Matches in SemOpenAlex for { <https://semopenalex.org/work/W4293207697> ?p ?o ?g. }
- W4293207697 endingPage "54" @default.
- W4293207697 startingPage "43" @default.
- W4293207697 abstract "Abstract Intelligent monitoring of a computer network provides a clear understanding of its behaviour at various times and in various situations. It also provides relief to support teams that spend most of their time troubleshooting problems caused by hardware or software failures. This type of monitoring ensures the accuracy and efficiency of the network to meet the expectations of its users. However, to ensure intelligent monitoring, it is necessary to start by automating this process, which often leads to long and costly interventions. The success of such automation implies the establishment of predictive maintenance as a prerequisite for good preventive maintenance governance. However, even when it is practiced effectively, preventive maintenance requires a great deal of time and the mobilization of several full-time resources, especially for large IT structures. This paper gives an overview of the monitoring of a computer network and explains its process and the problems encountered. It also proposes a method based on machine learning to allow for prediction and support decision making to proactively anticipate interventions." @default.
- W4293207697 created "2022-08-27" @default.
- W4293207697 creator A5003999513 @default.
- W4293207697 creator A5007183451 @default.
- W4293207697 creator A5049594471 @default.
- W4293207697 date "2022-06-01" @default.
- W4293207697 modified "2023-09-30" @default.
- W4293207697 title "Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision" @default.
- W4293207697 cites W1500693574 @default.
- W4293207697 cites W1919021221 @default.
- W4293207697 cites W1972809764 @default.
- W4293207697 cites W1990220114 @default.
- W4293207697 cites W2002983432 @default.
- W4293207697 cites W2030441628 @default.
- W4293207697 cites W2040366163 @default.
- W4293207697 cites W2047824738 @default.
- W4293207697 cites W2060280062 @default.
- W4293207697 cites W2063399198 @default.
- W4293207697 cites W2096474000 @default.
- W4293207697 cites W2103129939 @default.
- W4293207697 cites W2104333234 @default.
- W4293207697 cites W2109560632 @default.
- W4293207697 cites W2111038504 @default.
- W4293207697 cites W2113659417 @default.
- W4293207697 cites W2114837486 @default.
- W4293207697 cites W2116752428 @default.
- W4293207697 cites W2118022153 @default.
- W4293207697 cites W2124403757 @default.
- W4293207697 cites W2146946579 @default.
- W4293207697 cites W2150788325 @default.
- W4293207697 cites W2156813924 @default.
- W4293207697 cites W2158907675 @default.
- W4293207697 cites W2159587105 @default.
- W4293207697 cites W2160251991 @default.
- W4293207697 cites W2161006788 @default.
- W4293207697 cites W2161591413 @default.
- W4293207697 cites W2162332844 @default.
- W4293207697 cites W2162831674 @default.
- W4293207697 cites W2240435662 @default.
- W4293207697 cites W2617931713 @default.
- W4293207697 cites W2737647392 @default.
- W4293207697 cites W2755897503 @default.
- W4293207697 cites W2784294263 @default.
- W4293207697 cites W2911964244 @default.
- W4293207697 cites W2933589034 @default.
- W4293207697 cites W2963765036 @default.
- W4293207697 cites W2997822014 @default.
- W4293207697 cites W3004795158 @default.
- W4293207697 cites W3162728205 @default.
- W4293207697 cites W3163791259 @default.
- W4293207697 cites W341531820 @default.
- W4293207697 cites W4300945730 @default.
- W4293207697 doi "https://doi.org/10.2478/acss-2022-0005" @default.
- W4293207697 hasPublicationYear "2022" @default.
- W4293207697 type Work @default.
- W4293207697 citedByCount "2" @default.
- W4293207697 countsByYear W42932076972022 @default.
- W4293207697 countsByYear W42932076972023 @default.
- W4293207697 crossrefType "journal-article" @default.
- W4293207697 hasAuthorship W4293207697A5003999513 @default.
- W4293207697 hasAuthorship W4293207697A5007183451 @default.
- W4293207697 hasAuthorship W4293207697A5049594471 @default.
- W4293207697 hasBestOaLocation W42932076971 @default.
- W4293207697 hasConcept C111919701 @default.
- W4293207697 hasConcept C112930515 @default.
- W4293207697 hasConcept C115901376 @default.
- W4293207697 hasConcept C127413603 @default.
- W4293207697 hasConcept C147494362 @default.
- W4293207697 hasConcept C195094911 @default.
- W4293207697 hasConcept C200601418 @default.
- W4293207697 hasConcept C24090081 @default.
- W4293207697 hasConcept C41008148 @default.
- W4293207697 hasConcept C70452415 @default.
- W4293207697 hasConcept C71924100 @default.
- W4293207697 hasConcept C78519656 @default.
- W4293207697 hasConcept C98045186 @default.
- W4293207697 hasConceptScore W4293207697C111919701 @default.
- W4293207697 hasConceptScore W4293207697C112930515 @default.
- W4293207697 hasConceptScore W4293207697C115901376 @default.
- W4293207697 hasConceptScore W4293207697C127413603 @default.
- W4293207697 hasConceptScore W4293207697C147494362 @default.
- W4293207697 hasConceptScore W4293207697C195094911 @default.
- W4293207697 hasConceptScore W4293207697C200601418 @default.
- W4293207697 hasConceptScore W4293207697C24090081 @default.
- W4293207697 hasConceptScore W4293207697C41008148 @default.
- W4293207697 hasConceptScore W4293207697C70452415 @default.
- W4293207697 hasConceptScore W4293207697C71924100 @default.
- W4293207697 hasConceptScore W4293207697C78519656 @default.
- W4293207697 hasConceptScore W4293207697C98045186 @default.
- W4293207697 hasIssue "1" @default.
- W4293207697 hasLocation W42932076971 @default.
- W4293207697 hasLocation W42932076972 @default.
- W4293207697 hasOpenAccess W4293207697 @default.
- W4293207697 hasPrimaryLocation W42932076971 @default.
- W4293207697 hasRelatedWork W138176736 @default.
- W4293207697 hasRelatedWork W1984020633 @default.
- W4293207697 hasRelatedWork W2096383539 @default.
- W4293207697 hasRelatedWork W2380319506 @default.