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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
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Wed, 22 Apr 2020 11:34:52 +0200
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Marcin Paprzycki <[log in to unmask]>
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FedCSIS 2020 Data Mining Competition

Challenge: Network Device Workload Prediction

FedCSIS 2020 Data Mining Challenge: Network Device Workload Prediction 
is the seventh data mining competition organized in association with 
Conference on Computer Science and Information Systems 
(https://fedcsis.org/).

This time, the considered task is related to the monitoring of large IT 
infrastructures and the estimation of their resource allocation. The 
challenge is sponsored by EMCA Software and Polish Information 
Processing Society (PTI).


************************* COVI-19 Information **************************

We would like to assure everybody that FedCSIS 2020 will take place, the 
submitted and accepted papers will be published and sent for indexations 
as usual. In case the conference cannot be staged in real setting of 
beautiful Sofia, we will provide a virtual web-based avenue for it. We 
look forward to receiving your contributions as usual and guarantee that 
FedCSIS 2020 will be another memorable and rewarding conference.

FedCSIS organizers

************************************************************************


Overview:

EMCA Software is a Polish vendor of Energy Logserver - a system capable 
of collecting data from various log sources to provide in-depth data 
analysis and alerting to its end-users. EMCA is based in Poland but also 
operates in Nordics, APAC, and the USA, through the partner channels. 
The company focuses on cybersecurity and IT infrastructure monitoring 
use cases, intending to deliver a system that is ready-to-use and offers 
inbox correlations and predictions on monitored data.

By this challenge, we want to help EMCA to answer the question of 
whether it is possible to reliably predict workload-related 
characteristics of monitored devices, based on historical data gathered 
from such devices. This task is of paramount importance for IT and 
technical teams that can put their hands on a tool that allows them to 
manage the capacity of their infrastructure.

An additional difficulty within this challenge, and also the reason why 
it might be especially interesting for the data science community, 
arises from the fact that devices considered in the data are not 
uniform. In essence, logs cover readings from various types of hardware. 
Some of them are cross-dependent, as they are a part of the same IT 
system. Moreover, some devices have multiple interfaces for which the 
data is aggregated.

More details regarding the task and a description of the challenge data 
can be found in the Task description section (see: 
https://knowledgepit.ml/fedcsis20-challenge/)


Result presentation:

Special session at FedCSIS 2020: As in previous years, a special session 
devoted to the competition will be held at the conference. We will 
invite authors of selected challenge reports to extend them for 
publication in the conference proceedings (after reviews by Organizing 
Committee members) and presentation at the conference. The papers will 
be indexed by the IEEE Digital Library and Web of Science. The invited 
teams will be chosen based on their final rank, innovativeness of their 
approach, and quality of the submitted report.


Awards:

Authors of the top-ranked solutions (based on the final evaluation 
scores) will be awarded prizes funded by our sponsors:

First Prize: 1500 USD + one free FedCSIS'20 conference registration,
Second Prize: 1000 USD + one free FedCSIS'20 conference registration,
Third Prize: 500 USD + one free FedCSIS'20 conference registration.
The award ceremony will take place during the FedCSIS'20 conference. 
Please note that the winners will only be eligible for the money prizes 
only if their final score exceeds the baseline solution score by at 
least 10%.


Contact information:

For all details, see: https://knowledgepit.ml/fedcsis20-challenge/
or contact:
Piotr Biczyk <[log in to unmask]>
Andrzej Janusz <[log in to unmask]>


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