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

Adel Hidri,Ahmed Selmi,Minyar Sassi Hidri

doi:10.1109/access.2020.2965647

Abstract

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.

Highlights

Data mining is defined as the science of extracting meaningful information from several flows of large data
Science groups a number of underlying methods and techniques, whose aims vary and spread all over a spectrum of applications: pattern recognition [4], statistical models [19], predictive analysis [20], machine learning [25], Information science [6], etc
The remainder of this paper is organized as follows: in section II, we briefly present a state of the art on sequential pattern mining

Summary

Introduction

Data mining is defined as the science of extracting meaningful information from several flows of large data. Some of the tasks most commonly found are: Summarization & Reduction [35]

Objectives

Results

Conclusion