Dissertation topics suggested by the teacher.
The Intentional Analytics Model (IAM) has been envisioned as a way to tightly couple OLAP and analytics. The IAM approach relies on two major cornerstones: (i) the user explores the data space by expressing her analysis intentions rather than by explicitly stating what data she needs, and (ii) in return she receives both multidimensional data and knowledge insights in the form of annotations of interesting subsets of data.
As to (i), five intention operators are proposed, namely, describe, assess, explain, predict, and suggest. As to (ii), first-class citizens of the IAM are enhanced cubes, defined as multidimensional cubes coupled with highlights, i.e., sets of cube cells associated with interesting components of models extracted from cube.
The goal of the thesis is to provide a prototypical implementation of the explain and predict operators.
Recent dissertations supervised by the teacher.
Second cycle degree programmes dissertations
- Data-driven decision making: analisi delle vendite in Marposs Spa con PowerBI
- Migrazione di un data warehouse in ambiente cloud multi-tenant: benchmarking e proof-of-concept
- Migrazione sul cloud della Business
Intelligence: il caso di studio di
un’azienda del settore imballaggio
- Ottimizzare i processi decisionali con la Business Intelligence: Un approccio con Sap Analytics Cloud
- Progettazione e sviluppo della Proof of Concept di un data warehouse per una PMI italiana
- Raffinamento di schemi concettuali multidimensionali tramite uso di LLM
- Selezione di un data catalog per l'azienda Bonfiglioli S.p.A.
- Self-Service Business Intelligence e Data Catalog nel settore metalmeccanico: il caso Bonfiglioli
- Sviluppo di una data platform innovativa per un'azienda leader nel settore farmaceutico
- Un framework GenAI per l'interrogazione di metadati in ambito bancario: sperimentazione della tecnica RAG
