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UID:655@lincs.fr
DTSTART;TZID=Europe/Paris:20210708T140000
DTEND;TZID=Europe/Paris:20210708T170000
DTSTAMP:20210713T073708Z
URL:https://www.lincs.fr/events/phd-thesis-defense-alarm-prediction-in-net
 works-via-space-time-pattern-matching-and-machine-learning/
SUMMARY:PhD thesis defense "Alarm prediction in networks via space-time
 pattern matching and machine learning"
DESCRIPTION:Nowadays\, telecommunication networks occupy a predominant
 place in our world. Indeed\, they allow to share worldwide a huge amount of
 information. Networks are however complex systems\, both in size and
 technological diversity. Therefore\, it makes their management and repair
 more difficult. In order to limit the negative impact of such failures\,
 some tools have to be developed to detect a failure whenever as soons as it
 occurs\, analyse its root causes to solve it efficiently\, or even predict
 this failure to prevent it rather than cure&nbsp\;it. In this thesis\, we
 mainly focus on these last two problems. To do so\, we use files\, called
 alarm logs\, storing all the alarms issued by the system. However\, these
 files are generally noisy and verbose: an operator managing a network needs
 tools able to extract and handle in an interpretable manner the causal
 relationships within a log. First\, we build online a structure\, called
 DIG-DAG\, that stores all the potential causal relationships involving the
 events of a log. We then propose a query system to exploit this structure.
 Finally\, we apply this approach in the&nbsp\;context of root cause
 analysis. Second\, we discuss a generative approach for times&nbsp\;series
 prediction. In particular\, we compare two well-known models for this task:
 recurrent neural nets on the one hand\, hidden Markov models on the other
 hand. Indeed\, in their respective communities\, these two models are state
 of the art. Here\, we compare analytically their expressivity by
 encompassing them into a probabilistic model\, called GUM.
CATEGORIES:PhD Defense,Youtube
LOCATION:LINCS + Zoom\, 23 avenue d'Italie\, Paris\, 75013\, France
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 Paris\, 75013\, France;X-APPLE-RADIUS=100;X-TITLE=LINCS + Zoom:geo:0,0
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TZID:Europe/Paris
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DTSTART:20210328T030000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
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