Semantic technologies to support the user-centric analysis of activity data

There is currently a trend in giving access to users of on-line services to their own data. In this paper, we consider in particular the data which is generated from the interaction between a user and an organisation online: activity data as held in websites and Web applications logs. We show how we use semantic technologies including RDF integration of log data, SPARQL and lightweight ontology reasoning to aggregate, integrate and analyse activity data from a user-centric point of view

Semantic monitoring of personal web activity to support the management of trust and privacy

For individual Web users, understanding and controlling their exchange of personal data is a very complex task as they interact, some-times unknowingly, with hundreds of dierent websites. In this paper, we present a set of tools and an experiment dedicated to monitoring a user's Web activity in order to build an observed model of his behavior in terms of the trust given to accessed websites and of the criticality of the data exchanged. By exposing such a model to the users and allowing them to interact with it, we provide ways for users to be better informed about their own behavior with respect to privacy, and ultimately, to better control their own data exchange

Breast cancer management pathways during the COVID-19 pandemic: outcomes from the UK ‘Alert Level 4’ phase of the B-MaP-C study

Abstract: Background: The B-MaP-C study aimed to determine alterations to breast cancer (BC) management during the peak transmission period of the UK COVID-19 pandemic and the potential impact of these treatment decisions. Methods: This was a national cohort study of patients with early BC undergoing multidisciplinary team (MDT)-guided treatment recommendations during the pandemic, designated ‘standard’ or ‘COVID-altered’, in the preoperative, operative and post-operative setting. Findings: Of 3776 patients (from 64 UK units) in the study, 2246 (59%) had ‘COVID-altered’ management. ‘Bridging’ endocrine therapy was used (n = 951) where theatre capacity was reduced. There was increasing access to COVID-19 low-risk theatres during the study period (59%). In line with national guidance, immediate breast reconstruction was avoided (n = 299). Where adjuvant chemotherapy was omitted (n = 81), the median benefit was only 3% (IQR 2–9%) using ‘NHS Predict’. There was the rapid adoption of new evidence-based hypofractionated radiotherapy (n = 781, from 46 units). Only 14 patients (1%) tested positive for SARS-CoV-2 during their treatment journey. Conclusions: The majority of ‘COVID-altered’ management decisions were largely in line with pre-COVID evidence-based guidelines, implying that breast cancer survival outcomes are unlikely to be negatively impacted by the pandemic. However, in this study, the potential impact of delays to BC presentation or diagnosis remains unknown

Personal semantics: personal information management in the web with semantic technologies

Every web user has several online profiles through which personal information is exchanged with many service providers. This exchange of personal information happens at a pace difficult to fully comprehend and manage without a global view and control with obvious consequences on data control, ownership and, of course, privacy. To tackle issues associated with current service-centric approaches, we propose a user-centric architecture where the interaction between a user and other agents is managed based on a global profile for the user, maintained in a profile management system and controlled by the user herself. In this PhD, we will investigate research issues and challenges in realizing such a system based on semantic technologies