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Erschienen in:
General Smart City Experts’ Perceptions of Citizen Participation: A Questionnaire Survey Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Smart Incident Management, Prediction Engine and Performance Enhancement

verfasst von : Jamal El Abdelkhalki, Mohamed Ben Ahmed

Erschienen in: Innovations in Smart Cities Applications Edition 3

Verlag: Springer International Publishing

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Abstract

The rapid growth of the Internet has resulted in an exponential increase in the type and frequency of anomalies, and many of the well-known anomaly detection solutions are in place.
However, each computer system collects information about the different tasks performed. Each piece of information is stored in specific files called log files. The log files consist of log messages or simply journal. A log message is what a computer system, software. Generate in response to some kind of stimulation. The information that has removed a log message and declares that the log message was generated is called log data. A common log message contains the timestamp, source, and data. The timestamp indicates the time at which the login message was created. The source is the system that created the log message and the data is the core of the log message. Unfortunately, this format is not a standard; A log message can be significantly different from one system to another. Classification is an important data mining technique with broad applications. It classifies data of various kinds.
This paper has been carried out to make a performance evaluation of KNN, J48 and – Naive Bayes classification algorithm and sets out to make comparative evaluation of classifiers in the context of dataset.

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Vorheriges Kapitel Recommendation Enhancement Using Traceability and Machine Learning: A Review
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Metadaten
Titel
Smart Incident Management, Prediction Engine and Performance Enhancement
verfasst von
Jamal El Abdelkhalki
Mohamed Ben Ahmed
Copyright-Jahr
2020
Buch
Innovations in Smart Cities Applications Edition 3

Print ISBN: 978-3-030-37628-4

Electronic ISBN: 978-3-030-37629-1

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-37629-1_73

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