Varvara Kalokyri

Varvara Kalokyri, Alexander Borgida, Amélie Marian

Numerous applications capture in digital form aspects of people's lives. The resulting data, which we call Personal Digital Traces - PDTs, can be used to help reconstruct people's episodic memories and connect to their past personal events. This reconstruction has several applications, from helping patients with neurodegenerative diseases recall past events to gathering clues from multiple sources to identify recent contacts and places visited - a critical new application for the current health crisis. This paper takes steps towards integrating, connecting and summarizing the heterogeneous collection of data into episodic narratives using scripts - prototypical plans for everyday activities. Specifically, we propose a matching algorithm that groups several digital traces from many different sources into script instances (episodes), and we provide a technique for ranking the likelihood of candidate episodes. We report on the results of a study based on the personal data of real users, which gives evidence that our episode reconstruction technique 1) successfully integrates and combines traces from different sources into coherent episodes, and 2) augments users' memory of their past actions.

Daniela Vianna, Varvara Kalokyri, Alexander Borgida et al.

Digital traces of our lives are now constantly produced by various connected devices, internet services and interactions. Our actions result in a multitude of heterogeneous data objects, or traces, kept in various locations in the cloud or on local devices. Users have very few tools to organize, understand, and search the digital traces they produce. We propose a simple but flexible data model to aggregate, organize, and find personal information within a collection of a user's personal digital traces. Our model uses as basic dimensions the six questions: what, when, where, who, why, and how. These natural questions model universal aspects of a personal data collection and serve as unifying features of each personal data object, regardless of its source. We propose indexing and search techniques to aid users in searching for their past information in their unified personal digital data sets using our model. Experiments performed over real user data from a variety of data sources such as Facebook, Dropbox, and Gmail show that our approach significantly improves search accuracy when compared with traditional search tools.