Exponential data growth in life sciences demands cross discipline work that brings together computing and life sciences in a usable manner that can enhance knowledge and understanding in both fields. High throughput approaches, advances in instrumentation and overall complexity of mass spectrometry data have made it impossible for researchers to manually analyse data using existing market tools. By applying a user-centred approach to effectively capture domain knowledge and experience of biologists, this thesis has bridged the gap between computation and biology through software, PepTracker (http://www.peptracker.com). This software provides a framework for the systematic detection and analysis of proteins that can be correlated with biological properties to expand the functional annotation of the genome. The tools created in this study aim to place analysis capabilities back in the hands of biologists, who are expert in evaluating their data. Another major advantage of the PepTracker suite is the implementation of a data warehouse, which manages and collates highly annotated experimental data from numerous experiments carried out by many researchers. This repository captures the collective experience of a laboratory, which can be accessed via user-friendly interfaces. Rather than viewing datasets as isolated components, this thesis explores the potential that can be gained from collating datasets in a “super-experiment” ideology, leading to formation of broad ranging questions and promoting biology driven lines of questioning. This has been uniquely implemented by integrating tools and techniques from the field of Business Intelligence with Life Sciences and successfully shown to aid in the analysis of proteomic interaction experiments. Having conquered a means of documenting a static proteomics snapshot of cells, the proteomics field is progressing towards understanding the extremely complex nature of cell dynamics. PepTracker facilitates this by providing the means to gather and analyse many protein properties to generate new biological insight, as demonstrated by the identification of novel protein isoforms.

Submitted by Pedro Henrique Gomes Ruggiero (ruggiero.pedro@gmail.com) on 2017-03-07T21:35:42Z No. of bitstreams: 1 Pedro Ruggiero - Inteligência Analítica.pdf: 1428333 bytes, checksum: 5c6a222d7a65e07f37e372a4a766af25 (MD5) / Approved for entry into archive by Pamela Beltran Tonsa (pamela.tonsa@fgv.br) on 2017-03-08T15:30:53Z (GMT) No. of bitstreams: 1 Pedro Ruggiero - Inteligência Analítica.pdf: 1428333 bytes, checksum: 5c6a222d7a65e07f37e372a4a766af25 (MD5) / Made available in DSpace on 2017-03-09T13:38:58Z (GMT). No. of bitstreams: 1 Pedro Ruggiero - Inteligência Analítica.pdf: 1428333 bytes, checksum: 5c6a222d7a65e07f37e372a4a766af25 (MD5) Previous issue date: 2017-02-17 / In the era of Big Data, the area of Business Intelligence and Analytics has become increasingly important for academic communities as well as for the market. Most researchers believe that it is necessary to train and train professionals with a set of skills in technologies, techniques and business, but there is little understanding and convergence on this set. The objective of this paper is to list and describe, according to the literature, a set of competencies to perform in Business Intelligence and Analytics, since there are many complementary studies and with different answers. Focusing on this objective, an analysis of citations was carried out to collect a base of texts that were the scope of the work; An analysis in these texts, from the perspective of the Theory of Competences, to find the competences; A validation with specialists, in order to diminish the subjectivity of the use of the Theory of Competences; And a frequency count of competencies validated throughout the text base, to obtain material and to describe those competencies. A unified set of skills was determined, between knowledge, skills and behavior. For researchers, the result of this work serves as a unified basis of competences and their descriptions, which could be used to carry out numerous more advanced studies and directed towards the use of Business Intelligence and Analytics. For business managers, the result of this work could be used to select professionals to perform in Business Intelligence and Analytics, or to develop internal training, among others. / Na era do Big Data, a área de Inteligência Analítica tem se tornado cada vez mais importante para comunidades acadêmicas e também para o mercado. A maioria dos pesquisadores acredita que é necessário formar e treinar profissionais cada vez mais completos, com um conjunto de competências em tecnologias, técnicas e negócio, mas há pouco entendimento e convergência sobre esse conjunto. O objetivo deste trabalho é apontar e descrever, segundo a literatura, um conjunto de competências para atuação em Inteligência Analítica, visto que há muitos estudos complementares e com diferentes respostas. Com foco nesse objetivo realizou-se uma análise de citações para levantamento de uma base de textos que foram escopo do trabalho; uma análise nesses textos, sob óptica da Teoria de Competências, para levantamento das competências; uma validação com especialistas, de forma a diminuir a subjetividade do uso da Teoria de Competências; e uma contagem de frequência das competências validadas em toda a base de textos, para obter material e descrever essas competências. Obteve-se um conjunto unificado de competências, entre conhecimentos, habilidades e comportamentos. Para pesquisadores, o resultado deste trabalho serve como uma base unificada de competências e suas descrições, o que poderia ser utilizado para realizar inúmeros estudos mais avançados e voltados para o uso de Inteligência Analítica. Para gerentes de negócio, o resultado deste trabalho poderia ser utilizado para seleção de profissionais para atuação em Inteligência Analítica, ou para desenvolver treinamentos internos, entre outros.

Submitted by Ana Lucia de Queiroz Tourinho (analucia.tourinho@gmail.com) on 2017-07-27T15:24:24Z No. of bitstreams: 1 Tese_Ana Lucia Tourinho_Final_Entrega.pdf: 2587062 bytes, checksum: 3a12b24dad9c3cf4ced309dcabea48f9 (MD5) / Approved for entry into archive by Pamela Beltran Tonsa (pamela.tonsa@fgv.br) on 2017-07-27T15:25:56Z (GMT) No. of bitstreams: 1 Tese_Ana Lucia Tourinho_Final_Entrega.pdf: 2587062 bytes, checksum: 3a12b24dad9c3cf4ced309dcabea48f9 (MD5) / Made available in DSpace on 2017-07-27T17:03:10Z (GMT). No. of bitstreams: 1 Tese_Ana Lucia Tourinho_Final_Entrega.pdf: 2587062 bytes, checksum: 3a12b24dad9c3cf4ced309dcabea48f9 (MD5) Previous issue date: 2017-06-29 / O crescimento maciço da quantidade de dados que as empresas, organizações e a sociedade foram obrigadas a lidar, reforça a necessidade de estudos sobre Business Intelligence, Business Intelligence Analytics e Big data. Este assunto permanece na agenda das empresas como uma das prioridades e vem se tornando cada vez mais relevante, visto que os dados e as informações compreendem a matéria-prima a partir da qual as empresas desenvolvem as suas estratégias de negócios para competir em um mundo mais complexo e rápido. Portanto, entender como explorar melhor os dados disponíveis é hoje uma disciplina de grande importância, não só como uma questão de negócios, mas também como um tema de pesquisa acadêmica. Neste cenário, onde se busca o melhor uso dos dados, o presente estudo tem como objetivo desenvolver uma pesquisa científica que permita medir a capacidade de uma organização transformar os dados em valor para o negócio. Embora certos aspectos do tema Business Intelligence, Business Intelligence Analytics e Big data já estejam bem definidos entre os pesquisadores, ainda há uma falta de compreensão e consenso sobre quais são os antecedentes do construto Business Intelligence Analytics (BIA) e quais combinações de variáveis levam a um melhor uso dos dados. Além disso, embora vários pesquisadores tenham endereçado o tema sucesso em BIA com diferentes abordagens, evidenciamos a falta de estudos que comprovem sua eficácia. Neste estudo, estamos propondo um modelo conceitual integrado para Business Intelligence Analytics Capability (BIAC), com escalas que possibilitem testar sua eficácia. Em última análise, o modelo proposto BIAC poderá ser posteriormente aplicado pelas empresas que desejem avaliar seu nível de maturidade na BIA. / The massive growth in the amount of data that companies, organizations, and society have been compelled to deal with, reinforce the need for studies on Business Intelligence, Business Intelligence Analytics and Big data. This subject remains among business agenda of priorities and has become more and more relevant since data and information comprise the raw material from which business strategies are developed to compete in a more complex and fast world. Therefore, understanding how to better exploit the available data is today a matter of great importance, not only as a business issue but also as an academic research topic. Within this scenario, where the best use of data is sought, the present study aims at developing a scientific research that enables to measure the capability of an organization to transform data into business value. Although certain aspects of the theme Business Intelligence, Business Intelligence Analytics and Big data are already established between researchers, there is still a lack of understanding and consensus on which are the antecedents of a Business Intelligence Analytics construct (BIA), and how to combine variables to enforce a better use of data. In addition, although researchers have addressed success in BIA by different approaches, we have also identified a lack of studies proving its effectiveness. In this study, we are proposing an integrated conceptual model for Business Intelligence Analytics Capability (BIAC) with proper scales to test its effectiveness. Ultimately, the BIAC may later be applied by firms to assess their level of maturity in BIA.