Progettazione e validazione di un framework di algoritmi ensemble per la classificazione di Open Data IoT

La quantità di dati IoT liberamente accessibili in rete - comunemente chiamati Open Data - è potenzialmente di grande utilità per innumerevoli applicazioni pratiche. Tuttavia, tali dati sono spesso inutilizzabili o incomprensibili, al punto in cui talvolta non si riesce nemmeno a discernere la tipologia di osservazione effettuata. Per etichettare tali misurazioni è dunque necessaria l’applicazione di modelli di classificazione. Questo tuttavia non è un lavoro semplice, in quanto i dati open sono in generale molto eterogenei, per cui molti degli algoritmi comunemente usati in letteratura hanno difficoltà a classificarli correttamente. Il contributo maggiore di questa tesi è perciò la presentazione di MACE, un framework ensemble per la classificazione di Open Data IoT: dopo averne trattato progettazione ed implementazione, ne valuteremo le performance, dimostrando la sua efficacia nel risolvere quello che è, ad oggi, un problema decisamente trascurato dalla letteratura

The role of semantic web technologies for IoT data in underpinning environmental science

The advent of Internet of Things (IoT) technology has the potential to generate a huge amount of heterogeneous data at different geographical locations and with various temporal resolutions in environmental science. In many other areas of IoT deployment, volume and velocity dominate, however in environmental science, the more general pattern is quite distinct and often variety dominates. There exists a large number of small, heterogeneous and potentially complex datasets and the key challenge is to understand the interdependencies between these disparate datasets representing different environmental facets. These characteristics pose several data challenges including data interpretation, interoperability and integration, to name but a few, and there is a pressing need to address these challenges. The author postulates that Semantic Web technologies and associated techniques have the potential to address the aforementioned data challenges and support environmental science. The main goal of this thesis is to examine the potential role of Semantic Web technologies in making sense of such complex and heterogeneous environmental data in all its complexity. The thesis explores the state-of-the-art in the use of such technologies in the context of environmental science. After an in-depth assessment of related work, the thesis further examined the characteristics of environmental data through semi-structured interviews with leading experts. Through this, three key research challenges emerge: discovering interdependencies between disparate datasets, geospatial data integration and reasoning, and data heterogeneity. In response to these challenges, an ontology was developed that semantically enriches all sensor measurements stemmed from an experimental Environmental IoT infrastructure. The resultant ontology was evaluated through three real-world use-cases derived from the interviews. This led to a number of major contributions from this work including: the development of an ontology tailored for streaming environmental data offering semantic enrichment of IoT data, support for spatio-temporal data integration and reasoning, and the analysis of unique characteristics of environmental science around data