Many problems from the real life deal with the generation of enormous, varied, dynamic, and interconnected datasets coming from different and heterogeneous sources. This PhD Thesis focuses on the proposal of novel knowledge extraction techniques from graphs, mainly based on Big Data methodologies. Two application contexts are considered: Biological and Medical data, with the final aim of identifying biomarkers for diagnosis, treatment, prognosis, and prevention of diseases. Social data, for the optimization of advertising campaigns, the comparison of user profiles, and neighborhood analysis.
Bonomo M (2022). Knowledge Extraction from Biological and Social Graphs. In S. Chiusano, T. Cerquitelli, R. Wrembel, K. Nørvåg, B. Catania, G. Vargas-Solar, et al. (a cura di), New Trends in Database and Information Systems (pp. 648-656). Springer [10.1007/978-3-031-15743-1_60].
Knowledge Extraction from Biological and Social Graphs
Bonomo M
Primo
2022-08-29
Abstract
Many problems from the real life deal with the generation of enormous, varied, dynamic, and interconnected datasets coming from different and heterogeneous sources. This PhD Thesis focuses on the proposal of novel knowledge extraction techniques from graphs, mainly based on Big Data methodologies. Two application contexts are considered: Biological and Medical data, with the final aim of identifying biomarkers for diagnosis, treatment, prognosis, and prevention of diseases. Social data, for the optimization of advertising campaigns, the comparison of user profiles, and neighborhood analysis.
| File | Dimensione | Formato | |
|---|---|---|---|
|
ADBIS 2022 paper.pdf
Solo gestori archvio
Descrizione: formato stampato
Tipologia:
Versione Editoriale
Dimensione
5.35 MB
Formato
Adobe PDF
|
5.35 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
