Data Stream Mining of Event and Complex Event Streams

Wrench, Chris; Stahl, Frederic; Fatta, Giuseppe Di; Karthikeyan, Vidhyalakshmi; Nauck, Detlef

doi:10.4018/978-1-5225-0293-7.ch003

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…Here we are using it in our SIEM solution, where we have different results when single Esper engine with heavy load settings and multiple Esper engine with load distributed among engines setting in configured. A number of survey articles have also reviewed complex event stream processing such as [33] and [35]. Wrench et al [35] recently surveyed techniques of data stream mining of event and complex event streams.…”

Section: Related Workmentioning

confidence: 99%

“…A number of survey articles have also reviewed complex event stream processing such as [33] and [35]. Wrench et al [35] recently surveyed techniques of data stream mining of event and complex event streams. The authors argued that volume, velocity, veracity and variety characteristics of big data has to be accounted for while mining events and complex event streams as these characteristics demand special consideration.…”

Section: Related Workmentioning

confidence: 99%

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Optimizing SIEM Throughput on the Cloud Using Parallelization

et al. 2016

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Processing large amounts of data in real time for identifying security issues pose several performance challenges, especially when hardware infrastructure is limited. Managed Security Service Providers (MSSP), mostly hosting their applications on the Cloud, receive events at a very high rate that varies from a few hundred to a couple of thousand events per second (EPS). It is critical to process this data efficiently, so that attacks could be identified quickly and necessary response could be initiated. This paper evaluates the performance of a security framework OSTROM built on the Esper complex event processing (CEP) engine under a parallel and non-parallel computational framework. We explain three architectures under which Esper can be used to process events. We investigated the effect on throughput, memory and CPU usage in each configuration setting. The results indicate that the performance of the engine is limited by the number of events coming in rather than the queries being processed. The architecture where 1/4th of the total events are submitted to each instance and all the queries are processed by all the units shows best results in terms of throughput, memory and CPU usage.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimizing SIEM Throughput on the Cloud Using Parallelization

et al. 2016

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Data-Driven Techniques in Computing System Management

Zeng

Jiang

et al. 2017

ACM Comput. Surv.

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Modern forms of computing systems are becoming progressively more complex, with an increasing number of heterogeneous hardware and software components. As a result, it is quite challenging to manage these complex systems and meet the requirements in manageability, dependability, and performance that are demanded by enterprise customers. This survey presents a variety of data-driven techniques and applications with a focus on computing system management. In particular, the survey introduces intelligent methods for event generation that can transform diverse log data sources into structured events, reviews different types of event patterns and the corresponding event-mining techniques, and summarizes various event summarization methods and data-driven approaches for problem diagnosis in system management. We hope this survey will provide a good overview for data-driven techniques in computing system management.

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Akan Veri Karakterizasyonu, Üretimi Ve Analitiği Üzerine Kapsamlı Bir İnceleme

Utku¹,

Akçayol

2019

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Gelişen donanım ve yazılım teknolojileri sayesinde anlık olarak üretilen veri miktarı ve hızı giderek artmaktadır. Akan veriler, statik verilerden farklı olarak, potansiyel olarak sonsuz, hızlı, zamanla değişen ve gelişen bir yapıya sahiptir. Bu sebeple akan verilerin tamamını depolamak zaman ve depolama alanı kısıtları sebebiyle imkânsız olabilmektedir. Akan veri madenciliğinde kullanılan yöntemlerin, statik veri madenciliğinden farklı olarak akan verilerin doğasına uygun bir şekilde optimize edilmesi gerekmektedir. Bu çalışma kapsamında akan veriler, statik veriler ile karşılaştırmalı olarak analiz edilmiştir. Akan veri madenciliği, akan veri karakterizasyonu ve akan veri üretimi üzerine detaylı araştırmalar yapılmıştır. Akan verilerin türleri, kaynakları ve akan veriden öğrenme yöntemleri ile akan veri uygulamalarında kullanılan yöntemler ve algoritmalar kapsamlı bir şekilde incelenmiştir. Yapılan literatür araştırmaları ve incelemeler sonucunda, veri gizliliği, geleceğe dönük öngörülerin oluşturulması, veri ön işleme, kaynak kullanımı ve çevrimiçi güncelleme konularının geliştirilecek akan veri uygulamalarında değerlendirilmesi gereken konular olduğu ortaya konulmuştur.

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Data Stream Mining of Event and Complex Event Streams

Cited by 3 publications

References 48 publications

Optimizing SIEM Throughput on the Cloud Using Parallelization

Optimizing SIEM Throughput on the Cloud Using Parallelization

Data-Driven Techniques in Computing System Management

Akan Veri Karakterizasyonu, Üretimi Ve Analitiği Üzerine Kapsamlı Bir İnceleme

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