Discovery of Frequent Patterns of Episodes Within a Time Window for Alarm Management Systems

IEEE Access

Adel Hidri¹  Minyar Sassi Hidri¹  Ahmed Selmi² 

[1] Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia;Data Scientist, Applied Mathematics Research Engineer, National Engineering School of Tunis, Tunis El Manar University, Tunis, Tunisia;

关键词: Sequential pattern mining;    alarm management;    association rules;    data mining;    artificial intelligence;   

DOI  :  10.1109/ACCESS.2020.2965647

来源: DOAJ

【 摘 要 】

The sequential pattern mining field is expanding through numerous researches and has a large number of applications such as language processing, alarms management and event management on a broader scale. Its use began with processing items baskets to learn patterns and have a directed marketing strategy but it is generalized to telecommunication alarms management with several works. Our work is in line with this, as it tries to locate patterns and identify them to make predictive statements about certain patterns. It is axed around providing a way to break sequences into episodes and assigning them a value of confidence and support, more precisely in the discovery of frequent patterns of episodes within a time window. Experimental results have shown the effectiveness of our sequential pattern mining approach and its adaptability to alarm management and analytics.

【 授权许可】

Unknown