LQS 2014 - Workshop on Linking The Quantified Self at Hypertext 2014

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.
Call for participation
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.
Other topics of interest regard privacy and security issues for the data gathered, since users perceive QS data as extremely private and are constantly worried about their final destination. Both aspects are of particular importance when very sensitive information is recorded, such as biometrical data or locations. Besides, Information visualization techniques actually used, for example, for visualizing social data, could suggest new ways for displaying behavioral data in meaningful manner. Suggestions for interacting in new ways with Intelligent Objects that are intertwined through the Internet of Things and are embedded with data gathering functions are also of interest for the workshop.
Topics
Case studies, position papers, future research challenges, reflections in other domains such as Ubiquitous computing, Ambient intelligence, Cyber-Physical Systems are welcome.
Relevant workshop topics include but are not limited to:
(Long-term) User Modeling
Semantic web
Web of Things
Information visualization
Privacy and security
Interoperability
Semantics for reusing
Sharing of data
User interaction with linked things
Ubiquitous computing
Lifelogging
Organizers
Amon Rapp Università di Torino
Frank Hopfgartner Technische Universität Berlin
Till Plumbaum Technische Universität Berlin
Bob Kummerfeld University of Sydney
Judy Kay University of Sydney
Eelco Herder L3S Research Center Hannover