Quantified Self (QS), also known as Personal Informatics (PI), is a school of thought that aims to use technology for acquiring and collecting data on different aspects of the daily lives of people. These data can be internal states (such as mood or glucose level in the blood) or indicators of performance (such as the kilometers run). The purpose of collecting these data is self-‐monitoring, performed in order to gain self-‐knowledge or some kind of change or improvement (behavioral, psychological, therapeutic, etc.). Although the current spread on the market of these kinds of tools, many issues arise when we consider their usage in the daily lives of common people, such as the meaningfulness and utility of the gathered data for the final users. We can think to address some of these issues looking beyond the Quantified Self for finding new technologies and design techniques that could be applied to this field. One of the main challenges of self-‐tracking data is that it comes in heterogeneous and often very unstructured form. One of the possible ways is leveraging Semantic Web techniques for integrating heterogeneous data originated from different devices and applications and give them some kind of structure. In Quantified Self, in fact, the information gathered by QS tools are scattered in autonomous silos, that can hardly be meshed together in order to provide users a complete and satisfying mirror of their behaviors and physical or psychological states. Besides, often QS tools simply juxtapose different data in their visualizations but they are not able to highlight meaningful correlations and provide structures for the data gathered. Given that the quantified-‐self trend is just gaining momentum, it is not unlikely that we will soon have more and more users who create their own personal repositories, also referred to lifelogs. Structuring the data in these lifelogs is of particular importance in the context of user modeling. User Modeling techniques can provide useful insights for reasoning on data gathered, since users are not only in search of the possibility to visualize their behavioral data, but also to receive useful suggestions for improving their habits and behavior. Although QS tools have at their disposal huge amount of data on user behavior, they are not currently exploiting them for modeling users and providing them personalized recommendations. In this workshop we tried to investigate challenges, open issues and new perspectives related to the dominion of data employed in Quantified Self and Personal Informatics technologies. The workshop organizers:
Frank Hopfgartner, Technische Universität Berlin Till Plumbaum, Technische Universität Berlin Judy Kay, University of Sydney Bob Kummerfeld, University of Sydney Eelco Herder, L3S Research Center Hannover Program (accepted papers)
Program Committee Federica Cena, Silvia Likavec, Amon Rapp, Martina Deplano and Alessandro Marcengo. Ontologies for Quantified Self: a semantic approach Faisal Alquaddoomi, Cameron Ketcham, Deborah Estrin. The Email Analysis Framework: Aiding the Analysis of Personal Natural Language Texts Timothy Wayne Cook and Luciana Tricai Cavalini. A Multilevel-‐Model Driven Social Network for Healthcare Information Exchange.