SenseML 2015 - 2nd International Workshop on Machine Learning for Urban Sensor Data (SenseML 2015)

2nd International ACM SenSys 2015 Workshop
on
*Machine Learning for Urban Sensor Data*
(SenseML 2015)
https://www.tk.informatik.tu-darmstadt.de/en/sense...
Nov. 1, 2015 - Seoul, Korea
*** Paper submission deadline: Friday, July 31th, 2015 ***
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Over a decade, sensor research has proven the use of sensor networks for different use cases. The research has mostly focused on aspects of sensor network deployment, energy-efficiency, and wireless networking. Today, the focus is shifting from “How do we collect data?” to “What can we learn from the data and how do the models look like?” as more and more data becomes available. Especially urban applications ? from people and car movement to building and environmental sensing ? has been a constant driver of innovation over the past years, delivering an ever increasing amount of useful data. Even more data comes from participatory sensing as everyone collects data all the time. However, most of the data remains unused as the full potential of machine learning and data mining is yet to be utilized in all its strength.
Moreover, the challenges that come with urban sensor data for the design and application of learning algorithms are also not coped with sufficiently yet. Consequently, algorithms that are suited to the special needs this kind of data imposes such as missing values, unreliable measurements, missing calibration or high spatial diversity still need to be developed. Also, the architecture of systems, i.e., the complete data analysis pipeline from the collection of measurements to the final model is of interest.
Bringing together the sensor systems expertise from the Wireless Sensor Networks and Sensor Systems community with the knowledge available in the machine learning community, will open up a whole new set of applications and technical questions. SenseML will, therefore, promote work that on one hand concentrates on generating high quality data set, and on the other hand on applying or developing state of the art machine learning algorithms to yield highly accurate models. A special focus also lies on interpretable models such as rule sets or decision trees. Especially for sensor data, having interpretable models is of interest for discovering potential relationships in the sensor network. Proceeding this way, we expect to gain unique insights into sensor data and ideas for novel applications.
The workshop considers hot topics of both disciplines, novel ideas, in-progress work on system architecture (e.g., the data analysis pipeline), enabling technologies, and emerging applications.
TOPICS OF INTEREST
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Topics of interest include, but are not limited to:
* Real-time machine learning
* Iterative machine learning
* Multi-target learning
* Generating data analysis pipelines
* Evaluation of machine learning models tailored to sensor data
* Data extraction from sensor networks
* Data conversion and calibration issues
* Meta-learning, e.g., learning to adjust the analysis pipeline automatically
* Interpretable models, e.g., Rule Learning or Decision Tree Learning
* Generating high-quality data sets
* Data quality issues
* Dealing with missing and low quality data
* Feature Engineering with a focus on sensor data features
* Feature weighting and combination
* Generating high-quality features from sensor data
WORKSHOP ORGANIZERS
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* Frederik Janssen, Knowledge Engineering Group, Technische Universität Darmstadt, Germany (janssen-AT-ke.tu-darmstadt.de)
* Immanuel Schweizer, Telecooperation Group, Technische Universität Darmstadt, Germany (schweizer-AT-cs.tu-darmstadt.de)
PROGRAM COMMITTEE
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* Emiliano Miluzzi (Apio Systems, USA)
* Kristian Kersting (TU Dortmund, Dortmund, Germany)
* Geoffrey Challen (University at Buffalo, USA)
* Heiko Paulheim (University of Mannheim, Germany)
* Martina Brachmann (TU Dresden, Germany)
* Nico Piatkowski (TU Dortmund, Dortmund, Germany)
* Robert Jäschke (Leibniz Universität Hannover, Germany)
* Sebastian Kauschke (TU Darmstadt, Germany)
* Andrei Tolstikov (TU Darmstadt, Germany)
* Petar Ristoski (University of Mannheim, Germany)
SUBMISSIONS AND STYLE
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We invite two types of submissions for this workshop:
* Full papers (Up to 8 pages)
* Short papers (Up to 4 pages)
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).
Submissions must be formatted as follows: 8.5” x 11” pages, including figures, tables, in two-column format, using 10-point type on 12-point (single-spaced) leading, with a maximum text block of 7” wide x 9” deep with an inter-column spacing of .25”. The ACM conference templates can be found here: https://www.acm.org/sigs/publications/proceedings-... (please use Option 2).
Papers should be submitted through Easychair: https://easychair.org/conferences/?conf=senseml201....
If you have any further question please contact the SenseML Organizers.
More details can be found on the workshop website:
https://www.tk.informatik.tu-darmstadt.de/en/sense...
IMPORTANT DATES
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* Paper Submission Deadline: Friday, July 31st, 2015
* Notification of Acceptance: Tuesday, September 1st, 2015
* Final Version: Tuesday, September 8th, 2015
* SenSys conference: Sunday, November 1st, 2015 - Wednesday, November 4th, 2015
* Workshop SenseML: Sunday, November 1st, 2015