Abstract

Abstract

In the era of Big Data, every single user of our hyper-connected world leaves behind a myriad of digital breadcrumbs while performing her daily activities. It is sufficient to think of a simple smartphone that enables each one of us to browse the Web, listen to music on online musical services, post messages on social networks, perform online shopping sessions, acquire images and videos and record our geographical locations. This enormous amount of personal data could be exploited to improve the lifestyle of each individual by extracting, analyzing and exploiting user's behavioral patterns like the items frequently purchased, the routinary movements, the favorite sequence of songs listened, etc. However, even though some user-centric models for data management named Personal Data Store are emerging, currently there is still a significant lack in terms of algorithms and models specifically designed to extract and capture knowledge from personal data. This thesis proposes an extension to the idea of Personal Data Store through Personal Data Analytics. In practice, we describe parameter-free algorithms that do not need to be tuned by experts and are able to automatically extract the patterns from the user's data. We define personal data models to characterize the user profile which are able to capture and collect the users' behavioral patterns. In addition, we propose individual and collective services exploiting the knowledge extracted with Personal Data Analytics algorithm and models. The services are provided for the users which are organized in a Personal Data Ecosystem in form of a peer distributed network, and are available to share part of their own patterns as a return of the service providing. We show how the sharing with the collectivity enables or improves, the services analyzed. The sharing enhances the level of the service for individuals, for example by providing to the user an invaluable opportunity for having a better perception of her self-awareness. Moreover, at the same time, knowledge sharing can lead to forms of collective gain, like the reduction of the number of circulating cars. To prove the feasibility of Personal Data Analytics in terms of algorithms, models and services proposed we report an extensive experimentation on real world data.