Personal Information Management Systems (PIMSs) enable the individuals to be the holders of their own personal information and provide them support to retrieve and organize it in a secure way. LifeBook is a PIMS running on the mobile device which manages in secure way information concerning events captured by all the user’s devices. To this aim, we present the LifeBook Security Model to protect user data stored on the cloud. Events are classified considering both the user’s context and their similarity w.r.t. other user events. Similarity is computed by considering content, location, time, and the type of the given event. LifeBook offers a re-find feature for searching and visualizing content already seen in the past, content of which the user remembers aspects such as the time and/or the place she was when the event occurred. The relationships among the user events are adaptively created by a process to extract characteristic information on the user habits based on the Principal Component Analysis (PCA).

A Multi-device Cloud-Based Personal Event Management System

Francese R.;Risi M.
;
Tortora G.
2019-01-01

Abstract

Personal Information Management Systems (PIMSs) enable the individuals to be the holders of their own personal information and provide them support to retrieve and organize it in a secure way. LifeBook is a PIMS running on the mobile device which manages in secure way information concerning events captured by all the user’s devices. To this aim, we present the LifeBook Security Model to protect user data stored on the cloud. Events are classified considering both the user’s context and their similarity w.r.t. other user events. Similarity is computed by considering content, location, time, and the type of the given event. LifeBook offers a re-find feature for searching and visualizing content already seen in the past, content of which the user remembers aspects such as the time and/or the place she was when the event occurred. The relationships among the user events are adaptively created by a process to extract characteristic information on the user habits based on the Principal Component Analysis (PCA).
2019
978-3-030-19222-8
978-3-030-19223-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4743232
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