Relatório de Estágio - Solução de BI Roaming Data Science (RoaDS) em ambiente Vodafone

A telecom company (Vodafone), had the need to implement a Business Intelligence solution for Roaming data across a wide set of different data sources. Based on the data visualization of this solution, its key users with decision power, can make a business analysis and needs of infrastructure and software expansion. This document aims to expose the scientific papers produced with the various stages of production of the solution (state of the art, architecture design and implementation results), this Business Intelligence solution was designed and implemented with OLAP methodologies and technologies in a Data Warehouse composed of Data Marts arranged in constellation, the visualization layer was custom made in JavaScript (VueJS). As a base for the results a questionnaire was created to be filled in by the key users of the solution. Based on this questionnaire it was possible to ascertain that user acceptance was satisfactory. The proposed objectives for the implementation of the BI solution with all the requirements was achieved with the infrastructure itself created from scratch in Kubernetes. This BI platform can be expanded using column storage databases created specifically with OLAP workloads in mind, removing the need for an OLAP cube layer. Based on Machine Learning algorithms, the platform will be able to perform the predictions needed to make decisions about Vodafone's Roaming infrastructure

Growth of relational model: Interdependence and complementary to big data

A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system

Database Principles and Technologies – Based on Huawei GaussDB

This open access book contains eight chapters that deal with database technologies, including the development history of database, database fundamentals, introduction to SQL syntax, classification of SQL syntax, database security fundamentals, database development environment, database design fundamentals, and the application of Huawei’s cloud database product GaussDB database. This book can be used as a textbook for database courses in colleges and universities, and is also suitable as a reference book for the HCIA-GaussDB V1.5 certification examination. The Huawei GaussDB (for MySQL) used in the book is a Huawei cloud-based high-performance, highly applicable relational database that fully supports the syntax and functionality of the open source database MySQL. All the experiments in this book can be run on this database platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Database Principles and Technologies – Based on Huawei GaussDB

This open access book contains eight chapters that deal with database technologies, including the development history of database, database fundamentals, introduction to SQL syntax, classification of SQL syntax, database security fundamentals, database development environment, database design fundamentals, and the application of Huawei’s cloud database product GaussDB database. This book can be used as a textbook for database courses in colleges and universities, and is also suitable as a reference book for the HCIA-GaussDB V1.5 certification examination. The Huawei GaussDB (for MySQL) used in the book is a Huawei cloud-based high-performance, highly applicable relational database that fully supports the syntax and functionality of the open source database MySQL. All the experiments in this book can be run on this database platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Antecedents of business intelligence system use

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Organisational reliance on information has become vital for organisational competitiveness. With increasing data volumes, Business Intelligence (BI) becomes a cornerstone of the decision-support system. However, employee resistance to use Business Intelligence Systems (BIS) is evident. This creates a problem to organisations in realising the benefits of BIS. It is thus important to study the enablers of sustained use of BIS amongst employees. This thesis identifies existing theories that can be used to study BI system use. It integrates and extends technology use theories through a framework focusing on Business Intelligence System Use (BISU). Empirical research is then conducted in Kuwait’s telecom and banking industries through a close-ended, self-administered questionnaire using a five-point Likert scale. Responses were received from 211 BI users. The data was analysed using SmartPLS to study the convergent and discriminant validity and reliability. Partial least squares structural equation modelling (PLS-SEM) was used to study the direct and indirect relationships between constructs and answer the hypotheses. In addition to SmartPLS, SPSS was used for descriptive analysis. The results indicated that UTAUT factors consisting of performance expectancy, effort expectancy and social influence positively impact BI system use. Voluntariness of use was found to positively moderate the relationship between social influence and BI system use. Furthermore, BI system quality positively impacts both performance expectancy and effort expectancy. The BI user’s self-efficacy also positively impacts effort expectancy. In addition, social influence was found to be positively influenced by organisational factors, namely top management support and information culture. The findings of this research contribute to literature by determining and quantifying the factors that influence BISU through the lens of employee perspectives. This thesis also explains how employees’ object-based beliefs about BI affect their behavioural beliefs, which in turn impact BISU. Limitations of this research include the omission of UTAUT’s facilitating conditions and the limited variance of respondent demographics

Uso de sistemas multidimensionais e algoritmos de data mining para implantação do método Time Driven Activity Based Costing (TDABC) em organizações orientadas por projectos

JEL: M150 e M410Quando o método ABC (Activity Based Costing) foi apresentado para o rateio de custos de actividades de processos gerenciais, representou uma profunda modificação em relação aos métodos anteriormente utilizados. Logo ficaram patentes as enormes vantagens que trazia assim como os desafios em implementálo. O método TDABC (Time Driven Activity Based Costing) surgiu devido justamente às dificuldades operacionais do uso do ABC. Ao invés do uso de estimativas, normalmente dadas pelo corpo de funcionários da empresa, do percentual de tempo gasto em cada actividade, o TDABC propõe duas fundamentais mudanças em relação ao seu predecessor. A primeira é que se considera um tempo de inactividade em relação ao total de horas potencialmente trabalhadas (idle time). A segunda é que será calculado o tempo gasto por hora de trabalho. Nesse caso, o gasto em cada actividade será conduzido multiplicando-se esse valor por hora pelo total de horas requerido por ela. O método TDABC gera um resultado fundamental na hora que for implantado em uma empresa. São as chamadas equações de tempo para cada actividade. Nessas equações, é calculado o tempo gasto em cada actividade diante de diferentes níveis de complexidade na execução dessa. Todo esse trabalho só é possível diante da existência de sistemas de gestão integrada ERP (Enterprise Resource Planning) que registram cada acção na empresa. Nessa tese de doutoramento há duas propostas relativas a implantação do TDABC em empresas: A primeira é que o acompanhamento dos tempos de actividades seja feito por um sistema de ERP associado a um sistema de Business Intelligence (BI) ao invés de um sistema simples de ERP. A segunda proposta é decorrente da primeira. Sugere-se o uso de algoritmos de data mining (principalmente os algoritmos de árvore de indução e de análise de conglomerados), presentes nos sistemas de BI, para a detecção de níveis de complexidade nas equações de tempo. Como razão para a primeira proposta mostramos que sistemas de ERP jamais foram planejados para a detecção de padrões entre os dados neles armazenados. Portanto, sozinhos, eles não poderiam detectar os níveis de complexidade existentes na execução de uma mesma actividade. Para a segunda proposta mostramos que em organizações orientadas por projectos, ou que tenham departamentos que elaborem projectos e possam ser considerados como análogos a estas, a escala do número de actividades e seus dados gerados é tão ampla que gera a necessidade de um sistema automático de detecção de níveis de complexidade nessas actividades. A construção desses objectivos nessa tese segue a seguinte ordem: Primeiro é elaborada uma revisão do método ABC e as razões que levaram ao modelo subsequente TDABC. Em seguida apresenta-se também os conceitos de gerenciamento de projectos e Business Intelligence, notadamente a arquitectura multidimensional de dados e os algoritmos de data mining, introduzindo-se a maneira com que BI possibilita a diferenciação em níveis de complexidade nas equações de tempo. Para tanto faz-se uma introdução à linguagem MDX (Multidimensional Expression) de construção de relatórios em BI. Também se mostra, através de uma introdução aos sistemas de ERP, que esse tipo de sistema sozinho não viabilizaria esse tipo de resultado. Como forma de ilustrar todos esses conceitos é relatada a experiência de colecta de dados de actividades em projectos desenvolvidos em três organizações e a aplicação de BI para a geração das equações de tempo sobre esses dados.ABC (Activity Based Cost) method was introduced in order to organize the way costs should be partitioned among enterprise management activities, and caused a deep change in the way this division used to be made. Soon it became quite clear the huge advantages of employing such method and the challenges associated with it. The TDABC method (Time Driven Activity Based Cost) was designed to overcome the operational difficulties in using ABC. Rather than employing estimates provided by the company employees, concerning the time spent on each management activity, TDABC suggest two pivotal changes in comparison with its predecessor. First, TDABC considers an idle time regarding the potential total time available for work. Second, TDABC calculates the cost spent per work hour. Therefore, the overall activity cost is reached by simple multiplication of this cost per hour by the number of work hours required by the activity. TDABC produces a fundamental output when it is employed in a company. It is the set of time equations for the management activities. Through these equations, it is possible to calculate the time spent in each activity considering also their different levels of complexities. This result is possible only due to ERP (Enterprise Resource Planning) systems that record every action being performed within the company. In this thesis, it is suggested two main initiatives concerning the usage of TDABC in enterprises. The first one is to employ a Business Intelligence (BI) system associated with an ERP system in order to track the time spent on the management activities. The second initiative is a consequence of the first. It is suggested the usage of Data Mining algorithms (mainly the algorithms for cluster analysis), available in BI suites, for the detection of the complexities levels within the time equations. As justification for the first initiative, it is shown that ERP systems were never designed to detect patterns within their databases. Therefore, without a BI module, it would be quite cumbersome for an ERP system to detect complexity levels in executing a management activity. For the second initiative, it is shown that an average enterprise produces a large-scale number of management activities, and tracking these activities generates a huge amount of data. The volume of information makes impossible to realize the levels of complexities inside the time equations without an automatic procedure to support it. The first part of this work is oriented to introduce a revision of the ABC and TDABC methods. Later, it is introduced the concepts of projects and project management. It is also presented some concepts about Business Intelligence systems and the multidimensional data architecture. The work also introduces the data mining algorithms that make available the detection of the complexity levels in management activities. It is also introduced the MDX( Multidimensional Expression ) language for building reports in BI systems as way to generate the proper sets of data for such detection. It is then reinforced the difficulties to perform this type of analysis in pure ERP systems. In order to illustrate these results it is reported a case study performed in three project management companies and the BI generation of time equations

Data aggregation for multi-instance security management tools in telecommunication network

Communication Service Providers employ multiple instances of network monitoring tools within extensive networks that span large geographical regions, encompassing entire countries. By collecting monitoring data from various nodes and consolidating it in a central location, a comprehensive control dashboard is established, presenting an overall network status categorized under different perspectives. In order to achieve this centralized view, we evaluated three architectural options: polling data from individual nodes to a central node, asynchronous push of data from individual nodes to a central node, and a cloud-based Extract, Transform, Load (ETL) approach. Our analysis leads us to the conclusion that the third option is most suitable for the telecommunication system use case. Remarkably, we observed that the quantity of monitoring results is approximately 30 times greater than the total number of devices monitored within the network. Implementing the ETL-based approach, we achieved favorable performance times of 2.23 seconds, 7.16 seconds, and 27.96 seconds for small, medium, and large networks, respectively. Notably, the extraction operation required the most significant amount of time, followed by the load and processing phases. Furthermore, in terms of average memory consumption, the small, medium, and large networks necessitated 323.59 MB, 497.34 MB, and 1668.59 MB, respectively. It is worth noting that the relationship between the total number of devices in the system and both performance and memory consumption is linear in nature