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Activity Funded

Information Management Research Center

Centro de Investigação em Gestão de Informação

Reference
UIDB/04152/2020
Project Start Date
2020-01-01
Project End Date
2024-12-31
Principal Investigator
Scientific Area
Exact sciences
Funding Program
Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base

Abstract

10.2 Summary in English for general dissemination purposes MagIC is a Research Center focused on using information to improve organizations and develop society in general. The main objective is to contribute to the advance of the field of Information Management and Data Science. We are committed to improve information usage and reliability, while developing tools and methods to promote data-driven decision-making. Our contributions lie in finding ways to use information management and data science to improve productivity and sustainability through data-driven decisions and fact-based decision-making. It’s clear that the ability of technology to capture and store data has not yet been matched by its ability to transform data into actionable and relevant information. We live in a data rich environment, but the tools to make sense of all that data are still somewhat limited. Moreover, the dawn of big data will only make this problem/opportunity more apparent, while presenting new and harder challenges. Recent developments have produced significant enthusiasm about how the meeting between big datasets and very powerful computers will change this state of things. MagIC researchers share the enthusiasm for this new and exciting field and are committed to help shape more information-rich environments, as a force for positive change in science and business. The ability to find insights in our data-intensive society will translate into new and clever solutions for pressing societal challenges. As a research group we are interested in the study of information, and the interaction of humans and organizations with information. MagIC has developed a wide range of expertise, with local and international partners that bring complementary know-how to our core competencies. We also seek to help develop a specialized workforce, which can support organizations making the most of their data and information resources. Through our research we aim to inspire young talents to pursue a research career and become the leading data scientists and researchers of tomorrow. In the next five years we will be focusing our research efforts in four research lines: Geoinformatics: on how to leverage information and technology developments to promote cities that are more intelligent and sustainable, but also more open and participatory; Information Systems: what are the key drivers for the adoption, diffusion and success of IT, such as blockchain, e-participation or IoT; Data Science: understand the role of neuroevolution strategies in the development of dynamic deep learning architectures; and also improve imbalanced learning through new data generation algorithms; Data-Driven Marketing: what are the processes underlying human judgment and decision-making, and how technology has changed them. 10.3 Summary in English for evaluation MagIC is a Research Center focused on using information to improve organizations and develop society in general. The main objective is to contribute to the advance of the field of Information Management and Data Science. Weare committed to improve information usage and reliability, by developing tools and methods to promote data-driven decision-making. Our contributions lie in finding ways to use information management and data science to improve productivity and sustainability through data-driven decisions and fact-based decision-making. It’s clear that the ability of technology to capture and store data has not yet been matched by its ability to transform data into actionable and relevant information. We live in a data rich environment, but the tools to make sense of all that data are still somewhat limited. Moreover, the dawn of big data will only make this problem/opportunity more apparent, while presenting new and harder challenges. Recent developments have produced significant enthusiasm about how the meeting between very big datasets and very powerful computers will change this state of things. MagIC researchers share the enthusiasm for this new and exciting field and are committed to help shape more information-rich environments, as a transformative force for positive change in science and business. We believe that the ability to find insights in our data-intensive society will translate into new and smart solutions for pressing societal challenges. In the next five years we will be focusing our research efforts in four research streams: Geoinformatics in enabling smart and open cities; Information technology diffusion processes; Data Science in neuroevolution and data generation for imbalanced learning; and Data-Driven Marketing. Geoinformatics: enabling smart and open cities. Cities must continuously strive to provide a sustainable, safe and livable environment for their ever-increasing populations. For citizens to participate in smart city projects they should understand and trust the processes driving smart cities and their services. In addition, sustainability and efficiency can only be attained through data-driven management. In the next five years we’ll be seeking to contribute with methods and tools to understand and manage the critical elements of trust and participation in open cities as well as methods that allow for a more fact-based decision-making. This line of research also benefits from the research developed in the other 3 research streams, providing the opportunity for practical applications for their research findings. Information technology (IT) diffusion processes. The research will continue focused on IT innovativeness adoption and value. We’ll focus on upcoming IT challenges such as Blockchain, IoT, digital transformation, e-participation, all significant tools for the smart and open city. The objective continues to be the understanding IT diffusion process across all adoption units using, primary and secondary survey, and real-behavior data (e.g., logs). We’ll be focused on providing research insights on how to adopt and get the most value out of innovative IT, which is usually an extremely difficult, complex and extensive process. Data Science. Deep Learning through Neuroevolution. One of the challenges faced by Deep Learning technology has to do with the fix-topology of the networks architecture, typically defined a priori based on the user experience. The ability to develop networks, which learn and adapt their architecture at the same time, constitutes a significant improvement over the state of the art. Making use of recent developments in neuroevolution and our own proposals (e.g. GSGP) we are interested in developing research towards the design of compact and effective representations for deep learning allowing for evolvable/growing architectures. GSGP constitutes a good candidate for the automatic synthesis of evolvable very large deep neural networks. Also, we will continue to research the processes of data generation for imbalanced learning. Our interest is twofold, not only in proposing new and improved algorithms for data generation, but also to apply these algorithms to remote sensing, where very small minority classes are frequent and important. So far we have published 3 new algorithms, all of them showing improvements over previous approaches, and we seek to deploy easy to use implementations for practitioners and develop new algorithms for the regression case, in which the target variables are continuous. Data-Driven Marketing. The Data-Driven Marketing research stream applies theories and tools to have a better understanding of human judgment, decision-making, and related processes. The research will continue on the development of new methods for testing marketing theories with structural equation modeling, with a special focus on bridging design and behavioral research using variance-based SEM. The research will also incorporate important themes such as Digital Marketing and Consumer Neuroscience providing additional tools for research and practice in marketing and consumer behavior. The focus will be placed on understanding consumer processes in digital environments and underlying neural mechanisms for information processing and decision-making, thus going beyond traditional views of marketing research and consumer psychology. This line of research will integrate contributions with other members of MagIC working on health and NOVA Clinical Research Unit.

Institutions

Main Institutions

  • Universidade Nova de Lisboa MagIC (MagIC)
  • Universidade Nova de Lisboa (UNL)

Funding 468.750,00 €

Fundação para a Ciência e a Tecnologia (FCT) - Portugal

468.750,00 €