MSE Master of Science in Engineering

The Swiss engineering master's degree

Ogni modulo equivale a 3 crediti ECTS. È possibile scegliere un totale di 10 moduli/30 ECTS nelle seguenti categorie: 

  • 12-15 crediti ECTS in moduli tecnico-scientifici (TSM)
    I moduli TSM trasmettono competenze tecniche specifiche del profilo e si integrano ai moduli di approfondimento decentralizzati.
  • 9-12 crediti ECTS in basi teoriche ampliate (FTP)
    I moduli FTP trattano principalmente basi teoriche come la matematica, la fisica, la teoria dell’informazione, la chimica ecc. I moduli ampliano la competenza scientifica dello studente e contribuiscono a creare un importante sinergia tra i concetti astratti e l’applicazione fondamentale per l’innovazione 
  • 6-9 crediti ECTS in moduli di contesto (CM)
    I moduli CM trasmettono competenze supplementari in settori quali gestione delle tecnologie, economia aziendale, comunicazione, gestione dei progetti, diritto dei brevetti, diritto contrattuale ecc.

La descrizione del modulo (scarica il pdf)riporta le informazioni linguistiche per ogni modulo, suddivise nelle seguenti categorie:

  • Insegnamento
  • Documentazione
  • Esame
Data Management (TSM_DataMgmt)

This course is about Data Engineering and Information Retrieval. It covers methods and technologies for managing, processing and analyzing potentially large and distributed data collections, including multi-model databases and NoSQL stores. And it covers also mastering data in unstructured form (full text search). The course consists of four parts: 1. Database management; 2. Data warehousing and data analytics (business intelligence); 3. Data integration including data synthesizing; and 4. Information Retrieval.


  • Relational Models, Relational Algebra
  • Normalization
  • SQL:92
  • Transaction Processing, Concurrency Control
  • Security in relational database systems
  • Query optimization (btree indexes)
  • RDBMS architectures

Obiettivi di apprendimento

This module covers following important aspects of Data Engineering:

  • Students understand the use of modern database technologies for processing and managing large and distributed data collections.
  • Reaching beyond RDBMS, students learn about data structures (data types) and know which of these to use depending on the requirements and type of data available (polyglot persistence, multi-model databases).
  • The students know NoSQL stores and selected cloud data stores.
  • The students know methods and tools to integrate, to cleanse and to synthesize data.
  • Students know how to deal with full text information using databases and search engines (information retrieval).
  • The students can also apply the acquired knowledge in their own working environment.

Categoria modulo

The lecture is divided into four parts:

  1. Database Management (DB): New data structures and alternatives to RDBMS. The first part deals with the storage of data and with the non-relational aspects, including NoSQL and cloud data stores
  2. Data Warehousing and Data Analytics (DW): The second part deals with data warehousing, i.e. data aggregation and data analytics (business intelligence).
  3. Data Integration (DI): In the third part, methods and tools for data integration, data cleansing and data synthesizing (e.g. for training and testing) are explained.
  4. Information Retrieval (IR): The fourth part deals with finding information in full text using databases and (enterprise) search engines, including crawling..


  1. DB: 4 - 6 weeks
  2. DW: 3 weeks
  3. DI: 1 week
  4. IR: 4 - 5 weeks

Metodologie di insegnamento e apprendimento

Frontal teaching, exercises, case studies.


Optional literature suggestion (books):

  • DB: Lena Wiese: Advanced Data Management for SQL, NoSQL, Cloud and Distributed Databases. De Gruyter Textbook. 2015. ISBN 978-3-11-044140-6.
  • IR: "Modern Information Retrieval". Baeza-Yates & Ribeiro-Neto, New York (2011). ISBN: 9780321416919.
  • IR: Introduction to Information Retrieval. C.D. Manning, P. Raghavan, H. Schütze. Cambridge UP, 2008. Classical and web information retrieval systems: algorithms, mathematical foundations and practical issues.
  • IR: Information Retrieval in Practice. B. Croft, D. Metzler, T. Strohman. Pearson Education, 2009.

Scarica il descrittivo completo del modulo