Hidden proxy per comunicazioni seamless da dispositivi multi-homed

Il concetto di mobilita' delle persone, assieme alla sempre maggior dipendenza di queste ai servizi offerti da Internet,ha fatto nascere la necessita' di rimanere connessi ad Internet indipendentemente dai propri spostamenti, mantenendo inalterate le prestazioni delle connessioni. HPforMSC, l'oggetto di questa tesi, e' un'architettura basata su hidden proxy che permette in maniera del tutto trasparente all'utente di utilizzare contemporaneamente le varie interfacce di rete a disposizione mantenendo inalterate le performance di tutte le comunicazioni in essere durante un handover, sia esso orizzontale o verticale.

Modellazione e sviluppo di un web desktop mediante Google Web Toolkit

Questa tesi descrive la progettazione e lo sviluppo di un prototipo di web desktop con la tecnologia Google Web Toolkit, presso Wincor Nixdorf Retail Consulting srl.

Frame-driven Extraction of Linked Data and Ontologies from Text

Ontology design and population -core aspects of semantic technologies- re- cently have become fields of great interest due to the increasing need of domain-specific knowledge bases that can boost the use of Semantic Web. For building such knowledge resources, the state of the art tools for ontology design require a lot of human work. Producing meaningful schemas and populating them with domain-specific data is in fact a very difficult and time-consuming task. Even more if the task consists in modelling knowledge at a web scale. The primary aim of this work is to investigate a novel and flexible method- ology for automatically learning ontology from textual data, lightening the human workload required for conceptualizing domain-specific knowledge and populating an extracted schema with real data, speeding up the whole ontology production process. Here computational linguistics plays a fundamental role, from automati- cally identifying facts from natural language and extracting frame of relations among recognized entities, to producing linked data with which extending existing knowledge bases or creating new ones. In the state of the art, automatic ontology learning systems are mainly based on plain-pipelined linguistics classifiers performing tasks such as Named Entity recognition, Entity resolution, Taxonomy and Relation extraction [11]. These approaches present some weaknesses, specially in capturing struc- tures through which the meaning of complex concepts is expressed [24]. Humans, in fact, tend to organize knowledge in well-defined patterns, which include participant entities and meaningful relations linking entities with each other. In literature, these structures have been called Semantic Frames by Fill- 6 Introduction more [20], or more recently as Knowledge Patterns [23]. Some NLP studies has recently shown the possibility of performing more accurate deep parsing with the ability of logically understanding the structure of discourse [7]. In this work, some of these technologies have been investigated and em- ployed to produce accurate ontology schemas. The long-term goal is to collect large amounts of semantically structured information from the web of crowds, through an automated process, in order to identify and investigate the cognitive patterns used by human to organize their knowledge.