LEARNING OUTCOMES

Learning the theoretical, methodological, and technological fundamentals of data management for advanced data processing architectures, with a specific reference to large-scale distributed environments, like key elements of NoSQL and stream-based systems as well as basic issues in parallel and distributed query processing, multi-query processing, and high-throughput transactional systems.

AIMS AND LEARNING OUTCOMES

DESCRIBE the principles for data management in distributed systems, environments for large-scale data processing,  systems for large-scale data management and approaches for data stream management

UNDERSTAND the differences between traditional data processing and management and large-scale (semantic) data processing and management

UNDERSTAND the differences between the presented approaches for large-scale (semantic) data management  

UNDERSTAND the ethical implications of large-scale (semantic) data management

SELECT the system and the methodology for large-scale (semantic) data management, suitable in a given application context

USE some of the presented systems for  large-scale (semantic) data management, for solving simple problems

USE at least one of the presented systems for large-scale (semantic) data management for solving non-trivial problems

ANSWER questions related to large-scale (semantic) data management

SOLVE exercizes related to the data design  in one of the presented systems and the interaction with such systems, through the available languages

PREREQUISITES

Prerequisites correspond to basic notions of data management in traditional systems:

Data model, notion of schema and instance
Conceptual data model
Relational model (logical model)
Conceptual design
Logical design
Basics of normalization theory
Relational algebra
SQL
Index
Transaction

TEACHING METHODS

For LM in Computer Science: Class, project and outside preparation

For LM in Computer Engineering: Class, project (optional) and outside preparation

SYLLABUS/CONTENT

Recap on large scale distributed architectures and data-intensive computing

Recap on big data and distributed archtectures
Principles of large scale data management
Architectural approaches for large scale data management
Recap on environments for large scale data processing (MapReduce, Spark)

Systems for large-scale data management

Introduction to NoSQL systems
NoSQL data models
Key-value data stores
Document-based data stores
Column-family data stores
Graph-based data stores

Semantic data management [only for LM in Computer Science]

The role of semantics in data management
Models, languages, and systems for semantic data management
Knowledge graphs and ontologies for data integration

Ethic-based data management

Principles of ethic-based data management
Techniques for ethic-based data management

RECOMMENDED READING/BIBLIOGRAPHY

• Serge Abiteboul, Ioana Manolescu, Philippe Rigaux, Marie-Christine Rousset, Pierre Senellart. Web Data Management. Cambridge University Press, 2011.
• P.J. Sadalage, M.Fowler. Nosql Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison Wesley, 2013
• Jeff Carpenter, Eben Hewitt,  Cassandra: The Definitive Guide, O'Reilly Media, 2016
• Ian Robinson, Jim Webber & Emil Eifrem. Graph Databases, New Opportunities  for Connected Data, 2nd Edition, O’Reilly, 2015
• Additional material and references provided by the instructors.