As the focus in the Wireless Sensor Networks and Sensor Systems community is shifting from “How do we collect data?” to “What can we learn from the data and how do the models look like?” we want to bring researchers from this community and the Machine Learning community together. Working with sensor data, machine learning methods become more and more popular (e.g., at the ACM SenSys conference – the major conference in this area – in 2013 the First International Workshop on Sensing and Big Data Mining (SenseMine) took place).
As the applications for machine learning expand into other areas, the need for high-quality machine learning methods constantly grows. Additionally, there is a need for interpretable models as researchers want to grasp the models and get a sense of how the sensor information is combined in the model.
However, sensor data poses a number of unique challenges for machine learning. Ranging from missing values, unreliable measurements, missing calibration to high spatial diversity. Most challenges have not been addressed with a focus on real-world sensor data. It is our belief that a discussion will help foster new results in the intersection of both communities.
|9:00 - 10:30||Welcome & Session 1|
|9:00 - 9:15||Welcome Notes|
|9:15 - 10:00||I. Schweizer: (Keynote) Why SenseML? (Slides)|
|10:00 - 10:30||F. Seraj, B. van der Zwaag, A. Dilo, T. Luarasi & P. Havinga: RoADS: A road pavement monitoring system for anomaly detection using smart phones (PDF) (Slides)|
|10:30 - 11:00||Coffee Break|
|11:00 - 12:30||Joint Session with MUSE Workshop and Discussion|
|11:00 - 11:30||M. Poussevin, N. Baskiotis, V. Guigue & P. Gallinari: Mining ticketing logs for usage characterization with nonnegative matrix factorization (SenseML) (PDF) (Slides)|
|11:30 - 12:00||A Latent Space Analysis of Editor Lifecycles in Wikipedia (MUSE)|
|12:00 - 12:30||Discussion|
|12:30 - 14:00||Lunch|
|14:00 - 15:30||Session 2|
|14:00 - 14:15||J. Yang & L. Meng: Feature Engineering for Map Matching of Low-Sampling-Rate GPS Trajectories on Road Networks (PDF) (Slides)|
|14:15 - 14:30||M. Bouuaert, T. Neutens, N. van de Weghe & B. De Baets: Predictive Modelling of Spatio-Temporal Sensor Network Data using Markov Random Fields (PDF) (Slides)|
|14:30 - 15:00||R. Cardell-Oliver: A Habit Discovery Algorithm for Mining Temporal Recurrence Patterns in Metered Consumption Data (PDF) (Slides)|
|15:00 - 15:30||T. van Craenendonck, T. Op De Beéck, W. Meert, B. Vanwanseele & J. Davis: Monitoring the Crus for Physical Therapy (PDF) (Slides)|
|15:30 - 16:00||Discussion & Closing|
We invite three types of submissions for this workshop:
Submitted papers will be peer-reviewed and selected on the basis of these reviews. Accepted papers will be presented at the workshop (Based on the number of submissions either as oral presentation or poster session).
The papers must be written in English and formatted according to the Springer LNAI guidelines. Authors instructions and style files can be downloaded here.
Papers should be submitted through Easychair.
September 15th to 19th, 2014 - Nancy, France.
Friday, June 20th, 2014
Notification of Acceptance:
Friday, July 11th, 2014
Final Version Due:
Friday, July 25th, 2014
Monday, September 15th, 2014
The post proceedings of the SenseML 2014 workshop are now available in the Springer LNCS series (LNAI 9546). The book is in conjunction with the 5th International Workshop on Modeling Social Media, MSM 2014 and the 5th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2014.