Lambda architectures for the interconnection of information systems services

Approaches to the storage, use and analysis of data through service flows. Batch layer to store the data in a medium, serving layer to create indexes and Real-time processing layer.

Information system as a service approach

Service catalogs and mechanisms for finding, selecting, executing, monitoring, evaluating and costing. Methodology for modeling and developing information systems as a service.

Platform as a Service

Platform approaches to implementing information systems as a service. Serverless computing architectures. Workshop focusing on programming, configuring, and running applications using the Google AppEngine platform and the Apache OpenWhisk platform.

Self-management of information systems

Real-time infrastructure and information system data monitoring and analysis techniques. Scalability, Elasticity and Fault Tolerance Approaches.

Artificial intelligence and machine learning for information systems management

Information system service development profiling services and runtime changes using artificial intelligence and machine learning (neural networks, reinforcement learning) approaches.

Cloud computing and information systems

Modeling and migration of information systems to cloud computing and storage infrastructures. Sizing of necessary resources and real-time feedback techniques to adapt the infrastructure based on the needs of the information systems.

Introduction and neural networks I

Introduction to artificial intelligence and machine learning, problem categories, supervised learning, unsupervised learning, reinforcement learning, examples of applications. Introduction to neural networks, neural network models and architectures, perceptron, linear and non-linear separability, multilayer perceptron, neural network training algorithms.

Neural networks II

Performance evaluation of neural networks, generalizability, neural network development applications, case study.

Clustering I

Definitions, clustering classes, distance functions, similarity functions, partitional clustering, k-means algorithm.

Clustering II

Hierarchical clustering, evaluation and validity of clustering, applications of clustering, case study.