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]> -- Ta wiadomość e-mail została sprawdzona pod kątem wirusów przez oprogramowanie AVG. http://www.avg.com ######################################################################## To unsubscribe from the AI-SGES list, click the following link: https://www.jiscmail.ac.uk/cgi-bin/webadmin?SUBED1=AI-SGES&A=1