A Survey of Real-Time Big Data Processing Algorithms

Lal, Devesh Kumar; Suman, Ugrasen

doi:10.1007/978-981-15-3746-2_1

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…For instance, an event might be filtered according to a predefined threshold, event characteristic or timestamp. Time-based aggregation is typically divided into hopping windows or tumbling windows [Lal and Suman, 2020]. Incident detection capability is also one of the key SIEM features, and be further split into categories such as signature and ML-based anomaly detection techniques.…”

Section: A Taxonomy For Evolved Siemsmentioning

confidence: 99%

Intrusion and Anomaly Detection in Industrial Automation and Control Systems

Rosa

Cruz

Simões

et al. 2023

NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium

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The completion of my dissertation would not have been possible without the valuable discussions and all the constructive criticism they provided me. In that sense, first, I would like to extend my deepest gratitude to my formal supervisors. Paulo Simões, with his experience and unique way to look at things, always encouraged me on this journey.Invariably, he gave the peace of mind I needed. Then, Edmundo Monteiro whose help and patience cannot be overestimated. In my long journey with ups and downs, whenever I requested, he had a word for me. And last but not least, I would like to express my sincere gratitude to Tiago Cruz. For the careful reader, Tiago does not appear in the first pages as my official supervisor. Nevertheless, after starting this dissertation, I was lucky enough to meet him. Since then, in the last years, I have been working closely with him and we became friends, he was always there to support me and listen to my endless complaints.More, he was kind enough to provide me his expertise and guidance as my supervisor and discuss all the technical details of this dissertation. For all of those reasons, I will always consider him one of my supervisors.Next, without getting into details to avoid being unfair, I would like to thank my friends and colleagues who really worked with me for the past years and made this journey less lonely.We called ourselves the G54 team. This was a group of young and committed researchers who developed strong relationships way beyond the normal working environment. They consistently offered their help, technical support, and push me to complete my dissertation.I'm a proud member of the G54 spirit. Likewise, I would like to extend my sincere thanks to the LCT research group which I'm also proud to be part of. Their continuous support and all the pleasant moments we get together are truly priceless.Finally, I'm deeply indebted to my family, and in particular to my wife, for the full support, motivation, love, and patience.

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Section: A Taxonomy For Evolved Siemsmentioning

confidence: 99%

Intrusion and Anomaly Detection in Industrial Automation and Control Systems

Rosa

Cruz

Simões

et al. 2023

NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium

View full text Add to dashboard Cite

show abstract

Survey of window types for aggregation in stream processing systems

et al. 2023

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In this paper, we present the first comprehensive survey of window types for stream processing systems which have been presented in research and commercial systems. We cover publications from the most relevant conferences, journals, and system whitepapers on stream processing, windowing, and window aggregation which have been published over the last 20 years. For each window type, we provide detailed specifications, formal notations, synonyms, and use-case examples. We classify each window type according to categories that have been proposed in literature and describe the out-of-order processing. In addition, we examine academic, commercial, and open-source systems with respect to the window types that they support. Our survey offers a comprehensive overview that may serve as a guideline for the development of stream processing systems, window aggregation techniques, and frameworks that support a variety of window types.

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An Online Approach to Determine Correlation between Data Streams

Lal

Suman

2021

Companion of the ACM/SPEC International Conference on Performance Engineering

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A Survey of Real-Time Big Data Processing Algorithms

Cited by 3 publications

References 22 publications

Intrusion and Anomaly Detection in Industrial Automation and Control Systems

Intrusion and Anomaly Detection in Industrial Automation and Control Systems

Survey of window types for aggregation in stream processing systems

An Online Approach to Determine Correlation between Data Streams

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