimize SQL scripts that work with big data, especially for companies that deal with business intelligence, more precisely expanding the functionality of Microsoft’s Microsoft Dynamics AX and NAV programs. As part of the process, data is processed and processed only in three main areas: Stage, Datawarehouse and analytics. SQL scripts are used for processing. My job will be to record the execution time and number of entries per time for each of these scripts. I will bring this data into Power BI, where I will display it by category. It will be necessary to have a good understanding of the Dynamics platform and the company’s needs for an optimal report in terms of the tables used and optimal visualization by category. The task will significantly help with a quick review of the execution history of these scripts, as this will make it easier to determine whether a script is not working correctly or whether it needs some optimization, and it will also be possible to see where the processing failed or which tables were not processed.

Internship Report presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management Closer is a Data Science company specialized in Business Intelligence, Advanced Analytics and Artificial Intelligence. Their purpose is to be at the forefront of innovation, and my intention in this internship was to contribute with my knowledge, work, and dedication to improve the data models that already exist and to create new ones that fills the needs of the company. Closer received feedback from several users that some of the dashboard visual elements present in Evalyze took too long to load and my goal was to achieve the highest performance by reducing errors, non-conformities and provide standardized responses to ensure a superior level of service, thus, controlling the efficiency more precisely. Throughout the internship I used SQL and Power BI on a daily basis to improve models and create new ones. I also created several dashboards in Power BI in accordance with the team's needs. In this essay, it will be explained the several techniques that were used to improve the existent models in Closer, more specifically one that lacked the most in efficiency, taking too much time to execute basic tasks, therefore required improvement as well as innovative upgrades. Also, I have built new models that Closer required and wished to have, allowing to create new dashboards to have clear visualizations of its business. As for the data sources used for the dashboards, it was stored on Data Warehouse, particularly in tables where data regarding Evalyze operations and Azure SQL on prem database. Azure SQL Database is an always-up-to-date, fully managed relational database service built for the cloud. Regarding the modeling of data, Power BI is utilized and make use of a star model. Finally, at the visualization level, reports were developed based on the model previously developed in Power BI (figures 27 and 28 in annexes). The data I worked with was about what was done in Evalyze, Closer's software. The data refer to the number of open activities, activities handled, activity resolution times and average productive times. This data is registered in the database and in turn goes to the data warehouse. Evalyze software is a tool that focuses on real-time operations management, improvement, assessment, and analysis. One of the features present in this software is the availability of generic dashboards that allow the customer to follow the evolution of the main business KPI's. To put it briefly, Evalyze is a web tool that automatically and intelligently distributes activities, increasing operations productivity. The process is done through Artificial Intelligence algorithms based on SLA’s, AHT, customers’ score, skills and operator’s availability, ensuring that the distribution is made activity-to-activity and in real time, according to what is the highest priority for the company, without the possibility of choice by operators. Built-in Artificial Intelligence (Machine Learning) enables the use of analytical/mathematical methods to automate end-to-end processes, incorporate business rules, and learn and evolve over time.

Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence Nowadays, companies are handling and managing data in a way that they weren’t ten years ago. The data deluge is, as a mere consequence of that, the constant day-to-day challenge for them - having to create agile and scalable data solutions to tackle this reality. The main trigger of this project was to support the decision-making process of a customer-centered marketing team (called Customer Voice) in the Company X by developing a complete, holistic Business Intelligence solution that goes all the way from ETL processes to data visualizations based on that team’s business needs. Having this context into consideration, the focus of the internship was to make use of BI, ETL techniques to migrate their data stored in spreadsheets — where they performed data analysis — and shift the way they see the data into a more dynamic, sophisticated, and suitable way in order to help them make data-driven strategic decisions. To ensure that there was credibility throughout the development of this project and its subsequent solution, it was necessary to make an exhaustive literature review to help me frame this project in a more realistic and logical way. That being said, this report made use of scientific literature that explained the evolution of the ETL workflows, tools, and limitations across different time periods and generations, how it was transformed from manual to real-time data tasks together with data warehouses, the importance of data quality and, finally, the relevance of ETL processes optimization and new ways of approaching data integrations by using modern, cloud architectures.

Diplomsko delo naslavlja problem izbora primerne tehnologije podatkovnega skladišča za različne tipe organizacij. Namen diplomskega dela je predstaviti poglavitne tehnologije na trgu programskih rešitev podatkovnega skladiščenja ter nekatere od ključnih dejavnikov pri izbiri le-teh. Rezultati diplomskega dela bodo koristili predvsem podjetjem, katera so v procesu izbora programskih rešitev oziroma tehnologij za podatkovno skladiščenje. The thesis addresses the problem of selecting the suitable data warehouse technology for different types of organizations. The purpose of the thesis is to present the main technologies in the market of data storage software solutions and some of the key factors in choosing them. The results of the thesis will be used mainly by companies in the process of choosing software solutions for data warehousing.

Mestrado Bolonha em Data Analytics for Business In the last few years, data volume has grown tremendously, and so has the importance of its analysis, given the potential knowledge emerging from the information it provides. Business Intelligence (BI) projects are getting a larger portion of enterprises’ IT budgets, as they allow the improvement of decision-making and enhanced value creation (Farrokhi & Pokorádi, 2012). This Master’s Final Work describes the internship at DNS.PT Association, where a Data Warehouse and a BI application were developed for this organization. DNS.PT Association, referred to as .PT, is a non-profit association responsible for Internet domain management, maintenance, and registration under the TLD .pt, that is, the top-level domain corresponding to Portugal. This report details, along with a theoretical background, the development of a Data Warehouse, with all its steps, including the ETL – Extract, Transform and Load – process. Subsequently, a BI visualization tool is used for the creation of dashboards to provide a better analysis of the data. Given the importance of documentation in any IS project, a brief overview of the Data Management and Governance Model is presented. Nos últimos anos, o volume de dados tem aumentado substancialmente, assim como a importância da sua análise, dado o conhecimento que surge da informação que proporciona. As empresas têm aplicado cada vez mais do seu orçamento em TI, em projetos de Business Intelligence (BI), sendo que permitem uma melhor tomada de decisão e maior criação de valor (Farrokhi & Pokorádi, 2012). Este Trabalho Final de Mestrado descreve o estágio na Associação DNS.PT, onde uma Data Warehouse e uma aplicação em BI foram desenvolvidos para a organização. A Associação DNS.PT, abreviadamente designada por .PT, é uma associação sem fins lucrativos responsável pela gestão, manutenção e registo de domínios de Internet sob o TLD .pt, ou seja, o domínio de topo correspondente a Portugal. Desta forma, este relatório explica, acompanhado de um contexto teórico, a implementação de uma Data Warehouse e o seu processo de ETL – Extract, Transform and Load. Para além disso, uma ferramenta de visualização é utilizada para a criação de dashboards, de forma a obter uma melhorar análise dos dados. Por fim, devido à grande importância de documentação em qualquer projeto de SI é apresentada uma breve descrição do Modelo de Gestão e Governação de Dados. info:eu-repo/semantics/publishedVersion

Fast access of data from Data Warehouse (DW) is a need for today’s Business Intelligence (BI). In the era of Big Data, the cache is regarded as one of the most effective techniques to improve the performance of accessing data. DW has been widely used by several organizations to manage data and use it for Decision Support System (DSS). Many methods have been used to optimize the performance of fetching data from DW. Query cache method is one of those methods that play an effective role in optimization. The proposed work is based on a cache-based mechanism that helps DW in two aspects: the first one is to reduce the execution time by directly accessing records from cache memory, and the second is to save cache memory space by eliminating non-frequent data. Our target is to fill the cache memory with the most used data. To achieve this goal aging-based Least Frequently Used (LFU) algorithm is used by considering the size and frequency of data simultaneously. The priority and expiry age of the data in the cache memory is managed by dealing with both the size and frequency of data. LFU sets priorities and counts the age of data placed in cache memory. The entry with the lowest age count and priority is eliminated first from the cache block. Ultimately, the proposed cache mechanism efficiently utilized cache memory and fills a large performance gap between the main DW and the business user query.

UD. Makmur Sejahtera sebagai salah satu distributor terbesar untuk barang kebutuhan sehari-hari di Manokwari Papua, memiliki data-data transaksi penjualan untuk setiap kategori barang dan jenis barang. Data-data ini masih tersimpan secara fisik dalam bentuk nota serta belum didigitalkan untuk dapat dimanfaatkan secara maksimal untuk membantu UD. Makmur Sejahtera meningkatkan penjualan. Penelitian ini memiliki ide dasar pemanfaatan data digital transaksi penjualan untuk mengetahui kategori barang mana yang memiliki penjualan terbanyak dalam kurun waktu tiga bulan (Juli 2020 hingga September 2020) melalui proses Extraction, Transformation, Loading (ETL) berbasis Pentaho Data Integration, untuk kemudian disimpan dalam bentuk data multi dimensi, dikategorikan, dan divisualisasikan menggunakan Tableau. Hasil pengujian menunjukkan bahwa komoditas beras merupakan kategori barang dengan penjualan terbanyak pada kurun waktu tiga bulan serta implementasi Data Warehouse sangat membantu UD. Makmur Sejahtera di dalam mencapai tujuan bisnis usahanya.

L’obiettivo di questo elaborato è descrivere le scelte progettuali che hanno portato nel 2020 alla ristrutturazione della reportistica dedicata al Bilancio INFN presente sull’impianto di Business Intelligence (BI) INFN. Nello specifico vengono presentate le motivazioni della dismissione dei vecchi report multidimensionali basati su Viste OLAP in favore degli attuali report JRXML. Vengono anche presentati alcuni aspetti peculiari delle soluzioni implementate sul data warehouse che offre funzionalità di backend per l’impianto volte a garantire il giusto layer autorizzativo nell’accesso ai dati ed alle risorse.

Pri načrtovanju podatkovnega skladišča se razvijalci pogosto srečajo z zahtevo po ohranjanju zgodovine sprememb. Za izpolnitev zahteve izberejo enega od osmih tipov počasi spreminjajočih dimenzij. Primernost tipa počasi spreminjajočih dimenzij se določi glede na potrebe poslovnih uporabnikov in števila podatkov, ki jih bodo hranili. V sklopu diplomske naloge bomo izdelali rešitev, ki bo uporabila eno od tipov počasi spreminjajočih dimenzij. Glavni del rešitve, ki bo reševal problem hranjenja zgodovine, bo razvit v sistemu Exasol s pomočjo skript, napisanih v programskem jeziku Lua. V rešitvi smo uporabili tip 7 počasi spreminjajočih dimenzij in s tem izpolnili zahtevo po hranjenju zgodovine sprememb. Z uporabo tega tipa lahko iz tabele dejstev neposredno dostopamo do podatkov, ki so bili veljavni ob vnosu zapisa, ali pa do podatkov, ki so veljavni sedaj. When planning a data warehouse, developers are often faced with the requirement to maintain a history of changes. To meet the requirements, they choose one of eight slowly changing dimensions types. The type of slowly changing dimensions that suits the most business requirements is determined by the needs of business users and the amount of data they will store. As part of the thesis, we will create a solution that will use one of the types of slowly changing dimensions. The main part of the solution, which will solve the problem of preserving history, will be developed in the Exasol system with the help of scripts written in the Lua programming language. We used the slowly changing dimensions type 7 in the solution, thus fulfilling the requirement to keep a history of changes. Using this type, we can directly access the data that was valid at the time of entering the record or the data that is valid now from the fact table.

[Resumen]: Con este proyecto, elaborado en colaboración con el Servizo Galego de Saúde (SERGAS), se busca realizar un diseño de un almacén de datos de un entorno sanitario para facilitar la exploración y análisis de información para la identificación de puntos de mejora en los procesos de atención hospitalaria. Para poder llevar a cabo este proyecto, es necesario seguir una serie de pasos: El primer paso consiste en realizar un estudio acerca de las necesidades que tiene la organización y tras una serie de reuniones, se acuerdan los datos reales y seudonimizados que aportará dicha organización y los análisis o estudios que se realizarán. Tras esto se procederá, con la elaboración de la diferentes tareas de diseño y creación del Data Warehouse, con la posterior realización del proceso ETL. Finalmente, se utilizarán herramientas de visualización para poder realizar diferentes análisis que permitan estudiar de manera sencilla la información extraída de los datos. [Abstract]: This project, developed in collaboration with Servizo Galego de Saúde (SERGAS), aims to design a data warehouse in a healthcare environment to facilitate the exploration and analysis of information for the identification of improvement points in hospital care processes. In order to carry out this project, it is necessary to follow a series of steps: The first step is to conduct a study about the needs of the organization and after a series of meetings, the real and pseudonymized data to be provided by the organization and the analyses or studies to be performed are agreed upon. After this, we will proceed with the elaboration of the different design and creation tasks of the Data Warehouse, with the subsequent realization of the ETL process. Finally, visualization tools will be used to perform different analyses that allow the information extracted from the data to be studied in a simple manner. Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/2023

Dissertação de mestrado em Engenharia e Gestão Industrial, A tecnologia e o poder da informação orientam, cada vez mais, uma sociedade que por um lado, se encontra em mudanças constantes e que, por outro lado, cresce de forma exponencial devido aos avanços tecnológicos. Esta era é marcada também pelo crescimento da quantidade de dados que uma organização armazena e convertê-los em informações claras e concretas é uma chave fundamental para o sucesso de uma empresa. Assim, estes fatores levam as organizações a adotar estratégias que lhes permitam extrair dos dados, informação relevante que suporte a tomada de decisão para o sucesso do negócio. A startup tecnológica Infraspeak detém e estima deter um aumento do número de clientes e, por isso, um grande volume de dados. Cada vez que a Infraspeak adquire um novo cliente, este passa por um processo de onboarding, que pretende melhorar a sua experiência na adoção do produto. Como tal, o principal objetivo da presente dissertação reside no desenvolvimento de um sistema de apoio à tomada de decisão, mais concretamente na conceção de um sistema de Business Intelligence, que inclui um Data Warehouse para o repositório dos dados. Esta solução aplicada à área de Customer Success da Infraspeak pretende agregar os dados relativos aos clientes e proporcionar ferramentas orientadas à tomada de decisão. Através da metodologia estudo de caso, foi necessário conhecer em profundidade o contexto da empresa, para selecionar e descrever os dados a serem utilizados. Após a recolha desta informação, os dados foram tratados, foi realizado uma análise estatística univariada, implementou-se o Data Warehouse, seguida de uma dashboard e realizou-se uma análise dos dados obtidos. Através dos dados obtidos, conclui-se que a Infraspeak tem uma média de duração do onboarding de 60,41 dias, mas esta média foi variando e variáveis como o segmento da empresa e o tipo de atividades de manutenção que executam, o mercado e/ou país a que pertencem podem influenciar o processo de onboarding. A solução implementada permitiu um acesso simples e eficiente dos dados para a decisão., Technology and the power of information increasingly guide a society that on the one hand is in constant change and on the other hand is growing exponentially due to technological advances. This era is also marked by the growth in the amount of data that an organisation stores and converting it into clear and concrete information is a fundamental key to a company's success. Thus, these factors lead organisations to adopt strategies that allow them to extract from data, relevant information that supports decision making for business success. The technological startup Infraspeak holds and estimates to hold an increase in the number of customers and, therefore, a large volume of data. Each time Infraspeak acquires a new customer, this one goes through an onboarding process, which aims to improve their experience in adopting the product. As such, the main objective of this dissertation lies in the development of a decision support system, more specifically in the design of a Business Intelligence system, which includes a Data Warehouse for data repository. This solution applied to Infraspeak's Customer Success area intends to aggregate customer data and provide decision-oriented tools. Through the case study methodology, it was necessary to know in depth the context of the company in order to select and describe the data to be used. After collecting this information, the data was processed, a univariate statistical analysis was carried out, the Data Warehouse was implemented, followed by a dashboard and an analysis of the data obtained was carried out. Through the data obtained, it was concluded that Infraspeak has an average onboarding duration of 60.41 days, but this average varied and variables such as the company's segment and the type of maintenance activities they perform, the market and/or country they belong to can influence the onboarding process. The solution implemented allowed simple and efficient access to data for the decision.

Next-generation sequencing (NGS), also called massively parallel sequencing, is a high-throughput technology that allows the determination of the nucleotide sequences of entire or specific regions of the genome. The application of this technology in a clinical environment enables personalized diagnostics for patients, for instance, allowing the identification of variants that might cause a disease. In this sense, clinical diagnostic laboratories are responsible for providing a robust and appropriate workflow that enables the obtention of genomic information ready to be interpreted by a clinician. The Molecular Biology CORE Laboratory in the Hospital Clinic de Barcelona performs hundreds of analyses each year, providing service to several diagnostic laboratories. Be sides, with the increasing number of NGS applications in clinical diagnostics, the number of analyses is expected to keep growing in the following years. Quality data is generated from different sources in each of these NGS analyses, including laboratory procedures, DNA sequencing, and bioinformatics analyses. These quality data must be carefully evaluated and validated to ensure the results' reliability. Moreover, the accumulation of quality data from each analysis can be used to assess the performance of the laboratory and to identify potential sources of technical artefacts that might lower the quality of the experiments. Hence, a database is needed to store and manage quality data for easy accessibility and analysis over time. In this thesis, we aim to develop a data warehouse to analyze and monitor NGS quality data coming from different data sources. To do that, we will perform the following steps: 1) design a multidimensional data model to ensure that data will be efficiently stored; 2) data extraction from different sources; 3) database loading; 4) design a visualization tool to enable descriptive analyses of the quality data. The designed tool will allow the historical exploration of quality parameters, as well as the evaluation of an experiment's quality metrics compared to the rest. With this tool, we are enabling the identification of areas of improvement by discovering sources of variation that might affect the quality of clinical NGS data.

This chapter covers three of the most exciting and demanding classes of cloud applications, Big Data, data streaming, and mobile cloud computing. Big Data and data streaming applications require low-latency, scalability, versatility, and a high degree of fault-tolerance. Achieving these qualities at scale is extremely challenging.

Nutrients are important to promote plant growth and nutrient deficiency is the primary factor limiting crop production. However, excess fertilisers can also have a negative impact on crop quality and yield, cause an increase in pollution and decrease producer profit. Hence, determining the suitable quantities of fertiliser for every crop is very useful. Currently, the agricultural systems with internet of things make very large data volumes. Exploiting agricultural Big Data will help to extract valuable information. However, designing and implementing a large scale agricultural data warehouse are very challenging. The data warehouse is a key module to build a smart crop system to make proficient agronomy recommendations. In our paper, an electronic agricultural record (EAR) is proposed to integrate many separate datasets into a unified dataset. Then, to store and manage the agricultural Big Data, we built an agricultural data warehouse based on Hive and Elasticsearch. Finally, we applied some statistical methods based on our data warehouse to extract fertiliser information such as a case study. These statistical methods propose the recommended quantities of fertiliser components across a wide range of environmental and crop management conditions, such as nitrogen (N), phosphorus (P) and potassium (K) for the top ten most popular crops in EU.

Data warehouses have been present in the business environment for several decades, providing businesses with deeper insight into their business data through which they gain competitive advantage and decision making support. However, the integrated characteristics of data warehouses can be a key factor in improving process automatization through the integration of various heterogeneous sources, speeding up certain processes, and providing process integration in the organization without impacting the existing infrastructure. In this paper, an analytical system based on a data warehouse is presented which integrates data sources in a high education institution, such as internal operational applications and structured and semi-structured files, and incorporates new data sources such as websites and their interfaces. By introducing the integration of the data sources, processes that share the same data scattered across multiple files or need multiple manual entries can use a single source of truth, while also having the possibility of automatic integration of open- access data. In the paper, the necessary processes are identified, and their respective data marts are proposed, while also providing proof-of- concept for several chosen processes.

Dissertação de mestrado em Engenharia de Sistemas, Face às mudanças, as organizações procuram atender às necessidades de um mercado cada vez mais ágil, ativo e globalizado, estimulado pela denominada de Quarta Revolução Industrial (Revolução 4.0) que, em conjunto com a inovação das tecnologias, conduzem à necessidade de reformular o modo de como os processos das diferentes áreas são geridos e integrados. As organizações que procuram sobreviver a esta revolução ambicionam por integrar os conceitos de flexibilidade, adaptabilidade, performance e segurança. A solução para esta questão pode estar envolvida com os processos de tomada de decisão efetuados na empresa, implicitamente, através da não geração de certa informação considerada irrelevante. Posto isto, o conceito de Business Intelligence, os seus diversos métodos e ferramentas, aparecem como um modo de otimizar e simplificar o processo de tomada de decisão, desde a extração de dados até à divulgação da informação, possibilitando encontrar soluções com maior nível de qualidade e eficiência. Portanto, para este projeto, foi utilizada de uma ferramenta de BI, Microsoft Power BI, para auxiliar no processo de otimização e atualização dos Key Performance Indicators do departamento de produção da AMF – Safety Shoes. Neste projeto, são abordados os conceitos relevantes associados à Gestão da Produção, Business Intelligence e Microsoft Power BI. Também será possível ver todos o processo de construção de um Data Warehouse, desde a fase de levantamento e análises de requisitos até à sua fase de implementação, sendo, por último, utilizado na construção de dashboards em PowerBI de modo a gerar informação mais consistente e integrada que auxiliará no processo de tomada e decisão. O sistema de BI construído consegue satisfazer os requisitos impostos pelos utilizadores, na maioria. A sua automatização foi conseguida encontrando um equilíbrio entre o que poderia ser automático e as exceções que vão ocorrendo na linha de produção., Faced with the experiences of changes by organizations that meet the needs of an increasingly agile, active and globalized market, stimulated by the Fourth Industrial Revolution (Revolution 4.0) which, along with the innovation of technologies, lead to the need to reformulate the way of processes from different areas are optimized and integrated. Organizations that want to survive this revolution aim to integrate the concepts of flexibility, adaptability, performance and security. The solution for this company can be generated with thoughtful decision-making processes, considered not to be of certain irrelevant implicit information. That said, the concept of Business Intelligence, its various methods and tools, appear as a way of optimizing and simplifying the data collection process until the greater dissemination of information, being able to find solutions with a level of quality and efficiency. Therefore, for this project, a BI design tool, Microsoft Power BI, was used to assist in the process of optimizing and updating the Key Performance Indicators of the production department of AMF – Safety Shoes. In this project, the concepts associated with Management, Business Intelligence and Microsoft Power BI are defined. It will also be the entire process of building a data warehouse, from the possible survey phase and see accessories to its implementation phase, being, finally, used in the construction of dashboards in PowerBI to generate more consistent and integrated information that assist in the decision-making and decision-making process. The BI system built manages to satisfy the requirements imposed by the users, in the majority. Its automation was achieved by finding a balance between what could be automatic and the exceptions that occur on the production line.

The large proliferation of the BPM discipline has considerably propelled companies' information systems which become ubiquitous. Consequently, a huge volume of data is generated during BP execution and stored in adequate IT systems. However, such data are often heterogeneous and the problem of their integration is posed with acuity in order to allow data analysis and to coordinate companies' activities. In fact, different techniques and approaches have been suggested in the research literature to tackle such issue. In the business intelligence specific context, this challenge is addressed by deploying the conventional ETL software tools allowing to build an unique container supporing various data and which is perceived as a data warehouse. Nevertheless, current distributed information systems are spanning enterprises boundaries and deal with a large variety of data sources having various formats and produced in a continuous manner. Thus, actual ETL tools seems unsuitable to handle massive data and they remain limited for facing to issues imposed by related to business processes execution data stored in log files. In this paper we propose an enhancement of the structure and functionalities of standard ETL tools in order to handle heterogeneous data integration generated by business processes execution. The improved system, named OLE-ST, constitutes a fundamental enrichment of the existing ETL mechanism. The proposed approach has been implemented in a software tool that ensures best performances for exploiting the target data warehouse to be built.

In an ever-growing technological world, there is a lot of value in being able to quickly analyze data and make decisions in order to maintain a successful business. Imaginary Cloud is a growing Software Development and UX/UI company, and with such growth, there is an increased amount of data to be stored and processed. Therefore, it is of great importance that each department can easily access their respective data, to increase the speed of their decision-making process. In order to better analyze its performance, the company intends on developing a dash-board system for internal use that allows the visualization of relevant data dynamically, in-cluding key performance indicators. The purpose of this project will be to work alongside Imaginary Cloud to develop a tool that extracts information from multiple sources as efficiently as possible and create a central-ized database to hold the extracted data, expected to be ever expanding. The database must be optimized for the retrieval of data in real data, so that it can be queried by the dashboard system (not to be developed in this project) at the user’s request. In the end, the existence of this tool should have a major impact on how easy each de-partment can access and interpret data, allowing for more informed and faster decision mak-ing, and increasing the overall company’s efficiency. Num mundo em constante desenvolvimento tecnológico, é muito importante ser capaz de analisar e tomar decisões rapidamente para que se consiga manter um negócio bem-sucedido. A Imaginary Cloud é uma empresa de desenvolvimento de Software e Design de UX/UI em crescimento, e com o seu aumento de escala, aumenta também a quantidade de dados a serem armazenados e processados. Como tal, é de alta importância que cada departamento consiga facilmente aceder aos seus respetivos dados, para que se aumente a velocidade do processo de tomada de decisões. Para melhorar a análise do seu desempenho, a empresa pretende desenvolver um sis-tema de dashboards para uso interno que permita visualizar dados relevantes de forma dinâ-mica, incluindo indicadores chave de desempenho. O objetivo deste projeto é colaborar com a Imaginary Cloud para desenvolver uma fer-ramenta que extraia informação de múltiplas fontes e criar uma base de dados centralizada que conterá os dados extraídos, que se espera que aumentem continuamente. A base de dados deve estar otimizada para a requisição de dados em tempo real, para que possa ser consultada pelo sistema de dashboards (que não será desenvolvido neste projeto) a pedido do utilizador. No final, a existência desta ferramenta deve ter um grande impacto na facilidade com que cada departamento consegue aceder e interpretar os seus dados, permitindo uma tomada de decisões mais rápida e informada, melhorando a eficiência geral da empresa.

Objective The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Materials and Methods Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020 to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering, and machine learning models. Results During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health records (EHRs) from the hospital data warehouse improved median relative error at 7 and 14 days by 10.9% and 19.8%, respectively. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection. Discussion Forecast model showed overall good performance both at 7 and 14 days which were improved by the addition of the data from Bordeaux Hospital data warehouse. Conclusions The development of hospital data warehouse might help to get more specific and faster information than traditional surveillance system, which in turn will help to improve epidemic forecasting at a larger and finer scale.

Schemaless databases offer a large storage capacity while guaranteeing high performance in data processing. Unlike relational databases, which are rigid and have shown their limitations in managing large amounts of data. However, the absence of a well-defined schema and structure in not only SQL (NoSQL) databases makes the use of data for decision analysis purposes even more complex and difficult. In this paper, we propose an original approach to build a document-oriented data warehouse from unstructured data. The new approach follows a hybrid paradigm that combines data analysis and user requirements analysis. The first data-driven step exploits the fast and distributed processing of the spark engine to generate a general schema for each collection in the database. The second requirement-driven step consists of analyzing the semantics of the decisional requirements expressed in natural language and mapping them to the schemas of the collections. At the end of the process, a decisional schema is generated in JavaScript object notation (JSON) format and the data loading with the necessary transformations is performed.

Objective Obtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution’s approach for matching investigators with tools and services for obtaining electronic patient data. Materials and Methods Supporting the spectrum of studies from populations to individuals, ARCH delivers a breadth of scientific functions—including but not limited to cohort discovery, electronic data capture, and multi-institutional data sharing—that manifest in specific systems—such as i2b2, REDCap, and PCORnet. Through a consultative process, ARCH staff align investigators with tools with respect to study design, data sources, and cost. Although most ARCH services are available free of charge, advanced engagements require fee for service. Results Since 2016 at Weill Cornell Medicine, ARCH has supported over 1200 unique investigators through more than 4177 consultations. Notably, ARCH infrastructure enabled critical coronavirus disease 2019 response activities for research and patient care. Discussion ARCH has provided a technical, regulatory, financial, and educational framework to support the biomedical research enterprise with electronic patient data. Collaboration among informaticians, biostatisticians, and clinicians has been critical to rapid generation and analysis of EHR data. Conclusion A suite of tools and services, ARCH helps match investigators with informatics systems to reduce time to science. ARCH has facilitated research at Weill Cornell Medicine and may provide a model for informatics and research leaders to support scientists elsewhere.

Social media data become an integral part in the business data and should be integrated into the decisional process for better decision making based on information which reflects better the true situation of business in any field. However, social media data are unstructured and generated in very high frequency which exceeds the capacity of the data warehouse. In this work, we propose to extend the data warehousing process with a staging area which heart is a large scale system implementing an information extraction process using Storm and Hadoop frameworks to better manage their volume and frequency. Concerning structured information extraction, mainly events, we combine a set of techniques from NLP, linguistic rules and machine learning to succeed the task. Finally, we propose the adequate data warehouse conceptual model for events modeling and integration with enterprise data warehouse using an intermediate table called Bridge table. For application and experiments, we focus on drug abuse events extraction from Twitter data and their modeling into the Event Data Warehouse.

Currently, COVID-19 pandemic is the major cause of disease burden globally. So, there is a need for an urgent solution to fight against this pandemic. Internet of Things (IoT) has the ability of data transmission without human interaction. This technology enables devices to connect in the hospitals and other planned locations to combat this situation. This article provides a road map by highlighting the IoT applications that can help to control it. This study also proposes a real-time identification and monitoring of COVID-19 patients. The proposed framework consists of four components using the cloud architecture: 1) data collection of disease symptoms (using IoT-based devices); 2) health center or quarantine center (data collected using IoT devices); 3) data warehouse (analysis using machine learning models); and 4) health professionals (provide treatment). To predict the severity level of COVID-19 patients on the basis of IoT-based real-time data, we experimented with five machine learning models. The results reveal that random forest outperformed among all other models. IoT applications will help management, health professionals, and patients to investigate the symptoms of contagious disease and manage COVID-19 +ve patients worldwide.

These days it is essential to maintain the information quality of data warehouse which is used by managers at different levels to take business decisions in organizations. Also, the information quality is assessed by its data models (requirements, conceptual, logical and physical). Various authors have proposed different metrics which were validated formally and empirically to assess the quality of its respective data models. However, no formal and empirical investigation of requirements completeness metrics was witnessed in the literature. Therefore, in this paper, we thoroughly validate the completeness metrics formally and empirically to evaluate the requirements data model quality. As a preliminary step, formal validation using Briand’s framework is carried out on completeness metrics which proves that out of ten metrics, five metrics are size measure, two metrics are cohesion measure, another two metrics are complexity measure and rest one metric is coupling measure. Further, empirical validation includes correlation analysis to ascertain whether completeness metrics are correlated with understandability of requirements schemas. The results illustrate that the eight metrics have positive and strong significant correlation with understandability of requirements schemas. Moreover, linear regression is employed in this study to evaluate the model quality in an objective manner by predicting the understandability of requirements schemas using requirements engineering approach. On the basis of linear regression results, except two metrics, all other eight metrics can build the accurate requirements model.

The purpose of the study is to develop a system for generating management reporting on the production activities of an energy company on the SAP Business Objects Platform. To build a reporting system, the following tasks has been set and solved: the conceptual and datalogical models of the data warehouse were created, data areas have been selected from the general data model, the data warehouse has been designed and developed, universes for report groups have been created, a mechanism for integrating data with the data warehouse has been implemented.The paper analyses the information and technological infrastructure of an energy company as well as formulates the basic requirements for the system being created for generating reporting. Two main subsystems have been designed: data storage and integration. The process of implementing the designed subsystems in physical form has been described using the appropriate software products: SAP Hana, SAP Universe Designer, SAP Data Services, SAP Business Intelligence.Thanks to the configured system through the corporate data bus, a relatively simple data integration mechanism became possible. In its information and technological architecture, the company managed to simulate an acceptable data warehouse model and set up the appropriate data flows. A complex data warehouse model has been implemented, and a convenient platform for further data processing has been provided. A clear data integration scheme is configured using SAP Data Services, with the ability to scale and configure the data loading schedule. The developed system has been put into operation and is used by employees to make management decisions within the framework of their professional activities.

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Methods: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

Purpose An analysis to determine the frequency of medication administration timing variances for specific therapeutic classes of high-risk medications using data extracted from a health-system clinical data warehouse (CDW) is presented. Methods This multicenter retrospective, observational analysis of medication administration data from 14 hospitals over 1 year was conducted using a large enterprise health-system CDW. The primary objective was to assess medication administration timing variance for focused therapeutic classes using medication orders and electronic medication administration records data extracted from the electronic health record (EHR). Administration timing variance patterns between standard hospital staffing shifts, within therapeutic drug classes, and for as-needed (PRN) medications were also studied. To assess medication administration timing variance, calculated variables were created for time intervals of 30-59, 60-120, and greater than 120 minutes. Scheduled medications were assessed for delayed administration and PRN medications for early administration. Results A total of 5,690,770 medication administrations (3,418,275 scheduled and 2,272,495 PRN) were included in the normalized data set. Scheduled medications were frequently subject to delays of ≥60 minutes (15% of administrations, n = 275,257) when scheduled for administration between 9-10 AM and between 9-10 PM. By therapeutic drug class, scheduled administrations of insulins, heparin products, and platelet aggregation inhibitors were the most commonly delayed. For PRN medications, medications in the anticoagulant and antiplatelet agent class (most commonly heparin flushes and line-management preparations) were most likely to be administered early, defined as more than 60 minutes from the scheduled time of first administration. Conclusion The findings of this study assist in understanding patterns of delayed medication administration. Medication class, time of day of scheduled administration, and frequency were factors that influenced medication administration timing variance.

Submitted by Aline Silva (alineslv@ufba.br) on 2022-05-26T20:04:50Z No. of bitstreams: 1 Artigo - Gillian L. Lima.pdf: 364716 bytes, checksum: 56e9595730da19c0f68ff09d791c8022 (MD5) Approved for entry into archive by Urania Araujo (urania@ufba.br) on 2022-06-02T19:45:40Z (GMT) No. of bitstreams: 1 Artigo - Gillian L. Lima.pdf: 364716 bytes, checksum: 56e9595730da19c0f68ff09d791c8022 (MD5) Made available in DSpace on 2022-06-02T19:45:40Z (GMT). No. of bitstreams: 1 Artigo - Gillian L. Lima.pdf: 364716 bytes, checksum: 56e9595730da19c0f68ff09d791c8022 (MD5) Previous issue date: 2021-10-08 Objetivo: realizar análise bibliométrica sobre as aplicações da ciência de dados no âmbito das organizações hospitalares. Método: por meio de pesquisa na base de dados Web of Science, verificou-se a existência de termos relacionados à ciência de dados, tais como “big data”, “data analytics”, “businesss intelligence”, “data mining”, “data warehouse”, “text mining” e “data science", relacionando-os a hospitais. A análise de dados pautou-se na técnica de análise de redes sociais. O período considerado foi de 2015 a 2019 . Resultado: “machine learning” e “electronic health records” despontam como assuntos relevantes. As interações mais expressivas refletem a inclinação da informática médica em assuntos relacionados à tomada de decisão, sistemas de informação para hospitais e unidade de cuidados intensivos. Sobre os campos científicos, nota-se a predominância esperada da área de saúde e dos domínios pertencentes ou fronteiriços à tecnologia. No mais, vê-se que a grande variedade de áreas encontradas acusa a natureza multidisciplinar do assunto, inclusive com importante participação da Ciência da Informação (CI). Em relação à geografia do conhecimento, observa-se um razoável grau de descentralização, havendo produções representativas na América do Norte, Europa e Ásia. Quanto aos veículos de publicação, destaque para os Studies in Health Technology and Informatics, que compreendem uma série de publicações. Os dois periódicos mais representativos da lista, integram, respectivamente, os grupos Springer Nature e Elsevier, grandes players do mercado editorial científico. Conclusões: por fim, evidencia-se a multidisciplinaridade existente em torno do assunto estudado e a relevância da tecnologia para o progresso das organizações hospitalares. Objective: to carry out the bibliometric analysis on the applications of Data Science in the context of hospital associations. Methods: Through research in the Web of Science database, it was verified the existence of terms related to Data Science, such as, big data, data analysis, businesss intelligence, data mining, data warehouse, text mining and data science, relating them to hospitals. Data analysis was based on the social network analysis technique. The period considered was from 2015 to 2019. Results: Machine learning and electronic health records emerge as relevant issues. The most expressive interactions reflect the inclination of Medical Informatics in matters related to decision making, information systems for hospitals and intensive care units. Regarding the fields, it is noted the expected predominance of the Health area and of the domains belonging or bordering on Technology. In addition, it can be seen that the wide variety of areas found accuses the interdisciplinary nature of the subject, including, with an important participation of Information Science. Regarding the geography of knowledge, there is a reasonable degree of decentralization, with representative productions in North America, Europe and Asia. As for the publication vehicles, emphasis is given to Studies in Informatics and Health Technology, which comprise a series of publications. The two most representative journals on the list are, respectively, members of the Springer Nature and Elsevier groups, major players in the scientific publishing market. Conclusions: Finally, there is evidence of the multidisciplinarity around the subject studied and the technology company for the progress of hospital associations.

Data warehouses (DWs) evolve in both their content and schema due to changes of user requirements, business processes, or external sources to name a few. Although multiple approaches using temporal and/or multiversion DWs have been proposed to handle these changes, an efficient solution for this problem is still lacking. The authors' approach is to separate concerns and use temporal DWs to deal with content changes, and multiversion DWs to deal with schema changes. To address the former, previously, they have proposed a temporal multidimensional (MD) model. In this paper, they propose a multiversion MD model for schema evolution to tackle the latter problem. The two models complement each other and allow managing both content and schema evolution. In this paper, the semantics of schema modification operators (SMOs) to derive various schema versions are given. It is also shown how online analytical processing (OLAP) operations like roll-up work on the model. Finally, the mapping from the multiversion MD model to a relational schema is given along with OLAP operations in standard SQL.

Equitable distribution of vaccines is necessary to ensure those at highest risk of illness are protected from COVID-19 (coronavirus disease 2019). Unfortunately, there is significant evidence that vaccines have not been reaching the most vulnerable. At our large hospital system, we created interactive online tools to measure and visualize equitability of vaccine administrations and to help stakeholders identify populations at highest risk within state-designated eligible vaccine groups. Using race, ethnicity, gender, and social vulnerability, we are able to measure and reflect our vaccine administration performance against the communities that we serve. With our visualization tools, stakeholders have been able to target interventions to improve equity in vaccine administrations, including improvements in race, ethnicity, and social vulnerability. We plan to use the data elements incorporated in our electronic health record and data warehouse due to the COVID-19 pandemic to guide further population health efforts at decreasing disparities.

In the virtual world, the authentication process is used to prove the identity for both of the server and the clients. Therefore, the authentication is an urgent issue for sharing data between users in the data warehouse systems. This paper presents the enhancement of Data Warehouse Signature (DWS) framework through proposing a novel authentication and group-key distribution model, based on the modular symmetric polynomials, to distribute a secure group-key between the manager and the chosen executors. To the best of our knowledge, we are the first to propose a group-key based on the symmetric polynomial and the modular arithmetic. This leads to increase the security level of the proposed model from t, where t is the symmetric polynomial degree, to ∑ i = 1 c c i × t , where c is the number of executors in the DWS system. In this paper, enhancing the DWS achieves the high performance using a parallel computing in our middleware. The proposed model has the lowest cost of computation, communication and complexity overheads among the existing security models. Additionally, security analysis shows that the proposed model achieves the key security, prevents insider and outsider attacks, and provides forward and backward secrecy.

Materialized view selection (MVS) is the problem of selecting an appropriate set of views to be materialized to speed up analytical query processing of data warehouses. Online analytical processing (OLAP) of queries is an essential application of the MVS problem, in which, the response times of the queries are reduced by storing the selected views. Views are intermediate results of query processing and are selected in the MVS problem to be stored and will then be exploited in answering process of several queries. Views are usually organized as a view representation structure in the MVS problem. Multiple Views Processing Plan (MVPP) is a standard structure used for view representation in the MVS problem. Due to the tremendous amount of data, constructing the MVPP is a challenge in the big data applications. The MR-MVPP (Map-Reduce-based construction of the MVPP) is the proposed method of this paper to address this problem. The MR-MVPP performs a set similarity join (similarity-based join) on the base relations and views using the map-reduce model and the hashing technique. The MVPP construction time in the proposed method is reduced by avoiding redundant calculations in the process of creating the MVPP. The performance of the proposed method is empirically evaluated. According to the results of the experiments, the execution time of the MR-MVPP method is better than the other methods. The average time improvement is about 26.5 units. This improvement is better than the other similar researches in this area and is significant due to the high volume of data in real applications. Moreover, the proposed method works well in terms of the effectiveness of the created MVPP and has about a 50% coverage rate for view selection methods. Deterministic methods are more accurate than hashing methods and can be utilized for set similarity join as future work to probably improve the effectiveness of the constructed MVPP.

Operational Systems collect transactional data and support the execution of business processes in an organization. These systems are often the data source for Decision Support Systems (DSS), i.e. analytical systems designed to aid business users in the decision-making process. For this reason, several problems in Operational Systems, such as missing data requirements or data quality issues, can lead to unfulfilled analytical needs of the DSS and, consequently, have a negative effect on the Decision Making Process since relevant business queries may not be answered. The objective of this study is to understand the impact of the integration of DSS requirements in the design of operational systems. To achieve this objective, this dissertation uses a real use case DSS to identify the missing requirements and develop a DSS prototype to demonstrate the positive impact on the Decision-Making process when these requirements are fulfilled. Throughout this development, ways of dealing with the various types of missing requirements are going to be addressed. Additionally, a methodology to evaluate the missing requirements is suggested, along with a proposal to classify and understand the missing requirements and how they can be dealt with. Also, the evaluation method is applied, and the developed prototype is compared to the baseline system in order to measure the impact. Finally, the benefits of this integration are shown, as well as other factors that can also constrain the DSS requirements. Os sistemas operacionais recolhem dados transacionais e apoiam a execução de processos de negócio numa organização. Estes sistemas são frequentemente a fonte de dados para os Sistemas de Apoio à Decisão (SAD), ou seja, sistemas analíticos concebidos para auxiliar os utilizadores empresariais no processo de tomada de decisão. Por esta razão, vários problemas nos Sistemas Operacionais, tais como requisitos de dados em falta ou questões de qualidade de dados, podem levar a necessidades analíticas não satisfeitas do SAD e, consequentemente, ter um efeito negativo no Processo de Tomada de Decisão, uma vez que as questões de negócio relevantes podem não ser respondidas. O objetivo do presente estudo é compreender o impacto da integração dos requisitos do SAD na conceção dos sistemas operacionais. Para atingir este objetivo, esta dissertação utiliza um caso de estudo real de um SAD para identificar os requisitos em falta e desenvolver um SAD protótipo para demonstrar o impacto positivo no processo de Tomada de Decisão quando estes requisitos são cumpridos. Ao longo deste desenvolvimento, as formas de lidar com os vários tipos de requisitos em falta serão abordadas. É também proposto um método de avaliação para compreender e categorizar os requisitos em falta e a forma como podem ser tratados. Além disso, o método de avaliação é aplicado, e o protótipo desenvolvido é comparado com o sistema de base, no sentido de medir o impacto. Finalmente, são mostrados os benefícios desta integração, bem como outros fatores que também podem limitar os requisitos do SAD.

Introduction of process approach in organizations engaged in processing statistical and economic information for analytical research, transition in information systems (IS) to service-oriented architecture, integration of data from different sources, use of data warehouses necessitated the development of new approaches to creating and modernizing IS. At the same time, the requirements for the meta-information component of the IS in terms of meeting the requirements of internal and external users regarding the possibility of self-assessment of data quality are increasing. All these challenges requirestrengthening the meta-information component of the IS for the use of metadata by different user groups. The article considers the key points that need to be taken into account when adapting to the existing IS existing international experience in the modernization of economic IS direction. This information will be useful when creating a metadata repository, data warehouse, information portal on statistical and economic research., Впровадження процесного підходу у виробництво в організаціях, які займаються обробленням статистичної та економічної інформації для аналітичних досліджень, перехід в інформаційних системах до сервісорієнтованої архітектури, інтеграція даних із різних джерел, до яких залучаються показники, отримані з “великих даних”, використання сховищ даних спричинило потребу в розробленні нових підходів до створення та модернізації інформаційних систем. Поряд з цим підвищуються вимоги до метаінформаційної складової ІС з точки зору задоволення вимог внутрішніх та зовнішніх користувачів щодо можливості самостійного оцінювання якості даних, насамперед релевантності, повноти, цілісності танесуперечливості. Всі ці виклики вимагають підсилення метаінформаційної складової ІС, зокрема, створення або модернізації (за наявності) репозитарію метаданих для підтримки та використання метаданих різними групами користувачів, а також для забезпечення автоматизованого централізованого керування виробничим процесом.Не менш важливою позитивною тенденцією розвитку інформаційних технологій в організаціях, які займаються обробленням економічної інформації з різних джерел, є використання процесного підходу до реалізації виробничого процесу. Застосування процесного підходу передбачає уніфікацію технологічного процесу оброблення інформації, розкладання його на складові, виконання яких не прив’язано до конкретного обстеження. При наявності репозитарію метаданих стає можливим фіксація важливих проміжних результатів для подальшого використання в інших дослідженнях. Для такого використання необхідним є збереження контексту отримання даних (період обстеження,вибірка, характеристики якості та ін.).Для формування / модернізації інформаційних систем на засадах використання репозитарію метаданих доцільно звернутися до міжнародного досвіду, зокрема до досвіду, узагальненого протягом останніх років Європейською економічною комісією Організації Об’єднаних Націй на своїх пленарних сесіях, нарадах, конференціях, робочих засіданнях щодо модернізації ІС в галузі статистики та економічних досліджень. У статті розглядаються ключові моменти, які потрібно враховувати при адаптації цього досвіду до власних інформаційних систем. Ця інформаціябуде корисною при створенні репозитарію метаданих, сховища даних, інформаційного порталу щодо статистичних та економічних досліджень.

Organizations must quickly adapt their processes to understand the dynamic nature of modern business environments. As highlighted in the literature, centralized governance supports decision-making and performance measurement processes in technology companies. For this reason, a reliable decision-making system with an integrated data model that enables the rapid collection and transformation of data stored in heterogeneous and different sources is needed. Therefore, this paper proposes the design of a data model to implement data-driven governance through a literature review of adopted approaches. The lack of a standardized procedure and a disconnection between theoretical frameworks and practical application has emerged. This paper documented the suggested approach following these steps: (i) mapping of monitoring requirements to the data structure, (ii) documentation of ER diagram design, and (iii) reporting dashboards used for monitoring and reporting. The paper helped fill the gaps highlighted in the literature by supporting the design and development of a DWH data model coupled with a BI system. The application prototype shows benefits for top management, particularly those responsible for governance and operations, especially for risk monitoring, audit compliance, communication, knowledge sharing on strategic areas of the company, and identification and implementation of performance improvements and optimizations.

Mestrado Bolonha em Gestão e Estratégia Industrial Currently in Portugal about one in ten people is between 20 and 29 years old, which corresponds to more than one million adults and potential clients. In this age group is where the professional path of people with some type of higher education usually starts, so it is also where we have the younger and healthier elements within this more qualified population. As a result of a statistical study within the company, it was identified that despite representing a large volume in the global customer base, they have very low loyalty rates. This means that these customers are signing up for the company's services, but they stay on average for a very short time when compared to other age groups. The main objective of this project is to characterize the consumption of this age group which has been conveniently called the young-adults age group, and thus identify which are the specialties that these customers most seek and value in an insurance. Given the unequivocal leader in the health insurance market of the company in which I am located, I consider that I am in a privileged position for this study. In the health insurance business, access to customer information is an essential need and managing this information well is vital. Due to the complexity of the business and the intrinsic need to share information within the company, the use of a CRM also seems to be crucial - both for data entry and analysis. The implementation of a CRM has already been studied and its impact ascertained, confirming in this study the need for such a system. In this project I was also in charge of creating the first database that will serve as an example for the IT teams to carry out the effective implementation of the final system further in the future. Therefore, along with the main objective of the project, I will also perform a study on the database that supports this project and that was used to retrieve the analyzed data. Atualmente em Portugal cerca de uma em cada dez pessoas tem entre os 20 e os 29 anos de idade, o que corresponde a mais de um milhão de adultos e de potenciais clientes. Neste segmento etário é onde normalmente se dá o início do caminho profissional de pessoas com algum tipo de formação superior, logo é também onde temos os elementos mais jovens e saudáveis dentro dessa população mais qualificada. Como resultado de um estudo estatístico dentro da empresa foi identificado que apesar de representarem um grande volume na carteira de clientes total apresentam muito baixas taxas de fidelização. Isto significa que estes clientes estão a aderir aos serviços da empresa, embora fiquem em média muito pouco tempo quando comparado com as outras faixas etárias. O principal objectivo deste projecto é caracterizar o consumo desta faixa etária que foi convenientemente apelidada de faixa etária dos jovens-adultos, e assim, identificar quais são as especialidades que estes clientes mais procuram e prezam num seguro de saúde. Dada a posição de líder inequívoca no mercado dos seguros de saúde da empresa em que me situo considero que tenho uma posição privilegiada para este estudo. No negócio dos seguros de saúde o acesso a informações dos clientes é uma necessidade absolutamente imprescindível e fazer uma boa gestão dessa informação é vital. Devido à complexidade do negócio e à necessidade intrínseca de partilha de informação dentro da empresa a utilização de um CRM parece ser também essencial - tanto para inserção de dados como para análise. A implementação de um CRM já foi estudada e apurado o seu impacto, confirmando nesse estudo a necessidade de um sistema deste género. Neste projeto fui também encarregue de criar a primeira base de dados que vai servir de exemplo para mais tarde as equipas de IT procederem à implementação efectiva do sistema final. Logo, juntamente com o objectivo principal do projecto, farei também um estudo sobre a base de dados que sustenta este projecto e que foi utilizada para extrair os dados analisados. info:eu-repo/semantics/publishedVersion

[ES] El presente trabajo tiene como primer objetivo realizar una limpieza de los datos de los pacientes que han realizado las pruebas con los tratamientos monoclonales en el hospital La Fe de la ciudad de València de los medicamentos Aimovig, Emgality y Ajovy, creando una base de datos. Posteriormente se realiza un análisis de los mismos guiado por las directrices de los médicos que llevan a los pacientes., [EN] This paper aims mainly to clean the data of the patients of whom have carried out the tests with monoclonal treatments at the La Fe hospital in the city of Valencia for the drugs Aimovig, Emgality and Ajovy, creating a database. Subsequently, an analysis of the same is carried out, guided by the guidelines of the responsible doctors.

The work presented in this paper focuses on the modeling and implementation of business intelligence processes, specifically how to apply model-driven architecture (MDA) throughout the entire development process of a data warehouse to automatically generate the multidimensional schema. As a result, this work specifies different rules for automating the process of obtaining an online analytical processing (OLAP) cube and implementing it using a collection of metamodels and automatic transformations. The data warehouse relational and OLAP cube models are then created using a model transformation from the conceptual model (class diagram). Finally, the XML file and SQL code are generated for creating the OLAP cubes and relational data warehouse, respectively. Using transformation languages like query-view-transformation (QVT) and Acceleo, all model transformations are performed.