AwareCast 2013

New Call for Posters, Demos, and Videos!

Please note, we have a new Call for Posters, Demos, and Videos online.

Topics

Context prediction breaks the border from reaction on past and present stimuli to proactive anticipation of actions. Research directions spread from applications for context prediction over event prediction, architectures for context prediction, data formats, and algorithms. Recent work focuses on three main challenges:

Prediction beyond location
Benchmarks and common data sets
Common development frameworks

While there have been contributions targeting some of these challenges, we still see them as unsolved. Thus we invite unique contribution addressing these challenges and provide a forum to facilitate collaboration among research groups focusing on context prediction.

Topics of interest include, but are not limited to:

Accurate prediction of seldom events

Important events are frequently also seldom events. How can we train a system on events which are not likely covered by training data sets?

Identification of actions and situations suitable for context prediction

User behaviour is noisy and not necessarily contains patterns which can be predicted. In particular, predictable patterns are frequently interleaved with non-predictable patterns. Inherently, the underlying (stochastic?) process has to feature some regularity or trends.

Continuous learning

User behaviour and habit changes over time. To guarantee constant accuracy, the approach must be able to ‘forget’ patterns which grow unimportant.

Development frameworks

To pave the way for a broader use of context prediction in applications, robust and easy to use frameworks are in need. These frameworks should simplify the development of context prediction applications and preferably be available as open source.

Novel applications

As discussed above, research on context prediction used to focus heavily on location prediction. While contributions dealing with location prediction are welcome, when they address at least one of the other topics, we like to see novel application of context prediction.

Multi-User and Multi-Sensor Prediction

Since humans tend to behave similar, the context time series of other users may be helpful to increase the accuracy of context prediction for similar users. Additionally the utilization of multiple sensors may affect the robustness of the prediction approaches.

Data sets and benchmarks

Currently, comprehensive data-sets are created for context-computing. However, these data-sets are hardly sufficient to be applied for context prediction applications. In particular, data has to be sampled over longer time-spans and cover stochastic processes which are inherently predictable.

Privacy and trust

Shared time series but also the fact that context time series might cover events and actions of remote entities rises questions of privacy and trust.

Technical Program

Technical Program ...

Attending

Registration is managed by the Ubicomp 2013 registration chair. More ...

Program Committee

Christos Anagnostopoulos, Ionian University
Martin Atzmueller, University of Kassel
Sebastian Bader, Rostock University
Dirk Bade, University of Hamburg
Christian Becker, University of Mannheim
Michael Beigl, KIT
Diane Cook, Washington State University
Anind Dey, CMU
Tristan Henderson, University of St Andrews
Teddy Mantoro, University of Technology Malaysia
Mirco Musolesi, University of Birmingham
Andrei Popleteev, Create-Net
Andreas Riener, Johannes Kepler University Linz
Nirmalya Roy, Washington State University
Kristof Van Laerhoven, TU Darmstadt
Arkady Zaslavsky, CSIRO
Mi Zhang, University of Southern California

Important Dates

Early Registration: On or before July 12th
Late Registration: July 13th – Sep. 5th
Workshop: September 8, 2013

UbiComp 2013

The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013) is the result of a merger of the two most renown conferences in the field: Pervasive and UbiComp. More ...

Workshop Chairs

Klaus David, University of Kassel, Germany
Bernd Niklas Klein, IdE Kassel, Germany
Sian Lun Lau, Sunway University, Malaysia
Stephan Sigg, NII, Tokyo, Japan
Brian Ziebart, University of Illinoi, USA