Inteligência coopetitiva setorial: uma proposta de modelo

The business environment context points at the necessity of new forms of management for the sustainable competitiveness of organizations through time. Coopetition is characterized as an alternative in the interaction of different actors, which compete and cooperate simultaneously, in the pursuit of common goals. This dual relation, within a gain-increasing perspective, converts competitors into partners and fosters competitiveness, especially that of organizations within a specific sector. The field of competitive intelligence has, in its turn, assisted organizations, individually, in the systematization of information valuable to decision-making processes, which benefits competitiveness. It follows that it is possible to combine coopetition and competitive intelligence in a systematized process of sectorial intelligence for coopetitive relations. The general aim of this study is, therefore, to put forth a model of sectorial coopetitive intelligence. The methodological outlining of the study is characterized as a mixed approach (quantitative and qualitative methods), of an applied nature, of exploratory and descriptive aims. The Coordination of the Strategic Roadmapping Project for the Future of Paraná's Industry is the selected object of investigation. Protocols have been designed to collect primary and secondary data. In the collection of the primary ata, online questionary were sent to the sectors selected for examination. A total of 149 answers to the online questionary were obtained, and interviews were performed with all embers of the technical team of the Coordination, in a total of five interviewees. After the collection, all the data were tabulated, analyzed and validated by means of focal groups with the same five members of the Coordination technical team, and interviews were performed with a representative of each of the four sectors selected, in a total of nine participants in the validation. The results allowed the systematization of a sectorial coopetitive intelligence model called ICoops. This model is characterized by five stages, namely, planning, collection, nalysis, project development, dissemination and evaluation. Each stage is detailed in inputs, activities and outputs. The results suggest that sectorial coopetition is motivated mainly by knowledge sharing, technological development, investment in R&D, innovation, chain integration and resource complementation. The importance of a neutral institution has been recognized as a facilitator and incentive to the approximation of organizations. Among the main difficulties are the financing of the projects, the adhesion of new members, the lack of tools for the analysis of information and the dissemination of the actions.

ProgettoCNR Energy+: metodo di calcolo semplificato per la scomposizione della radiazione solare globale e la stima della produzione da fotovoltaico

Scopo del presente lavoro è la presentazione del codice di calcolo semplificato adoperato nella sezione “Simulatore fotovoltaico” presente sul portale www.energia.cnr.it del progetto CNR ENERGY+. Utilizzando i valori reali di radiazione solare misurati dalle stazioni meteorologiche installate presso alcune sedi del CNR il codice, con appropriati algoritmi, generala scomposizione della radiazione sul piano orizzontale e su superfici inclinate e variamente orientate, in modo da pervenire alla potenza prodotta da un ipotetico impianto fotovoltaico posto sullo stesso sito di ubicazione della stazione.

Discovery driven analysis on semi-structured text data

Discovery Driven Analysis (DDA) is a common feature of OLAP technology to analyze structured data. In essence, DDA helps analysts to discover anomalous data by highlighting 'unexpected' values in the OLAP cube. By giving indications to the analyst on what dimensions to explore, DDA speeds up the process of discovering anomalies and their causes. However, Discovery Driven Analysis (and OLAP in general) is only applicable on structured data, such as records in databases. We propose a system to extend DDA technology to semi-structured text documents, that is, text documents with a few structured data. Our system pipeline consists of two stages: first, the text part of each document is structured around user specified dimensions, using semi-PLSA algorithm; then, we adapt DDA to these fully structured documents, thus enabling DDA on text documents. We present some applications of this system in OLAP analysis and show how scalability issues are solved. Results show that our system can handle reasonable datasets of documents, in real time, without any need for pre-computation.

The NumbersWithNames Program

We present the NumbersWithNames program which performs data-mining on the Encyclopedia of Integer Sequences to find interesting conjectures in number theory. The program forms conjectures by finding empirical relationships between a sequence chosen by the user and those in the Encyclopedia. Furthermore, it transforms the chosen sequence into another set of sequences about which conjectures can also be formed. Finally, the program prunes and sorts the conjectures so that themost plausible ones are presented first. We describe here the many improvements to the previous Prolog implementation which have enabled us to provide NumbersWithNames as an online program. We also present some new results from using NumbersWithNames, including details of an automated proof plan of a conjecture NumbersWithNames helped to discover.

Collecting valuable consumer data: Motivating consumers to disclose personal information

Collecting and analyzing consumer data is essential in today’s data-driven business environment. However, consumers are becoming more aware of the value of the information they can provide to companies, thereby being more reluctant to share it for free. Therefore, companies need to find ways to motivate consumers to disclose personal information. The main research question of the study was formed as “How can companies motivate consumers to disclose personal information?” and it was further divided into two subquestions: 1) What types of benefits motivate consumers to disclose personal information? 2) How does the disclosure context affect the consumers’ information disclosure behavior? The conceptual framework consisted of a classification of extrinsic and intrinsic benefits, and moderating factors, which were recognized on the basis of prior research in the field. The study was conducted by using qualitative research methods. The primary data was collected by interviewing ten representatives from eight companies. The data was analyzed and reported according to predetermined themes. The findings of the study confirm that consumers can be motivated to disclose personal information by offering different types of extrinsic (monetary saving, time saving, self-enhancement, and social adjustment) and intrinsic (novelty, pleasure, and altruism) benefits. However, not all the benefits are equally useful ways to convince the customer to disclose information. Moreover, different factors in the disclosure context can either alleviate or increase the effectiveness of the benefits and the consumers’ motivation to disclose personal information. Such factors include the consumer’s privacy concerns, perceived trust towards the company, the relevancy of the requested information, personalization, website elements (especially security, usability, and aesthetics of a website), and the consumer’s shopping motivation. This study has several contributions. It is essential that companies recognize the most attractive benefits regarding their business and their customers, and that they understand how the disclosure context affects the consumer’s information disclosure behavior. The likelihood of information disclosure can be increased, for example, by offering benefits that meet the consumers’ needs and preferences, improving the relevancy of the asked information, stating the reasons for data collection, creating and maintaining a trustworthy image of the company, and enhancing the quality of the company’s website.

Previsão da Classificação da qualidade dos efluentes da indústria petrolífera com recurso a sistemas inteligentes

O presente trabalho realizou-se na Refinaria de Sines e teve como principal objectivo a utilização de ferramentas oriundas da Área Científica da Inteligência Artificial no desenvolvimento de modelos de previsão da classificação da Água Residual Industrial de acordo com a Legislação em vigor, com vista à minimização dos impactes ambientais e das tarifas aplicadas pela Concessionária (Águas de Santo André) à Refinaria. Actualmente a avaliação da qualidade do efluente é realizada através de métodos analíticos após colheita de uma amostra do efluente final. Esta abordagem é muito restritiva já que não permite actuar sobre o efluente em questão pois apenas pode evitar que, no futuro, uma mistura semelhante volte a ser refinada. Devido a estas limitações, o desenvolvimento de modelos de previsão baseados em Data Mining mostrou ser uma alternativa para uma questão pró-activa da qualidade dos efluentes que pode contribuir decisivamente para o cumprimento das metas definidas pela Empresa. No decurso do trabalho, foram desenvolvidos dois modelos de previsão da qualidade do efluente industrial com desempenhos muito semelhantes. Um deles utiliza a composição das misturas processadas e o outro, utiliza informações relativas ao crude predominante na mistura. ABSTRACT; This study has taken place at the Sines Refinery and its main objective is the use of Artificial Intelligence tools for the development of predictive models to classify industrial residual waters according with the Portuguese Law, based on the characteristics of the mixtures of crude oil that arrive into the Refinery to be processed, to minimize the Environmental impacts and the application of taxes. Currently, the evaluation of the quality of effluent is performed by analytical methods after harvesting a sample of the final effluent. This approach is very restrictive since it does not act on the intended effluent; it can only avoid that in the future a similar mixture is refined. Duet these limitations, the development of forecasting models based on Data Mining has proved to be an alternative on the important issue which is the quality of effluent, which may contribute to the achievement of targets set by the Company. During this study, two models were developed to predict the quality of industrial effluents with very similar performances. One uses the composition of processed mixtures and the other uses information regarding the predominant oil in the mixture.

Analysis and Visualisation of Crowd-sourced Tourism Data

C3S2E '16 Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering

Uma arquitetura de referência para o processamento distribuído de stream de dados em soluções analíticas de near real-time

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.

Minería de datos con Oracle Data Miner

La intención del proyecto es mostrar las diferentes características que ofrece Oracle en el campo de la minería de datos, con la finalidad de saber si puede ser una plataforma apta para la investigación y la educación en la universidad. En la primera parte del proyecto se estudia la aplicación “Oracle Data Miner” y como, mediante un flujo de trabajo visual e intuitivo, pueden aplicarse las distintas técnicas de minería (clasificación, regresión, clustering y asociación). Para mostrar la ejecución de estas técnicas se han usado dataset procedentes de la universidad de Irvine. Con ello se ha conseguido observar el comportamiento de los distintos algoritmos en situaciones reales. Para cada técnica se expone como evaluar su fiabilidad y como interpretar los resultados que se obtienen a partir de su aplicación. También se muestra la aplicación de las técnicas mediante el uso del lenguaje PL/SQL. Gracias a ello podemos integrar la minería de datos en nuestras aplicaciones de manera sencilla. En la segunda parte del proyecto, se ha elaborado un prototipo de una aplicación que utiliza la minería de datos, en concreto la clasificación para obtener el diagnóstico y la probabilidad de que un tumor de mama sea maligno o benigno, a partir de los resultados de una citología.

Interfaces for Intelligent Environments

Presentaciones de la asignatura Interfaces para Entornos Inteligentes del Máster en Tecnologías de la Informática/Machine Learning and Data Mining.

Utilização de inteligência competitiva, da gestão de riscos e da computação aplicada para ganhos de competitividade em instituição organizadora de concursos

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.

Virtual Enterprise Process Management: An Application to Industrial Maintenance

Part 2: Behaviour and Coordination

Algorithms and tools of big data: A bibliographic review

66 p.

Hotel Vila Galé Lagos

Dissertação de Mestrado, Direção e Gestão Hoteleira, Escola Superior de Gestão, Hotelaria e Turismo, Universidade do Algarve, 2016