Master’s Degree 2021

The Master’s Degree 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 with technical and organizational knowledge for the modeling, development, selection, deployment and management of Information Systems in companies;
  • Enable students to develop skills for research, technological development and innovation, and teaching in higher education in the area of Information Systems;
  • Contribute to the constant improvement of the academic community of UNIRIO, stimulating the articulation between teaching, research and extension; and
  • Develop cutting-edge research in the area of Information Systems that can contribute to the social and economic development of the region in which UNIRIO operates.

In this sense, aligned with the Education References for the Stricto Sensu Graduate Courses in Computer Science 2019, we can indicate that Master’s Degree and Doctorate in Computer Science courses in UNIRIO aim to: “Provide the training of professionals in the field of Computer Science, with broad and deep scientific knowledge and academic and cultural vision, acting in the development of scientific research capacity, in the transfer of knowledge to society, in solving problems in public or private organizations, in generation and application of innovation processes and demands and improvements in the quality of education, contributing to the aggregation of quality, competitiveness, productivity and well-being of institutions and society.”

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 Master’s Degree 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, graduates from the course of Master’s Degree 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;
  • 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;
  • creative in proposing technically grounded solutions to research questions and problems in the Information Systems area;
  • consistent in choosing, combining and applying appropriate methods to research questions;
  • proactive in the oral and written dissemination of academic-scientific and technological results to the scientific community and society;
  • active in the management of individual and group research activities, in line with planned goals;
  • motivated to teach Information Systems;
  • motivated for 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

The compulsory subjects comprise essential content for the training of the Master’s Degree in Informatics at UNIRIO. According to the Computer Science Area Document (2019), “for courses in the academic modality: To provide studies with a solid training base in Computer Science, academic courses must also include a set of mandatory subjects in groups of areas: (i) Theory of Computer Science, Analysis of Algorithms and Complexity of Computer Science; (ii) Computer Science Methodology and Techniques; and (iii) Computer Science Systems. The student must take a mandatory subject in group (i), and choose one more mandatory subject in any of the groups (i), (ii) or (iii). The choice of mandatory subjects must be aligned with the objectives of the course and the student profile.”

60 h – 4 credits – Mandatory

Syllabus: Some Representative Algorithmic Problems. AlgorithmComplexity and Analysis. Fundamental Principles ofAlgorithm Projects. Algorithms in Graphs. GreedyAlgorithms. Division and Conquer. Dynamic Programming.NP-completeness. Polynomial Reduction. P and NPClasses. NP-complete problems.

Purpose: – Present techniques that allow to analyze and evaluatethe computational efficiency of algorithms and tocompare different algorithms that can be used to solvethe same problem.– Study fundamental principles and techniques for thedesign of correct and efficient algorithms.– Study the intrinsic complexity of problems and theirimpact on the development and efficiency of algorithmsfor their solution.– Introduce the main algorithmic ideas by presentingreal-world problems.

60 h – 4 credits – Mandatory

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

Purpose: Promote the foundation of the Information Systems area,based on systemic thinking and vision, theoreticalviews on the concept of systems, and complex thinking.Discuss the research and practice challenges inInformation Systems, from the viewpoint of fundamentaltechnologies, of the IS research community in Brazilfrom the viewpoint of the Brazilian Computer Societyand 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 todifferent philosophical traditions. Learn aboutdifferent research methods. Prepare a research project.Dynamics of production and scientific communication.

Optional Basic Core Subjects

60 h – 4 credits – Optional of Basic Core

Syllabus: Propositional Logic. Predicate Logic. Logic and Prologprogramming. Descriptive Logic. Notions of otherlogical formalisms and their applications forInformation Systems.

Purpose: Enable the student to define the syntax, semantics, anddeductive systems for propositional, predicate anddescription logic; develop Prolog programs; presentapplicability of logical formalisms for informationsystems.

60 h – 4 credits – Optional of Basic Core

Syllabus: Introduction to data analysis. Review of ProbabilitySampling Principles. Exploratory Data Analysis. BasicConcepts of Statistics. Statistical models. Estimate.The problem of statistical learning. Supervisedlearning. Linear models for regression. Linear modelsfor classification. Selection of models.Generalizability theory. Selection of variables.Dimensionality reduction. Sequential data andstochastic processes. Examples of applications.

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

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. QualityModels. Process Models. Software Measurement.Experimental Software Engineering.

Purpose: Present the main research concepts and challengesrelated to the definition, execution, and evaluation ofsoftware quality programs and initiatives to improvesoftware processes in organizations. Introduce studentsto understand and discuss research related to softwarequality and experimental software engineering.

60 h – 4 credits – Optional of Basic Core

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

Purpose: The objective of the subject is to provide thefundamentals and theories of Human Computer interactionin order to enable the student to evaluate systeminterfaces (conventional and web interfaces) toidentify the main usability and accessibility problemsand propose improvements.

60 h – 4 credits – Optional of Basic Core

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

Purpose: Present an overview of the concepts, models, andmethods of the Artificial Intelligence area, enablingthe student to develop practical applications.

60 h – 4 credits – Optional Basic Core

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

Purpose: This subject aims to allow the student to apply, in apractical way, the concepts related to modelingtechniques of computer/communication systems andperformance assessment. The focus of this subject isthe reproduction of experiments published in severalscientific articles so that the student can make acritical assessment of the approach adopted and theresults obtained in the selected works.

60 h – 4 credits – Optional Basic Core

Syllabus: Probability and statistics. Random variables. Measuresof interest. Confidence interval. Simulation. Queueingtheory.

Purpose: The mathematical modeling of computer/communicationsystems is one of the most important tasks in theprocess of analyzing and developing new technologies.Understanding how to model a system mathematically andhow to analyze the measurements obtained frommathematical models is essential for the training ofprofessionals in the area of Information Systems. Thissubject will provide the student with the necessarybasis for the use of modeling and mathematical analysistools and their application in the choice of atechnology.

Optional Thematic Subjects

60 h – 4 credits – Optional thematic

Syllabus: Basic concepts of Data Science. Data Science projectsand 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: supervisedand unsupervised. Supervised learning: regression andclassification. Unsupervised learning: clustering andassociation rules; Experimental methodologies for modelselection and parameter adjustment. Key performancemetrics. Result analysis and interpretation techniques.Applications.

Purpose: Allow the student to be able to recognize and framedata science problems and applications in a criticalway, develop low complexity analytical model projectinitiatives, systematically and consistently evaluateproposals for analytical solutions by identifying themain modeling elements as well as the experimentalmethodology.

60 h – 4 credits – Optional thematic

Syllabus: Review of complexity theory. Computationally difficultproblems. Constructive methods. Local search.Metaheuristics. Applications. Computational experimentwith heuristics.

Purpose: The objective of the course is to enable the student tosolve, on a heuristic basis, computationally difficultproblems (NP-Complete and NP-Hard), and of enormouspractical importance, through the application ofsuccessful techniques in the construction of efficientheuristics. Another objective of the course is toenable the student to perform a computationalexperiment to evaluate and analyze the results obtainedand 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; Semanticservices; Intelligent agents

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

60 h – 4 credits – Optional thematic

Syllabus: Conceptual Framework of Web Models; InterdisciplinaryThinking; Cyberculture and the Web in Society; Web 2.0and Social Software; Cyberculture, Technology andsocial life in contemporary culture; InformationTechnologies and the Web as Science; InternetGovernance (including Privacy and Reliability on theWeb); Theories of Cyberculture.

Purpose: Present fundamental concepts of Web Science as aninterdisciplinary science, as well as its impact onsociety. Discuss Cyberculture and its theories.Highlight research topics relevant to Cyberculture andWeb Science, relating to Information Systems.

60 h – 4 credits – Optional thematic

Syllabus: Fuzzy Logic; Hybrid Models; Smart Tools; Neural NetworkApplications; Fuzzy Logic Applications; Hybrid ModelApplications

Purpose: Complement the Subject of Artificial Intelligence andapply intelligent techniques in different domains. Theemphasis of the course will be on the application ofdifferent techniques and intelligent tools forknowledge extraction.

60 h – 4 credits – Optional thematic

Syllabus: Graph theory: basic definitions; paths, andconnectivity; distance and searches. Weak and stronginterconnections: triadic enclosure; strength of weakinterconnections; strength of interconnections and thestructure of large-scale networks; enclosure,structural holes and capital stock. Small-worldphenomenon: six degrees of separation; structures andrandomness; decentralized search; modeling thedecentralized search process; empirical analysis andgeneralized models; center-periphery structure and thedifficulty of decentralized search.

Purpose: The course aims to deepen the study of concepts andapproaches for the analysis of complex and socialnetworks.

60 h – 4 credits – Optional thematic

Syllabus: Introduction to wireless transmission and its mainconcepts. Characterization of types of wirelessnetworks; Personal wireless networks and Bluetoothtechnology; Wireless local area networks and the IEEE802.11 standard; Metropolitan wireless networks and theIEEE 802.16 standard; Second and third generationmobile phone networks: GSM, GPRS, UMTS, and CDMA one.Ad hoc networks, delay/interruption tolerant networks,vehicular networks, and sensor networks. Use ofsimulators for wireless networks: NS-2, NS-3, Glomosim,OPNET, OMNET++, ONE.

Purpose: Study the main concepts in wireless transmission andthe main technologies for personal networks, wirelesslocal area networks, metropolitan wireless networks,and wireless wide area networks. In addition, therecent approaches to wireless mobile networks will bestudied and these approaches will be investigatedthrough some wireless network simulators.

60 h – 4 credits – Optional thematic

Syllabus: network virtualization, cloud computing, software /OpenFlow-defined networks, mobility, wireless sensornetworks, delay- and disconnect-tolerant networks, P2Parchitectures, dynamic circuit networks, vehicularnetworks, characterization of user behavior, trafficcharacterization.

Purpose: Study, critically analyze and obtain experimentalresults related to the state-of-the-art technologiesfocused on aspects of management, performance and/orsecurity

60 h – 4 credits – Optional thematic

Syllabus: Introduction to vehicular networks, their componentsand modes of operation. Communication architectures,protocols, and standards. Vehicle mechanisms andapplications. Urban mobility and its components.Characterization, modeling and analysis of differentaspects of urban mobility. Intelligent TransportationSystems (ITS). Use of KDD and machine learningtechniques applied to vehicular networks, urbanmobility, and ITS.

Purpose: Investigate advanced topics on mobile and wirelessnetworks used by pedestrians and vehicles in a varietyof scenarios. Study the state-of-the-art of researchquestions related to the design and development ofprotocols, mechanisms and vehicular applicationsapplied to different scenarios, such as in urbancenters. Raise the state-of-the-art of topics relatedto the characterization and modeling of urban mobility.In addition, investigate topics of vehicular networksand/or urban mobility, as well as their application inintelligent transport systems.

60 h – 4 credits – Optional thematic

Syllabus: General concepts of Computer Supported CollaborativeWork (CSCW) and Groupware (software to supportcollaboration). Information and CommunicationTechnologies. Workflow Systems. Meetings Support Tools(Meetingware). Collaborative Editors. IntelligentAgents. Computer Supported Collaborative Learning(CSCL). Learning Environments (Learningware and LMS -Learning Management System). Evaluation ofcollaborative processes supported by computerenvironments

Purpose: The complexity of the problems that organizations dealwith today leads to the increasing need for group work.Teams must interact, exchange experiences, seeksolutions, and innovate. Support systems forcooperative work appear in this scenario as fundamentalpieces and, therefore, research on the construction anduse of this type of application, in addition to theirintegration with other information systems, justifiesthis subject.

60 h – 4 credits – Optional thematic

Syllabus: Software Measurement. IT Services Measurement. GQMMethods, GQM+Strategies, and PSM. Measurement in themanagement of software projects and IT services.Measurement in process improvement. Quality Standardsand Maturity Models. Introduction to StatisticalControl of Software Processes. High Maturity inSoftware Development. Statistical and QuantitativeManagement. Applications in real cases.

Purpose: Measurement is essential in Software Engineering as itconstitutes a continuous process that makes it possibleto define, collect, and analyze process data related toprojects and products, to understand and controlprojects, processes and obtain significant informationto improve processes and products. Measurement can alsobe applied to better understand IT service deliveryprocesses. Purpose: Present the main concepts relatedto the Measurement of Software and IT Services and themost used methods for the definition of a measurementplan. Discuss how measurement can assist inunderstanding the behavior of processes, in themanagement and monitoring of software projects and inthe continuous improvement of software processes and ITservices in the organization.

60 h – 4 credits – Optional thematic

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

Purpose: The objective of the subject is to provide the basicfoundations and theories of digital accessibility andweb accessibility in order to enable the student tobetter understand the different user profiles in orderto analyze the different systems (conventional and webinterfaces) in order to identify the main onesaccessibility problems and propose improvements with afocus 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 reframingof social relations) aspects of Computer-MediatedCommunication (CMC)

Purpose: At the end of the subject, the student should be ableto: design a research in Computer MediatedCommunication (CMC) and argue the scientific validityof his research project based on the theories aboutCMC.

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 theconstruction of intelligent systems.

60 h – 4 credits – Optional thematic

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

Purpose: Enable the student to understand, model, and analyzecomplex systems, teaching advanced engineeringtechniques and putting them into practice in thedevelopment of works.

60 h – 4 credits – Optional thematic

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

Purpose: Software development life cycles currently incorporatebusiness process modeling as one of their mostimportant activities. This subject aims to convey tostudents concepts related to business process modelingand support for transforming business models intorequirements and information systems models. It alsopresents the evolution of this issue towards BusinessProcess Management, discussing methods, techniques, andtools for this purpose.

60 h – 4 credits – Optional thematic

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

Purpose: The growing importance of knowledge in the daily lifeof organizations has led companies to develop resourcesthat facilitate and encourage the management of thisinput. This increasingly includes the need fortechnology and computational support to facilitate thedissemination of experience and knowledge inorganizations. This subject aims to convey to studentsconcepts related to processes, techniques, andtechnology to support knowledge management andorganizational 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 theirstorytelling characteristics. Analysis andcharacterization of games. Games as informationsystems. Discussion about games with purpose andgamification. Gamification of scenarios, withdiscussions about the application of games and theirelements in different contexts.

Purpose: Present concepts and fundamentals about games andgamification, their artistic, cultural, and socialelements, as well as their use and elements used inorganizational, educational contexts and how theyrelate to Information Systems. In addition to allowingstudents to enter researches in the field of games.

60 h – 4 credits – Optional thematic

Syllabus: Critical and Creative Thinking. Authorship inEducation. Open educational resources. CollaborativeEducation. Educational Design. PedagogicalArchitectures. Conceptual and mind maps for Education.Artificial Intelligence in Education. Affective systemsin Education. Intelligent Tutoring Systems. Search as aLearning Process. Applications in Education. Mobile andUbiquitous Computing in Education. IOT in Education.

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

60 h – 4 credits – Optional thematic

Syllabus: Presentation of the main architectures of DeepLearning. Feedforward Neural Networks. ConvolutionalNeural Networks. Recurrent Networks. Autoencoder.Generative Adversarial Networks. Learning byReinforcement. Applications to problems from differentareas.

Purpose: Allow the student to understand the operation of themain Deep Learning architectures and learn to identifysituations in which they can apply each of thesearchitectures.

Elective of instrumentation

  • Independent Studies I (30 h) – 2 credits
  • Independent Studies II (30 h) – 2 credits
  • Independent Studies III (30 h) – 2 credits
  • Quantitative Research Methods (60 h) – 4 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 (30 h) – 2 credits

Elective (Extra)

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

Elective of Research Group

  • Research Topics in Educational, Social and BusinessTechnologies I (60 h) – 4 credits
  • Research Topics in Educational, Social and BusinessTechnologies II (60 h) – 4 credits
  • Research Topics in Optimization Applied to SoftwareEngineering I (60 h) – 4 credits
  • Research Topics in Optimization Applied to SoftwareEngineering II (60 h) – 4 credits
  • Research Topics in Network and Service Management I (60 h) – 4 credits
  • Research Topics in Network and Service Management II (60 h) – 4 credits
  • Research Topics in IT Services and Software Quality I (60 h) – 4 credits
  • Research Topics in IT Services and Software Quality II (60 h) – 4 credits
  • Research Topics in Complex Systems Engineering I (60 h) – 4 credits
  • Research Topics in Complex Systems Engineering II (60 h) – 4 credits
  • Research Topics in Data Science I (60 h) – 4 credits
  • Research Topics in Data Science II (60 h) – 4 credits
  • Research Topics in Accessibility I (60 h) – 4 credits
  • Research Topics in Accessibility II (60 h) – 4 credits
  • Research Topics in Intelligent Heuristics and SocialMedia Analysis I (60 h) – 4 credits
  • Research Topics in Intelligent Heuristics and SocialMedia Analysis II (60 h) – 4 credits
  • Research Topics in Computer-Mediated Communication I (60 h) – 4 credits
  • Research Topics in Computer-Mediated Communication II (60 h) – 4 credits
  • Research Topics in Wireless Networks, Mobile Networks,and Vehicular Networks I (60 h) – 4 credits
  • Research Topics in Wireless Networks, Mobile Networks,and Vehicular Networks II (60 h) – 4 credits
  • Research Topics in Human Computing and CollectiveIntelligence, and Ubiquitous Computing and ArtificialIntelligence I (60 h) – 4 credits
  • Research Topics in Human Computing and CollectiveIntelligence, and Ubiquitous Computing and ArtificialIntelligence II (60 h) – 4 credits
  • Research Topics in Collaboration and Business ProcessesManagement I (60 h) – 4 credits
  • Research Topics in Collaboration and Business ProcessesManagement II (60 h) – 4 credits
  • Research Topics inGames and Gamification I (60 h) – 4 credits
  • Research Topics inGames and Gamification II (60 h) – 4 credits
  • Research Topics in Intellectual Property and Business I (60 h) – 4 credits
  • Research Topics in Intellectual Property and BusinessII (60 h) – 4 credits
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