Systems for processing large amounts of data

Data collection: smart phones, sensors and internet-connected devices, web, cleaning and preparation of data, data anonymization and de-identification.

Data retention; scalable relational databases, NoSQL databases, understanding the compromise between the consistency of data, performance and availability.

Data processing: event-oriented processing, processing parallelization (map-reduce), extraction of structured data from unstructured.

Analyses: efficient algorithms for processing and analysis of data, machine learning

Visualization, procedures and challenges of visualizing large amounts of data, other modalities of presentation of data (soundification, etc.).

Applications of the presented techniques: systems for context detection, smart systems (applications of smart cities, smart transport, etc.), medical applications, social networks, financial systems

Is familiar with the concept of "big data". Able to evaluate the amount of data, the rate of events, their diversity, and the key challenges associated with large amounts of data.

Knows the difference and can choose among relational or NoSQL database, and evaluate the appropriateness of use.

Knows the strengths and weaknesses of map-reduce model and evaluates it in comparison with relational databases.

Can apply basic analytical and visualization techniques for working with large amounts of data in a use-case.

Lectures or mentoring

Seminar