studies

The first part of the course, focuses on managing security and privacy of Information Systems and the main phases of the ISMS (Information Security Management System) implementation are described. The requirements of the information security management standards (e.g. ISO 27001, 27002) as well as the main steps of the risk assessment / risk management methodologies (e.g. OCTAVE, CRAMM, ISO 27005) are presented. The second part examines the governance of information security involving the evaluation of the implementation of the security and business continuity standards (eg ISO 22301) based on the business needs of the organization. For this purpose the COBIT for Security standard is presented for the specialization of operational needs (goal cascade) to IT goals.

Introduction - review of relational and object-relational databases. Modern trends in database design. Non-traditional data types (text, multimedia, spatial information). Non-traditional database architecture (sensor networks, data streams, distributed, in the cloud). The era of "big data" - Big Data (MapReduce architecture, data processing techniques and knowledge mining). Laboratory hours in PostgreSQL and Spark.

The course studies the theory, methods and technologies for developing business process and knowledge management systems. It integrates core concepts from Management Information System (MIS) with those of Operations Management (OM) and helps learn to model and analyze business processes, assess and improve process efficiency, identify and capture knowledge assets and implement knowledge representation and sharing systems. Course topics include:
• Business process modeling. Systematically document and model operational business processes by means of event and information flow models. Construct visual process models using methods such as UML activity diagrams or data flow diagrams.
• Knowledge representation with ontologies. Protégé tool.
• Knowledge-based inferencing with stochastic and fuzzy rules.
• Knowledge management using Content Management Systems.

Nowadays, more and more companies choose to satisfy their requirements in Enterprise software not by following traditional approaches such as: buying ready software solutions or developing software solutions to meet their business needs. Instead more and more companies choose a software-as-a-service (SaaS) business model to satisfy their needs. Thus, Customer Relationship Management (CRM) solutions, Enterprise Resource Planning (ERP) systems and Supply Chain Management (SCM) systems are provided as software-as-a-service. A SaaS business model provides flexibility to the companies, but also creates new challenges. In this module we analyze these challenges and the new opportunities that arise in this new and dynamic business environment. At the same time we analyze issues that are related to the three basic types of cloud computing: Infrastructure as a service (IaaS), Platform as a service (PaaS) and Software as a service (SaaS).

Introduction - data preparation for analytics. Statistical data analysis. Mechanical learning and knowledge extraction techniques (categorization / classification, cluster analysis, etc.). Special applications (detailed time series, spatial information, writers, etc.). Privacy issues - personal data protection. Laboratory hours in Matlab, R, Python.

Introductory concepts. Constraint-based Optimization. Optimization in linear systems. Dualism. Linear optimization applications in analytical data. Curved and non-curved data set housing. Boundary analysis and data envelopment analysis. Computational techniques. Envelopment analysis of data streams.

Principles of Probability Theory, Variation Analysis (ANOVA), Regression and Correlation Analysis, Confidence Space and Statistical Testing of Cases, Prediction Techniques, Principal Component Analysis - Factor Analysis, Data Visualization Methods.

Emerging architectures, technologies and algorithms of the 4th Industrial Revolution (Industry 4.0), Models, methodologies and supply chain security systems (Supply Chain Security), Security and privacy in the age of Cybersecurity in Artificial Intelligence and the European Approach, Requirements arising from the European Legislative framework and the technological challenges that arise in matters of cybersecurity and Artificial Intelligence.