Doctorates Degree 2021

The Doctorate in Informatics of UNIRIO focuses on the Information Systems area, one of the sub-areas of Computer Science (according to the CAPES table). Research in Information Systems involves issues associated with the application of Computer Science, especially with regard to aspects of conceptualization, construction and effective application of computational systems called Information Systems.

  • Deepen the scientific and technical-professional knowledge of students in the area of Information Systems;
  • Train professionals in the state of the art of technical and organizational knowledge for the conception, development and deployment of Information Systems in organizations;
  • Develop in students skills for research and teaching in higher education in the area of Information Systems;
  • Develop in students skills for research and innovation in the area of Information Systems;
  • Contribute to the constant improvement of the academic community (national and international) in Information Systems, stimulating the articulation between teaching, research and extension;
  • Develop cutting-edge research in the area of Information Systems that can contribute to the social and economic development of the country;
  • Expand Brazil’s international insertion in the Information Systems area, through cooperation projects with institutions and companies abroad.

Since the creation of the course, the curricular structure contemplates the development of the necessary competences for a graduate student in Computer Science/Information Systems, according to the multidimensional view currently discussed by CAPES. The curricular structure of the course also follows the training benchmarks, considering the education of the student in his/her various possibilities of activity – teacher (undergraduate and/or graduate), advisor, researcher, professional in the industry, entrepreneur/innovator – not necessarily exclusive .

The constant evolution of Information Systems, applying techniques from different areas of Computer Science and knowledge from different application domains, requires professionals capable of monitoring such evolution and promoting the dissemination of knowledge.

The complexity of Information Systems and their integration in organizations creates the demand for professionals able to deal with these systems, both observing and influencing the context in which they are applied, and analyzing the specificities of their components, in relation to their modeling, architecture of construction and execution.

The professional graduated by the Doctorate in Informatics at UNIRIO shall be able to analyze the several technologies available for information processing under multiple aspects, in order to select, apply, integrate, develop, improve and manage the most appropriate technologies for sectors of society that require information processing mechanisms. Professionals will also be able to discuss the deficiencies of current technologies, being able to promote the development of new technologies, either through innovation, by combining existing technologies or by analogy with other areas of knowledge.

More specifically, egresses from the course of Doctorate in Informatics of UNIRIO must have the following profile:

  • observer, reflective, and critical about contemporary social and organizational contexts;
  • meticulous and critical in the survey and analysis of knowledge that shapes the state of the art and/or national and international practice in Information Systems;
  • attentive to the activities of national and international scientific communities in their area of expertise in research;
  • updated and objective in the identification and description of problems for research in the Information Systems area, as well as in the justification of its relevance;
  • versed in scientific theories relevant to a domain of knowledge in the area of Information Systems and other areas;
  • original in proposing technically grounded solutions to research questions and problems in the Information Systems area;
  • consistent and rigorous in choosing, combining and applying appropriate methods to research questions;
  • proactive and proficient in the oral and written dissemination of academic-scientific and technological results to the scientific community and society;
  • active and autonomous in the management of individual and group research activities, in line with planned goals;
  • conscious and responsible to teach Information Systems;
  • innovative and entrepreneurial in the possibilities of transferring technical and scientific knowledge and in the practical application of artifacts resulting from research activities;
  • resilient, persistent and determined in the face of the challenges of training and acting in scientific research and/or innovation;
  • attentive to the ethical and legal aspects related to the exercise of research and innovation activities.

 

Course organization:

The organization of the course follows the view of Information Systems and encompasses the identification of problems and the proposition of solutions related to:

(i) Modeling – conception and representation of both the systems and the information and knowledge they manipulate;
(ii) Development – application and management of the complexity of using technologies to build these systems; and
(iii) Management – effective use of systems in business, organizational and social contexts.

Following this vision and based on the maturity of the Program and the evolution of research and faculty, the course is currently structured in two lines of research: “Business Support Systems” and “Applied Intelligent Systems and Optimization,” that address themes, methodologies, techniques, and technologies directly related to at least one of the aspects mentioned: Modeling, Development and Management of Information Systems.

Mandatory Subjects

60 h – 4 credits – Mandatory

Syllabus: Some Representative Algorithmic Problems. Algorithm Complexity and Analysis. Fundamental Principles of Algorithm Projects. Algorithms in Graphs. Greedy Algorithms. Division and Conquer. Dynamic Programming. NP-completeness. Polynomial Reduction. P and NP Classes. NP-complete problems.

Purpose: – Present techniques that allow to analyze and evaluate the computational efficiency of algorithms and to compare different algorithms that can be used to solve the same problem.– Study fundamental principles and techniques for the design of correct and efficient algorithms.– Study the intrinsic complexity of problems and their impact on the development and efficiency of algorithms for their solution.– Introduce the main algorithmic ideas by presenting real-world problems.

60 h – 4 credits – Mandatory

Syllabus: IS Research and Practice Challenges. Systemic View. IS Research Theories and Paradigms.

Purpose: Promote the foundation of the Information Systems area, based on systemic thinking and vision, theoretical views on the concept of systems, and complex thinking. Discuss the research and practice challenges in Information Systems, from the viewpoint of fundamental technologies, of the IS research community in Brazil from the viewpoint of the Brazilian Computer Society and Administration.

60 h – 4 credits – Mandatory

Syllabus: Science and Epistemology. Research Methodology. Theorization. Research Paradigms. Research Methods. Scientific Communication. Research Ethics.

Purpose: Understand and identify research affiliated to different philosophical traditions. Learn about different research methods. Prepare a research project. Dynamics of production and scientific communication.

Optional of Basic Core Subjects

60 h – 4 credits – Optional of Basic Core

Syllabus: Propositional Logic. Predicate Logic. Logic and Prolog programming. Descriptive Logic. Notions of other logical formalisms and their applications for Information Systems.

Purpose: Enable the student to define the syntax, semantics, and deductive systems for propositional, predicate and description logic; develop Prolog programs; present applicability of logical formalisms for information systems.

60 h – 4 credits – Optional of Basic Core

Syllabus: Introduction to data analysis. Review of Probability Sampling Principles. Exploratory Data Analysis. Basic Concepts of Statistics. Statistical models. Estimate. The problem of statistical learning. Supervised learning. Linear models for regression. Linear models for classification. Selection of models. Generalizability theory. Selection of variables. Dimensionality reduction. Sequential data and stochastic processes. Examples of applications.

Purpose: Enable the student to understand and apply theories, techniques, and methods for the treatment and analysis of data and the construction of statistical models, putting them into practice in the development of case studies.

60 h – 4 credits – Optional of Basic Core

Syllabus: Introduction to Software Engineering. Software Quality. Software Process Quality. Software Product Quality. Software Process Improvement. Agile Methods. Quality Models. Process Models. Software Measurement. Experimental Software Engineering.

Purpose: Present the main research concepts and challenges related to the definition, execution, and evaluation of software quality programs and initiatives to improve software processes in organizations. Introduce students to understand and discuss research related to software quality and experimental software engineering.

60 h – 4 credits – Optional of Basic Core

Syllabus: Basic Concepts (usability, accessibility/social inclusion and communicability – semiotic engineering). Theoretical approaches in HCI. User-centered systems. Evaluation of interfaces (user inspection and observation). Identifying User Needs and HCI Requirements. Topics related to human aspects of systems.

Purpose: The objective of the subject is to provide the fundamentals and theories of Human Computer interaction in order to enable the student to evaluate system interfaces (conventional and web interfaces) to identify the main usability and accessibility problems and propose improvements.

60 h – 4 credits – Optional of Basic Core

Syllabus: Introduction to Artificial Intelligence. Knowledge Representation. Inference. Search. Decision Processes. Bayesian Networks. Machine Learning.

Purpose: Present an overview of the concepts, models, and methods of the Artificial Intelligence area, enabling the student to develop practical applications.

60 h – 4 credits – Optional of Basic Core

Syllabus: Parameters used in modeling systems; Modeling and simulation tools; Performance evaluation of models; Case study

Purpose: This subject aims to allow the student to apply, in a practical way, the concepts related to modeling techniques of computer/communication systems and performance assessment. The focus of this subject is the reproduction of experiments published in several scientific articles so that the student can make a critical assessment of the approach adopted and the results obtained in the selected works.

60 h – 4 credits – Optional of Basic Core

Syllabus: Probability and statistics. Random variables. Measures of interest. Confidence interval. Simulation. Queueing theory.

Purpose: The mathematical modeling of computer/communication systems is one of the most important tasks in the process of analyzing and developing new technologies. Understanding how to model a system mathematically and how to analyze the measurements obtained from mathematical models is essential for the training of professionals in the area of Information Systems. This subject will provide the student with the necessary basis for the use of modeling and mathematical analysis tools and their application in the choice of a technology.

Optional Thematic Subjects

60 h – 4 credits – Optional thematic

Syllabus: Basic concepts of Data Science. Data Science projects and solutions. Data Science Methodologies: KDD, CRISP-DM, etc; Analytical models: exploratory, descriptive, predictive, diagnostic, prescriptive. Maturity levels; Data Mining: Objectives, Techniques, and Algorithms; Learning patterns in data: supervised and unsupervised. Supervised learning: regression and classification. Unsupervised learning: clustering and association rules; Experimental methodologies for model selection and parameter adjustment. Key performance metrics. Result analysis and interpretation techniques. Applications.

Purpose: Allow the student to be able to recognize and frame data science problems and applications in a critical way, develop low complexity analytical model project initiatives, systematically and consistently evaluate proposals for analytical solutions by identifying the main modeling elements as well as the experimental methodology.

60 h – 4 credits – Optional thematic

Syllabus: Review of complexity theory. Computationally difficult problems. Constructive methods. Local search. Metaheuristics. Applications. Computational experiment with heuristics.

Purpose: The objective of the course is to enable the student to solve, on a heuristic basis, computationally difficult problems (NP-Complete and NP-Hard), and of enormous practical importance, through the application of successful techniques in the construction of efficient heuristics. Another objective of the course is to enable the student to perform a computational experiment to evaluate and analyze the results obtained and prepare a technical report on the experiment.

60 h – 4 credits – Optional thematic

Syllabus: Metadata and Web Search; Semantics and Context; Semantic Web Architecture; Ontologies; Rules; Semantic services; Intelligent agents

Purpose: Present the representation of content on the web, focusing mainly on its semantics, as well as the context and technology related to the Semantic Web.

60 h – 4 credits – Optional thematic

Syllabus: Conceptual Framework of Web Models; Interdisciplinary Thinking; Cyberculture and the Web in Society; Web 2.0 and Social Software; Cyberculture, Technology and social life in contemporary culture; Information Technologies and the Web as Science; Internet Governance (including Privacy and Reliability on the Web); Theories of Cyberculture.

Purpose: Present fundamental concepts of Web Science as an interdisciplinary science, as well as its impact on society. Discuss Cyberculture and its theories. Highlight research topics relevant to Cyberculture and Web Science, relating to Information Systems.

60 h – 4 credits – Optional thematic

Syllabus: Introduction to intelligent systems. Area background. Overview. Taxonomy. Applications. Advanced topics.

Purpose: Complement the Subject of Artificial Intelligence and apply intelligent techniques in different domains. The emphasis of the course will be on the application of different techniques and intelligent tools for knowledge extraction.

60 h – 4 credits – Optional thematic

Syllabus: Graph theory: basic definitions; paths, and connectivity; distance and searches. Weak and strong interconnections: triadic enclosure; strength of weak interconnections; strength of interconnections and the structure of large-scale networks; enclosure, structural holes and capital stock. Small-world phenomenon: six degrees of separation; structures and randomness; decentralized search; modeling the decentralized search process; empirical analysis and generalized models; center-periphery structure and the difficulty of decentralized search.

Purpose: The course aims to deepen the study of concepts and approaches for the analysis of complex and social networks.

60 h – 4 credits – Optional thematic

Syllabus: Introduction to wireless transmission and its main concepts. Characterization of types of wireless networks; Personal wireless networks and Bluetooth technology; Wireless local area networks and the IEEE 802.11 standard; Metropolitan wireless networks and the IEEE 802.16 standard; Second and third generation mobile phone networks: GSM, GPRS, UMTS, and CDMA one. Ad hoc networks, delay/interruption tolerant networks, vehicular networks, and sensor networks. Use of simulators for wireless networks: NS-2, NS-3, Glomosim, OPNET, OMNET++, ONE.

Purpose: Study the main concepts in wireless transmission and the main technologies for personal networks, wireless local area networks, metropolitan wireless networks, and wireless wide area networks. In addition, the recent approaches to wireless mobile networks will be studied and these approaches will be investigated through some wireless network simulators.

60 h – 4 credits – Optional thematic

Syllabus: Network virtualization, cloud computing, software / OpenFlow-defined networks, mobility, wireless sensor networks, delay- and disconnect-tolerant networks, P2P architectures, dynamic circuit networks, vehicular networks, characterization of user behavior, traffic characterization.

Purpose: Study, critically analyze and obtain experimental results related to the state-of-the-art technologies focused on aspects of management, performance and/or security

60 h – 4 credits – Optional thematic

Syllabus: Introduction to vehicular networks, their components and modes of operation. Communication architectures, protocols, and standards. Vehicle mechanisms and applications. Urban mobility and its components. Characterization, modeling and analysis of different aspects of urban mobility. Intelligent Transportation Systems (ITS). Use of KDD and machine learning techniques applied to vehicular networks, urban mobility, and ITS.

Purpose: Investigate advanced topics on mobile and wireless networks used by pedestrians and vehicles in a variety of scenarios. Study the state-of-the-art of research questions related to the design and development of protocols, mechanisms and vehicular applications applied to different scenarios, such as in urban centers. Raise the state-of-the-art of topics related to the characterization and modeling of urban mobility. In addition, investigate topics of vehicular networks and/or urban mobility, as well as their application in intelligent transport systems.

60 h – 4 credits – Optional thematic

Syllabus: General concepts of Computer Supported Collaborative Work (CSCW) and Groupware (software to support collaboration). Information and Communication Technologies. Workflow Systems. Meetings Support Tools (Meetingware). Collaborative Editors. Intelligent Agents. Computer Supported Collaborative Learning (CSCL). Learning Environments (Learningware and LMS – Learning Management System). Evaluation of collaborative processes supported by computer environments

Purpose: The complexity of the problems that organizations deal with today leads to the increasing need for group work. Teams must interact, exchange experiences, seek solutions, and innovate. Support systems for cooperative work appear in this scenario as fundamental pieces and, therefore, research on the construction and use of this type of application, in addition to their integration with other information systems, justifies this subject.

60 h – 4 credits – Optional thematic

Syllabus: Software Measurement. IT Services Measurement. GQM Methods, GQM+Strategies, and PSM. Measurement in the management of software projects and IT services. Measurement in process improvement. Quality Standards and Maturity Models. Introduction to Statistical Control of Software Processes. High Maturity in Software Development. Statistical and Quantitative Management. Applications in real cases.

Purpose: Measurement is essential in Software Engineering as it constitutes a continuous process that makes it possible to define, collect, and analyze process data related to projects and products, to understand and control projects, processes and obtain significant information to improve processes and products. Measurement can also be applied to better understand IT service delivery processes. Purpose: Present the main concepts related to the Measurement of Software and IT Services and the most used methods for the definition of a measurement plan. Discuss how measurement can assist in understanding the behavior of processes, in the management and monitoring of software projects and in the continuous improvement of software processes and IT services in the organization.

60 h – 4 credits – Optional thematic

Syllabus: Basic Concepts. User profiles. Sight. Hearing. Functional illiteracy. Senior Age. Physical/Motor Disabilities. Evaluation of Interfaces. Accessibility. Accessible Browsing. Accessible Content. Accessible Data Entry

Purpose: The objective of the subject is to provide the basic foundations and theories of digital accessibility and web accessibility in order to enable the student to better understand the different user profiles in order to analyze the different systems (conventional and web interfaces) in order to identify the main ones accessibility problems and propose improvements with a focus on diversity of audiences.

60 h – 4 credits – Optional thematic

Syllabus: Technical (computer systems and services), linguistic (textual genres and language modifications), cultural (new practices) and social (establishment and reframing of social relations) aspects of Computer-Mediated Communication (CMC)

Purpose: At the end of the subject, the student should be able to: design a research in Computer Mediated Communication (CMC) and argue the scientific validity of his research project based on the theories about CMC.

60 h – 4 credits – Optional thematic

Syllabus: Introduction to intelligent systems. Area background. Overview. Taxonomy. Applications. Advanced topics.

Purpose: Enable the student to understand and apply theories, techniques, and methods for the project and the construction of intelligent systems.

60 h – 4 credits – Optional thematic

Syllabus: Introduction to the engineering of complex, large-scale, and long-term systems. Types of complex systems. Basic concepts. Background. Definition and examples. Taxonomy and characteristics. Modeling, analysis, and monitoring techniques for complex system platforms. Research and practice challenges and perspectives. Applications in real cases.

Purpose: Enable the student to understand, model, and analyze complex systems, teaching advanced engineering techniques and putting them into practice in the development of works.

60 h – 4 credits – Optional thematic

Syllabus: Organizations and Processes; Business Process Concept; Process Modeling (Models, Method, and Tools); Business Process Management; Deriving Requirements from Process Models; Information technology impact analysis; Information Architecture; Implementation strategies and systems customization; Modeling and Implementation of Workflow Systems; Statistical process control.

Purpose: Software development life cycles currently incorporate business process modeling as one of their most important activities. This subject aims to convey to students concepts related to business process modeling and support for transforming business models into requirements and information systems models. It also presents the evolution of this issue towards Business Process Management, discussing methods, techniques, and tools for this purpose.

60 h – 4 credits – Optional thematic

Syllabus: Principles of knowledge management and transformation in organizations; Organizational memory; Knowledge Management Tools; Knowledge capture, representation, storage and retrieval paradigm; Context in Knowledge Management; Knowledge sharing; Organizational learning; Communities of practice; E-learning; Work-based training

Purpose: The growing importance of knowledge in the daily life of organizations has led companies to develop resources that facilitate and encourage the management of this input. This increasingly includes the need for technology and computational support to facilitate the dissemination of experience and knowledge in organizations. This subject aims to convey to students concepts related to processes, techniques, and technology to support knowledge management and organizational learning.

60 h – 4 credits – Optional thematic

Syllabus: Fundamentals of research in the field of games. Discussion and definition about games. Games, culture, and society. Arts and sounds in games. Games and their storytelling characteristics. Analysis and characterization of games. Games as information systems. Discussion about games with purpose and gamification. Gamification of scenarios, with discussions about the application of games and their elements in different contexts.

Purpose: Present concepts and fundamentals about games and gamification, their artistic, cultural, and social elements, as well as their use and elements used in organizational, educational contexts and how they relate to Information Systems. In addition to allowing students to enter researches in the field of games.

60 h – 4 credits – Optional thematic

Syllabus: Critical and Creative Thinking. Authorship in Education. Open educational resources. Collaborative Education. Educational Design. Pedagogical Architectures. Conceptual and mind maps for Education. Artificial Intelligence in Education. Affective systems in Education. Intelligent Tutoring Systems. Search as a Learning Process. Applications in Education. Mobile and Ubiquitous Computing in Education. IOT in Education.

Purpose: Collaboratively build an understanding of the state of the art in educational technologies. Develop an educational technology project.

60 h – 4 credits – Optional thematic

Syllabus: Presentation of the main architectures of Deep Learning. Feedforward Neural Networks. Convolutional Neural Networks. Recurrent Networks. Autoencoder. Generative Adversarial Networks. Learning by Reinforcement. Applications to problems from different areas.

Purpose: Allow the student to understand the operation of the main Deep Learning architectures and learn to identify situations in which they can apply each of these architectures.

Elective of Instrumentalization Subjects

  • Independent Studies I (30 h) – 2 credits
  • Independent Studies III (30 h) – 2 credits
  • Qualitative Research Methods (60 h) – 4 credits
  • Theories of Information Systems and Theorization. (60 h) – 4 credits
  • Design Science Research (60 h) – 4 credits
  • Teaching in Information Systems (60 h) – 4 credits
  • Innovation in Information Systems (60 h) – 4 credits
  • Scientific Communication (60 h) – 4 credits
  • Teaching Internship I (30 h) – 2 credits
  • Teaching Internship II (30 h) – 2 credits

Elective – Extra Subjects

  • Research for Thesis I (0 h) – 0 credits
  • Research for Thesis II (0 h) – 0 credits
  • Research for Thesis III (0 h) – 0 credits
  • Research for Thesis IV (0 h) – 0 credits

Elective of Research Group Subjects

  • Research Topics in Educational, Social and Business Technologies I to VI (60 h) – 4 credits
  • Research Topics in Optimization Applied to Software Engineering I to VI (60 h) – 4 credits
  • Research Topics in Network and Service Management I to VI (60 h) – 4 credits
  • Research Topics in IT Services and Software Quality I to VI (60 h) – 4 credits
  • Research Topics in Complex Systems Engineering I to VI (60 h) – 4 credits
  • Research Topics in Data Science I to VI (60 h) – 4 credits
  • Research Topics in Accessibility I to VI (60 h) – 4 credits
  • Research Topics in Intelligent Heuristics and Social Media Analysis I to VI (60 h) – 4 credits
  • Research Topics in Computer-Mediated Communication I to VI (60 h) – 4 credits
  • Research Topics in Wireless Networks, Mobile Networks, and Vehicular Networks I to VI (60 h) – 4 credits
  • Research Topics in Human Computing and Collective Intelligence, and Ubiquitous Computing and Artificial Intelligence I to VI (60 h) – 4 credits
  • Research Topics in Intellectual Property and Business I to VI (60 h) – 4 credits
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