Crowdfunding campaigns: Perceived quality and success

Research in Crowdfunding is an emerging priority within the field of Entrepreneurship. Hundreds of platforms provide nowadays multiple Crowdfunding schemes which are intended to make it easier for entrepreneurs and others to collect money from the crowd. However, only a few campaigns become successful as others don’t reach the pre-established funding goal. It is thus necessary to keep on understanding the dynamics of these platforms and the factors which justify success. The asymmetry of information has been shown to be a delicate issue as people perceive quality in different manners. As so, this research aims to understand which components of perceived quality mostly influence investments decisions. Mainly Entrepreneurship and Marketing theories were explored along the way. This is research follows a causal approach where nineteen hypotheses are tested. An experimental survey was conducted and data was collected from 127 people who were asked to evaluate one of the most important pieces of any Crowdfunding campaign – the pitch video – and consequently invest on the presented products.

Semantic architecture for sensors

Based on the report for the unit “Sociology of New Information Technologies” of the Master on Computer Sciences at FCT/University Nova Lisbon in 2015-16. The responsible of this curricular unit is Prof. António Moniz

Proving a market for a startup befor scaling using the hypothesis-driven entrepreneurship process

The year is 2015 and the startup and tech business ecosphere has never seen more activity. In New York City alone, the tech startup industry is on track to amass $8 billion dollars in total funding – the highest in 7 years (CB Insights, 2015). According to the Kauffman Index of Entrepreneurship (2015), this figure represents just 20% of the total funding in the United States. Thanks to platforms that link entrepreneurs with investors, there are simply more funding opportunities than ever, and funding can be initiated in a variety of ways (angel investors, venture capital firms, crowdfunding). And yet, in spite of all this, according to Forbes Magazine (2015), nine of ten startups will fail. Because of the unpredictable nature of the modern tech industry, it is difficult to pinpoint exactly why 90% of startups fail – but the general consensus amongst top tech executives is that “startups make products that no one wants” (Fortune, 2014). In 2011, author Eric Ries wrote a book called The Lean Startup in attempts to solve this all-too-familiar problem. It was in this book where he developed the framework for The Hypothesis-Driven Entrepreneurship Process, an iterative process that aims at proving a market before actually launching a product. Ries discusses concepts such as the Minimum Variable Product, the smallest set of activities necessary to disprove a hypothesis (or business model characteristic). Ries encourages acting briefly and often: if you are to fail, then fail fast. In today’s fast-moving economy, an entrepreneur cannot afford to waste his own time, nor his customer’s time. The purpose of this thesis is to conduct an in-depth of analysis of Hypothesis-Driven Entrepreneurship Process, in order to test market viability of a reallife startup idea, ShowMeAround. This analysis will follow the scientific Lean Startup approach; for the purpose of developing a functional business model and business plan. The objective is to conclude with an investment-ready startup idea, backed by rigorous entrepreneurial study.

Plataforma de gestão educativa de mediação entre a secretaria e a comunidade educativa

O presente Relatório tem como objetivo expor o trabalho desenvolvido durante o estágio, no âmbito da componente não letiva do mestrado em Ciências da Educação, realizado ao abrigo de um protocolo entre a Faculdade de Ciências Socais e Humanas (FCSH) da Universidade Nova de Lisboa (UNL) e a Escola Profissional Gustave Eiffel (EPGE), sob orientação científica do Professor Doutor Luís Manuel Bernardo da FCSH e orientação prática pela Mestre Maria Goreti Freitas, da EPGE. O estágio teve a duração de três meses, com um total de 400 horas. Deste modo, serão descritas as práticas de estágio, sustentadas nos conhecimentos adquiridos nas componentes letivas do mestrado em Ciências da Educação. O relatório está estruturado em três capítulos, finalizando com uma reflexão sobre a produtividade do estágio para a nossa aprendizagem. No primeiro capítulo, pretende-se justificar a escolha da temática deste relatório, procedendo-se à caracterização da secretaria e da escola, fundamentada através de uma revisão de literatura, bem como por algumas entrevistas realizadas durante o estágio. Neste capítulo dá-se importância ao facto deste estágio ter permitido uma interação com toda a comunidade educativa, nas suas respetivas diferenças, em diferentes níveis e situações. No segundo capítulo, efetuou-se a uma contextualização acerca das plataformas de gestão educativas bem como a sua importância para a gestão da comunidade educativa. Evidenciou-se em particular o papel das plataformas de gestão na educação, enquanto elo de ligação entre a secretaria e toda a comunidade educativa. No terceiro capítulo levou-se a cabo a descrição das principais atividades desenvolvidas durante o estágio curricular. Por fim, apresenta-se a uma reflexão crítica sobre o processo de estágio. Todo o trabalho remete para documentos anexos, os quais comprovam a documentação complementar. Alguns destes documentos foram elaborados de forma original, designadamente as entrevistas realizadas no decurso do estágio.

Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.

Crowdfunding

Crowdfunding, as we know it today, is a very recent activity that was born almost accidentally in the end of the 90’s decade. Due to the advent of the internet and the social networks, entrepreneurs are now able to promote their projects to a very large community. Whether it is composed by family, friends, acquaintances or simply people that are interested in the same topic or share the passion, the community is able to fund new ventures by individually investing modest amounts of money. In return, the entrepreneur can offer symbolic rewards, shares or other financial returns. New crowdfunding platforms are born almost every day all over the world, offering a new way of raising capital for their projects or a new way to invest their money in innovative ventures. Although crowdfunding is still finding its place in the financial services, successful cases such as Kickstarter demonstrate the power of the crowd in boosting creativity and productivity, financing thousands of projects by raising millions of dollars from thousands of investors. Due to regulatory restrictions, the most prominent model for now is reward-based crowdfunding, where the investors are prized with symbolic returns or privileged access to the products or services offered by the entrepreneurs. Other models such as peer-to-peer lending are also surging, allowing borrowers access to capital at a lower cost compared to so-called traditional financial institutions, and offering lenders a higher rate of return. But when it comes to offering shares to investors, i.e. using equity-based crowdfunding, entrepreneurs face regulatory obstacles in almost every country, where legislation was passed decades ago with the objective of promoting financially-capable ventures and protecting investors. Access to capital has become more difficult after the global economic recession of 2008, and for most countries it will not get easier in the near future, leaving start-ups and small enterprises with few options to start or expand their operations. In this study we attempt to answer the following research questions: how has equity-based crowdfunding evolved since its creation? Where and how has equity-based crowdfunding been implemented so far? What are the constraints and opportunities for implementing equity-crowdfunding in the world, and more particularly in Portugal? Finally, we will discuss the risks of crowdfunding and reflect on the future of this industry.

Modelos de negócios para a internet das coisas baseados em big data : Muzzley, um estudo de caso

A Internet das Coisas tal como o Big Data e a análise dos dados são dos temas mais discutidos ao querermos observar ou prever as tendências do mercado para as próximas décadas, como o volume económico, financeiro e social, pelo que será relevante perceber a importância destes temas na atualidade. Nesta dissertação será descrita a origem da Internet das Coisas, a sua definição (por vezes confundida com o termo Machine to Machine, redes interligadas de máquinas controladas e monitorizadas remotamente e que possibilitam a troca de dados (Bahga e Madisetti 2014)), o seu ecossistema que envolve a tecnologia, software, dispositivos, aplicações, a infra-estrutura envolvente, e ainda os aspetos relacionados com a segurança, privacidade e modelos de negócios da Internet das Coisas. Pretende-se igualmente explicar cada um dos “Vs” associados ao Big Data: Velocidade, Volume, Variedade e Veracidade, a importância da Business Inteligence e do Data Mining, destacando-se algumas técnicas utilizadas de modo a transformar o volume dos dados em conhecimento para as empresas. Um dos objetivos deste trabalho é a análise das áreas de IoT, modelos de negócio e as implicações do Big Data e da análise de dados como elementos chave para a dinamização do negócio de uma empresa nesta área. O mercado da Internet of Things tem vindo a ganhar dimensão, fruto da Internet e da tecnologia. Devido à importância destes dois recursos e á falta de estudos em Portugal neste campo, com esta dissertação, sustentada na metodologia do “Estudo do Caso”, pretende-se dar a conhecer a experiência portuguesa no mercado da Internet das Coisas. Visa-se assim perceber quais os mecanismos utilizados para trabalhar os dados, a metodologia, sua importância, que consequências trazem para o modelo de negócio e quais as decisões tomadas com base nesses mesmos dados. Este estudo tem ainda como objetivo incentivar empresas portuguesas que estejam neste mercado ou que nele pretendam aceder, a adoptarem estratégias, mecanismos e ferramentas concretas no que diz respeito ao Big Data e análise dos dados.

Improving Millennium Bank's personal finance manager

Double Degree

Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

From a NoSQL data source to a business intelligence solution: An experiment

We are living in the era of Big Data. A time which is characterized by the continuous creation of vast amounts of data, originated from different sources, and with different formats. First, with the rise of the social networks and, more recently, with the advent of the Internet of Things (IoT), in which everyone and (eventually) everything is linked to the Internet, data with enormous potential for organizations is being continuously generated. In order to be more competitive, organizations want to access and explore all the richness that is present in those data. Indeed, Big Data is only as valuable as the insights organizations gather from it to make better decisions, which is the main goal of Business Intelligence. In this paper we describe an experiment in which data obtained from a NoSQL data source (database technology explicitly developed to deal with the specificities of Big Data) is used to feed a Business Intelligence solution.

Child abuse monitor system model: a health care critical knowledge monitor system

The Childhood protection is a subject with high value for the society, but, the Child Abuse cases are difficult to identify. The process from suspicious to accusation is very difficult to achieve. It must configure very strong evidences. Typically, Health Care services deal with these cases from the beginning where there are evidences based on the diagnosis, but they aren’t enough to promote the accusation. Besides that, this subject it’s highly sensitive because there are legal aspects to deal with such as: the patient privacy, paternity issues, medical confidentiality, among others. We propose a Child Abuses critical knowledge monitor system model that addresses this problem. This decision support system is implemented with a multiple scientific domains: to capture of tokens from clinical documents from multiple sources; a topic model approach to identify the topics of the documents; knowledge management through the use of ontologies to support the critical knowledge sensibility concepts and relations such as: symptoms, behaviors, among other evidences in order to match with the topics inferred from the clinical documents and then alert and log when clinical evidences are present. Based on these alerts clinical personnel could analyze the situation and take the appropriate procedures.

Detection of dangerous situations using a smart internet of things system

The Internet of Things (IoT) is a concept that can foster the emergence of innovative applications. In order to minimize parents’s concerns about their children’s safety, this paper presents the design of a smart Internet of Things system for identifying dangerous situations. The system will be based on real time collection and analysis of physiological signals monitored by non-invasive and non-intrusive sensors, Frequency IDentification (RFID) tags and a Global Positioning System (GPS) to determine when a child is in danger. The assumption of a state of danger is made taking into account the validation of a certain number of biometric reactions to some specific situations and according to a self-learning algorithm developed for this architecture. The results of the analysis of data collected and the location of the child will be able in real time to child’s care holders in a web application.

Using a domain-specific language to enrich ETL schemas

Today it is easy to find a lot of tools to define data migration schemas among different types of information systems. Data migration processes use to be implemented on a very diverse range of applications, ranging from conventional operational systems to data warehousing platforms. The implementation of a data migration process often involves a serious planning, considering the development of conceptual migration schemas at early stages. Such schemas help architects and engineers to plan and discuss the most adequate way to migrate data between two different systems. In this paper we present and discuss a way for enriching data migration conceptual schemas in BPMN using a domain-specific language, demonstrating how to convert such enriched schemas to a first correspondent physical representation (a skeleton) in a conventional ETL implementation tool like Kettle.

Improving selective collection of urban waste using a business intelligence system

Today recovering urban waste requires effective management services, which usually imply sophisticated monitoring and analysis mechanisms. This is essential for the smooth running of the entire recycling process as well as for planning and control urban waste recovering. In this paper we present a business intelligence system especially designed and im- plemented to support regular decision-making tasks on urban waste management processes. The system provides a set of domain-oriented analytical tools for studying and characterizing poten- tial scenarios of collection processes of urban waste, as well as for supporting waste manage- ment in urban areas, allowing for the organization and optimization of collection services. In or- der to clarify the way the system was developed and the how it operates, particularly in process visualization and data analysis, we also present the organization model of the system, the ser- vices it disposes, and the interface platforms for exploring data.

Using relational algebra on the specification of real world ETL processes

Modeling Extract-Transform-Load (ETL) processes of a Data Warehousing System has always been a challenge. The heterogeneity of the sources, the quality of the data obtained and the conciliation process are some of the issues that must be addressed in the design phase of this critical component. Commercial ETL tools often provide proprietary diagrammatic components and modeling languages that are not standard, thus not providing the ideal separation between a modeling platform and an execution platform. This separation in conjunction with the use of standard notations and languages is critical in a system that tends to evolve through time and which cannot be undermined by a normally expensive tool that becomes an unsatisfactory component. In this paper we demonstrate the application of Relational Algebra as a modeling language of an ETL system as an effort to standardize operations and provide a basis for uncommon ETL execution platforms.