13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Probabilistic graphical models for scalable data analytics

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Today, omnipresent sensors are continuously providing streaming data on the environments in which they operate. For instance, a typical monitoring and analysis system may use streaming data generated by sensors to monitor the status of a particular device.

Analysis and monitoring systems should be designed to make predictions about the future behaviour of the device, or diagnostically infer the most likely system configuration that has produced the observed data. Sources of streaming data with even a modest updating frequency can produce extremely large volumes of data, thereby making efficient and accurate data analysis and prediction difficult. This calls for scalable data analytics. From the point of view of inference and learning from massive data streams, there have been advances consisting of scaling up existing methods for batch data as well as methods for adapting to the continuous arrival of new data. However, data stream processing is still a highly challenging problem. One of the main lines where research is needed is related to handling uncertainty in data, where principled methods and algorithms for dealing with uncertainty in massive data applications are required.

Probabilistic graphical models (PGMs) provide a well-founded and principled approach
for performing inference and belief updating in complex domains endowed with uncertainty.

This special session welcomes contributions aimed at enabling PGMs as a key tool for
scalable data analytics. We welcome theoretical and applied contributions related to
the following topics:

  • Scalable PGM inference and learning.
  • Learning PGMs from data streams.
  • Inference and learning in Dynamic models.
  • Scalable algorithms for classification and regression based on PGMs.
  • Applications involving data streams.
  • Parallel / distributed algorithms.


  • Helge Langseth. Norwegian University of Science and Technology (Trondheim, Norway).
  • Anders L. Madsen. Hugin Expert A/S and Aalborg University (Aalborg, Denmark).
  • Thomas D. Nielsen. Aalborg University (Aalborg, Denmark).
  • Antonio Salmerón. University of Almería (Almería, Spain).

The four organizers conform the Project Science Review Group of the EU-FP7 project
"AMIDST- Analysis of massive data streams" ( that deals with
scalable algorithms based on PGMs.


Papers to the special session should be submitted through the general paper submission
website as regular submissions. During submission, authors submitting to the special
session should check the corresponding category. Papers submitted to the special session
will undergo the same review and decision process as regular papers.

The submission website is


Submission and decision dates are the same as regular submissions:

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