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Internship : Fault event detection through causal analysis of binary time seriesIn general, RCA is a hard problem in complex systems, because it requires a deep knowledge of cause-effect dependencies among many features, physical and logical components the network nodes.
In the data driven approach, where most of this knowledge is assumed to be unavailable a priori, a major difficulty can emanate from the fact that many of the variables are hidden or unknown.
Furthermore, even in a fully observable system we are faced with the combinatorial explosion of cause-and-effect dependencies and the difficulty to collect enough information for distinguishing causal dependencies from spurious correlations.
The goal of this project is to explore techniques for causal analysis of binary (or discrete-valued, in a more general case) time series that represent local state change sequences of monitored network resources.
We aim at deriving a causal graph that explains the relationships between these time series, whereas the occurrence of an original (source or root) fault can be considered as an external intervention into the system on a particular network resource that can causally propagate to other resources.
More specifically, given as input a set of time series, our objectives is1.detection of fault occurrences helped by the inference of high-level causal graphs before and after these external interventions,2.
Study of different data generating models such as Noisy-OR or dynamic Bayesian networks, structural equation models adapted to time-series, or models derived from formal methods (event generating models based on connected automata on Petri nets)3.
Experimentations with the synthetic data as well as with real alarm logs collected in operator networks.The internship will be co-advised by Armen Aghasaryan (Nokia Bell Labs, Paris-Saclay) and Gregor Gössler (INRIA Grenoble).
Candidates must have a strong interest in formal methods (concurrency theory, Bayesian networks) as well as have good skills for data intensive simulation and analysis.
The trainee will benefit from a large degree of autonomy regarding the evaluation and interpretation of results as well as the tuning of the algorithm.
Outstanding work performed by the intern may lead to co-authorship in publications and can also be pursued in a PhD thesis.
Duration 6 months (full time)Qualifications : Last year Master-level student (final project). Solid technical skills and background in at least some of the followingareas are required :
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