Some classes are given by invited speakers, experts in some specific fields.
– PostgreSQL: Up and Running (3rd Edition), Regina Obe and Leo Hsu, O’Reilly Media, 2017
– An Introduction to XML and Web Technologies, Anders Møller and Michael I. Schwartzbach, Addison-Wesley, 2006
– Building the Data Warehouse (4th Edition), W. I. Immon, Wiley Publishing, 2005
– Big Data: A Very Short Introduction, Dawn Holmes, Oxford, 2017
– The Design and Implementation of Modern Colum-Oriented Database Systems, Daniel Abadi, Peter Boncz, Stavros Harizopoulos, Stratos Idreos, Samuel Madden, 2013
– What’s Really New with NewSQL?, A. Pavlo and M. Aslett, ACM SIGMOD Record, Vol. 45, No. 2, pages 45-55, June 2016
– Column-Oriented Database Systems (slides), Stavros Harizopoulos, Daniel Abadi, and Peter Boncz, VLDB 2009 Tutorial, http://nms.csail.mit.edu/~stavros/pubs/tutorial2009-column_stores.pdf
– Graph Databases (2nd Edition), Ian Robinson, Jim Webber, and Emil Eifrem, O’Reilly Media, 2015
– Big Data Management and NoSQL Databases – Lecture 7. Column-family stores (slides), Irena Holubova, https://www.ksi.mff.cuni.cz/~svoboda/courses/2015-1-NDBI040/lectures/Lecture-07-Column.pdf
– Tutorial by Jeffrey Heer on Text Visualization (CSR 512 – Data Visualization), University of Washington
– Introduction to Time Series Mining (slides), Keogh Eamonn
– Temporal Data Mining, Theophano Mitsa, Taylor & Francis Ltd, 2010
– Apache Hadoop Online Documentation, Pig Latin Basics, https://pig.apache.org/docs/latest/basic.html
– Hadoop Platform and Application Framework – Tutorial offered on Coursera by the University of California San Diego
– MongoDB 4 Quick Start Guide, Doug Bierer, Packt Publishing Ltd, 2018
– Mastering MongoDB 3.x, Alex Giamas, Packt Publishing, 2017
– MongoDB Architecture Guide, MongoDB, Inc., http://s3.amazonaws.com/info-mongodb-com/MongoDB_Architecture_Guide.pdf
– MongoDB Data Modeling, Wilson da Rocha França, Packt Publishing Ltd, 2015
In addition, the course aims at providing competences about techniques and tools for big data management and analysis. A special attention will be given to data warehousing, data mining, and other methods and tools specific for big data, such as the MapReduce paradigm, time series and text analytics.
At the end of the course, the student will be able to evaluate and tune the performance of a database, will have learned the concepts and methodologies for the configuration of distributed databases, and for the analysis of small and big data.
1.1. Knowledge and understanding
– Parallel and distributed database system architectures.
– Data partitioning and replication in parallel and distributed systems.
– Centralized and distributed query processing and optimization.
– Alternative data model (with respect to the relational paradigm) for semi-structured and unstructured data.
– Features of new generation (NoSQL, NewSQL) systems.
1.2. Applying knowledge and understanding
– Techniques and tools for small and big data analysis and visualization (e.g., R and RStudio).
– Optimization techniques for performance improvement in relational systems.
– Data processing in non-relational systems (e.g. XML and MapReduce).
Cross-sectoral skills/soft skills
2.1. Making judgments
– Choose the correct techniques and the appropriate tools to carry out data analyses.
– Interpret the experimental results of the analysis and draw effective conclusions relevant to the domain of discourse.
– Determine the most suitable (centralized, parallel, distributed, relational or non-relational) architecture for a specific data management problem.
– Implement the best strategies to improve the query performance.
2.2. Communication skills
– Communicate using the technical lexicon of database systems.
– Communicate using the terminology of parallel and distributed systems.
– Communicate with the (technical and non-technical) stakeholders involved in the process of design, implementation, and use of a database system (e.g., communicate effectively the results of the analysis).
2.3. Learning skills
– Learn to optimize a (possibly parallel or distributed) data management system.
– Learn to choose a sufficiently rich row data set, to analyze the data to extract meaningful information, to draw and to communicate conclusions.
The students will learn, and practice, advanced query processing techniques for relational databases. They will also be introduced to the basic elements of distributed database management systems that play a fundamental role in the management of big data.
Data analysis and big data.
The students will learn, and practice, the main techniques and tools for data analysis and big data management. A special attention will be given to practical use cases, data warehousing, MapReduce paradigm, time series, and text analytics.
– Database System Concepts (7th Edition), Silberschatz, Korth, and Sudarshan, McGraw-Hill, 2020
– Readings in Database Systems (online, http://www.redbook.io)
– Principles of Distributed Database Systems (3rd Edition), Özsu and Valduriez, Springer, 2011
– Data Warehouse Systems – Design and Implementation, A. Vaisman, E. Zimányi, Springer, 2014
– Business Analytics: A Contemporary Approach, Thomas Jackson, Steven Lockwood, WHSmith, 2018
– SQL & NoSQL Databases – Models, Languages, Consistency Options and Architectures for Big Data Management, Andreas Meier, Michael Kaufmann, Springer, 2019
– Text Mining: Concepts, Implementation, and Big Data Challenge (1st Edition), Taeho Jo, Springer, 2019
– Temporal Data Mining, Theophano Mitsa, CRC Press, 2010.
– Hadoop: The Definitive Guide (4th Edition), Tom White, O’Reilly, 2015.
– The MongoDB 4.2 Manual, MongoDB, Inc., https://docs.mongodb.com/manual/