Business Intelligence on Non-Conventional Data

The revolution in digital communications witnessed over the last decade had a significant impact on the world of Business Intelligence (BI). In the big data era, the amount and diversity of data that can be collected and analyzed for the decision-making process transcends the restricted and structured set of internal data that BI systems are conventionally limited to. This thesis investigates the unique challenges imposed by three specific categories of non-conventional data: social data, linked data and schemaless data. Social data comprises the user-generated contents published through websites and social media, which can provide a fresh and timely perception about people’s tastes and opinions. In Social BI (SBI), the analysis focuses on topics, meant as specific concepts of interest within the subject area. In this context, this thesis proposes meta-star, an alternative strategy to the traditional star-schema for modeling hierarchies of topics to enable OLAP analyses. The thesis also presents an architectural framework of a real SBI project and a cross-disciplinary benchmark for SBI. Linked data employ the Resource Description Framework (RDF) to provide a public network of interlinked, structured, cross-domain knowledge. In this context, this thesis proposes an interactive and collaborative approach to build aggregation hierarchies from linked data. Schemaless data refers to the storage of data in NoSQL databases that do not force a predefined schema, but let database instances embed their own local schemata. In this context, this thesis proposes an approach to determine the schema profile of a document-based database; the goal is to facilitate users in a schema-on-read analysis process by understanding the rules that drove the usage of the different schemata. A final and complementary contribution of this thesis is an innovative technique in the field of recommendation systems to overcome user disorientation in the analysis of a large and heterogeneous wealth of data

Enabling Ubiquitous OLAP Analyses

An OLAP analysis session is carried out as a sequence of OLAP operations applied to multidimensional cubes. At each step of a session, an operation is applied to the result of the previous step in an incremental fashion. Due to its simplicity and flexibility, OLAP is the most adopted paradigm used to explore the data stored in data warehouses. With the goal of expanding the fruition of OLAP analyses, in this thesis we touch several critical topics. We first present our contributions to deal with data extractions from service-oriented sources, which are nowadays used to provide access to many databases and analytic platforms. By addressing data extraction from these sources we make a step towards the integration of external databases into the data warehouse, thus providing richer data that can be analyzed through OLAP sessions. The second topic that we study is that of visualization of multidimensional data, which we exploit to enable OLAP on devices with limited screen and bandwidth capabilities (i.e., mobile devices). Finally, we propose solutions to obtain multidimensional schemata from unconventional sources (e.g., sensor networks), which are crucial to perform multidimensional analyses

Transformação digital: integração de ferramentas BI com CRM e dados de vendas

In recent years, technological advancements and the escalating volume of data generated have encouraged companies to adopt new and improved management procedures, supported by business software able to accommodate this new reality. Business Intelligence (BI) tools serve this purpose, with the goal of providing businesses with the ability to extract useful information from available data, enabling them to position themselves in the target market with a greater understanding of the challenges and what can be done to achieve better results. BI involves the collection, processing, cleansing, and storage of data, as well as the implementation of analytical tools. Following this, as part of a comprehensive Digital Transformation (DT) project, Amorim, in particular the Amorim Cork SGPS (AC-SGPS) business unit, has observed a constant increase in the data flow and volume, as well as a growing need to maximize data utility. Consequently, one of the Amorim’s primary objectives is to invest in BI tools that support and simplify data analysis. In this context, the primary objective of my internship project was to integrate data from a CRM tool and the currently implemented ERP system into PowerBI (PBI) in order to facilitate the analysis of existing data. The project was divided into two case studies - Customer Service departments and Commercial departments of two Amorim group companies - where the PBI was implemented independently. Each case began with an analysis of the department’s data structure, followed by the collection of initial requirements from stakeholders, and concluded with the development and implementation of the solution. Special attention was deposited on the continuous participation of stakeholders throughout the development process so that they could make optimal use of the BI tools after implementation. Subsequently, a survey was conducted with the end users in order to collect and analyze the results and inquire the added value to the covered companies. According to the members of the departments, the new access to information is clearly superior to the methods previously used, as it makes the information easier to locate and contributes to a more independent and productive method of working. In addition, the significance of implementing this type of tool for monitoring and correcting processes from a factual and quantifiable data perspective while supporting the decision-making process was emphasized. Consequently, it is anticipated that the project will contribute, on the one hand, to a reduction in costs, as a result of increased productivity and a faster and more effective decision-making process, and, on the other hand, to an increase in revenues and profits, as a result of increased customer retention and attraction, as well as greater user satisfaction and motivation. Together, these contributions are intrinsically linked to the success of AC-SGPS’s multi-departmental DT project’s business strategy and long-term objectives.Nos últimos anos, os avanços tecnológicos e a crescente quantidade de dados gerados levaram as empresas a adotar novos e melhores procedimentos de gestão, apoiados por software empresarial com capacidade de fazer frente a esta nova realidade. É aqui que entram as ferramentas de Business Intelligence (BI), com o objetivo de proporcionar às empresas a capacidade de extrair informação útil dos dados disponíveis, permitindo que se posicionem no mercado alvo com uma maior compreensão dos desafios e do que pode ser feito para alcançar melhores resultados. BI engloba métodos de recolha, tratamento, limpeza e armazenamento de dados e a implementação de ferramentas analíticas. Neste seguimento, como parte de um projeto abrangente de Transformação Digital (TD), a Amorim, e em particular a unidade de negócios Amorim Cork SGPS (AC-SGPS), tem notado um aumento constante no fluxo e volume de dados, associado a uma necessidade crescente de tirar o máximo partido dos mesmos. Consequentemente, um dos principais objetivos do grupo Amorim passa pelo investimento em ferramentas de BI que tornem a análise de dados mais fácil e intuitiva. Neste contexto, o foco do meu projeto de estágio passou pela integração de dados de uma ferramenta CRM e do sistema ERP, atualmente implementados, com Power BI (PBI), a fim de simplificar e melhorar a análise da informação existente. O projeto foi dividido em dois casos de estudo - departamentos de Serviço de Apoio ao Cliente e departamentos Comerciais de duas empresas do grupo Amorim -, onde a implementação do PBI foi realizada separadamente. Para cada caso, começou-se por realizar uma análise da estrutura de dados da empresa, tendo-se seguido uma recolha de requisitos iniciais dos stakeholders, culminando com o seu desenvolvimento e implementação. Durante todo o processo de desenvolvimento, foi dada prioridade à participação constante dos stakeholders para que, numa fase pós-implementação, pudessem fazer o melhor uso possível das ferramentas BI. Posteriormente foi realizado um inquérito aos utilizadores como forma de recolher e analisar os resultados e averiguar o valor acrescentado às empresas abrangidas. De um ponto de vista geral, segundo os membros dos departamentos, o novo acesso à informação é claramente superior aos métodos utilizados até então, tornando-a mais fácil de encontrar e consequentemente contribuindo para um método de trabalho mais independente e produtivo. Foi também salientada a importância da implementação deste tipo de ferramenta na monitorização e correção de processos a partir de uma perspetiva factual e quantificável dos dados, ao mesmo tempo que apoia o processo de tomada de decisão. Por conseguinte, prevê-se que o projeto venha a contribuir, por um lado para uma redução dos custos, resultante de uma maior produtividade e de um processo de tomada de decisão mais rápido e eficaz, e por outro lado para um aumento das receitas e dos lucros, como resultado de uma maior retenção e atracão de clientes, a par de uma maior satisfação e motivação dos utilizadores. Estas contribuições, no seu conjunto, estão intrinsecamente ligadas ao sucesso da estratégia empresarial e dos objetivos a longo prazo definidos no projeto multidepartamental de TD da AC-SGPS.Mestrado em Engenharia e Gestão Industria

An HMM-Based Framework for Supporting Accurate Classification of Music Datasets

open3In this paper, we use Hidden Markov Models (HMM) and Mel-Frequency Cepstral Coecients (MFCC) to build statistical models of classical music composers directly from the music datasets. Several musical pieces are divided by instruments (String, Piano, Chorus, Orchestra), and, for each instrument, statistical models of the composers are computed.We selected 19 dierent composers spanning four centuries by using a total number of 400 musical pieces. Each musical piece is classied as belonging to a composer if the corresponding HMM gives the highest likelihood for that piece. We show that the so-developed models can be used to obtain useful information on the correlation between the composers. Moreover, by using the maximum likelihood approach, we also classied the instrumentation used by the same composer. Besides as an analysis tool, the described approach has been used as a classier. This overall originates an HMM-based framework for supporting accurate classication of music datasets. On a dataset of String Quartet movements, we obtained an average composer classication accuracy of more than 96%. As regards instrumentation classication, we obtained an average classication of slightly less than 100% for Piano, Orchestra and String Quartet. In this paper, the most signicant results coming from our experimental assessment and analysis are reported and discussed in detail.openCuzzocrea, Alfredo; Mumolo, Enzo; Vercelli, GianniCuzzocrea, Alfredo; Mumolo, Enzo; Vercelli, Giann

Data analytics

This study guide is devoted to substantiating the nature, role and importance of data, information, analytical work, explanation of its basic principles within modern information environment, as well as consideration of the main approaches and basic tools while performing the analytical tasks by specialists in the sphere of political analytics as well as of social work