In every KDD (Knowledge Discovery in Databases) process, the steps of data modeling, data preparation, and the selection of the most adequate technology for the target application context play a key role. Data analytics projects in domains such as Internet of Things (IoT), social networks, ICT service monitoring, require the management of complex and heterogeneous data types including time series, document data, georeferenced data, etc. Beyond relational DBMSs, there are now available NoSQL DBMS technologies (e.g., MongoDB, ElasticSearch, Neo4J) which allow the effective management of these data types, taking into account data characteristics as well as the application and functional context. The objective of the course is to present, for the main NoSQL DBMSs, the different data modeling patterns and their querying languages. Technologies for acquiring heterogeneous data from several sources and the data management in an ETL (Extraction, Transformation, and Loading) process will also be discussed. In the course, the students will learn how to identify the most suitable technologies based on the data characteristics and application domain, and how to define data acquisition and data analysis flows

- Knowledge of the main technological characteristics of NoSQL databases. - Ability to design the conceptual model and define the physical data structures for NoSQL databases. - Knowledge of querying NoSQL databases. - Knowledge of data preparation and processing techniques - Ability to design informative dashboards - Knowledge of databases for AI

Knowledge of the relational model, SQL language and basic programming skills.

- Conceptual and logical modeling in NoSQL databases (2 cfu) - Query languages in NOSQL databases (2 cfu) - Data preparation and processing (0.5 cfu) - Informative dashboard creation (0.5 cfu) - Database for AI (1 cfu) Different database technologies will be explored during the course such as MongoDB and ELK Stack.

The course includes practices on the lecture topics, and in particular design and querying of NoSQL database, The course includes laboratory sessions for hands-on experience on NoSQL database design and query and dashboard creation. Laboratory practices allow experimental activities on the most widespread commercial and open-source products, such as MongoDB, ELK Stack, Kibana

Copies of the slides used during the lectures will be made available. All teaching material is downloadable from the teaching portal. Reference books: - Dan Sullivan, NoSQL for Mere Mortals, Addison-Wesley Professional, 2015 - Kristina Chodorow, Shannon Bradshaw. MongoDB: The Definitive Guide (Powerful and Scalable Data Storage), 3 ed. O'Reilly Media, 2018 - Athick, Banon, 'Getting Started with Elastic Stack 8.0', Packt Publishing, 2022

Slides; Esercizi; Esercitazioni di laboratorio;

Modalità di esame: Elaborato progettuale in gruppo; Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;

Exam: Group project; Computer-based written test in class using POLITO platform;

... Computer-based written test in class using POLITO platform; Group projects; The wriiten test exam includes a mandatory oral part (70% of the overall mark), and the evaluation of the team project assigned during the course (70% of the overall mark). The written test covers all the concepts introduced during the course. The group projects are assigned during the course and they are related to the topics presented in lectures. The final grade is given by weighted average of the two parts (0.8*grade team projects + 0.2*grade oral part). The exam is passed if the grade of the team projects is greater than or equal to 18 and the grade of the oral part is greater than or equal to 18.

Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.