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Reasoning on the Efficiency of Distributed Complex Event Processing

Akili Samira, Weidlich Matthias

Fundamenta Informaticae

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2021

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Vol. 179, nr 2

113--134

EN

Complex event processing (CEP) evaluates queries over streams of event data to detect situations of interest. If the event data are produced by geographically distributed sources, CEP may exploit in-network processing that distributes the evaluation of a query among the nodes of a network. To this end, a query is modularized and individual query operators are assigned to nodes, especially those that act as data sources. Existing solutions for such operator placement, however, are limited in that they assume all query results to be gathered at one designated node, commonly referred to as a sink. Hence, existing techniques postulate a hierarchical structure of the network that generates and processes the event data. This largely neglects the optimisation potential that stems from truly decentralised query evaluation with potentially many sinks. To address this gap, in this paper, we propose Multi-Sink Evaluation (MuSE) graphs as a formal computational model to evaluate common CEP queries in a decentralised manner. We further prove the completeness of query evaluation under this model. Striving for distributed CEP that can scale to large volumes of high-frequency event streams, we show how to reason on the network costs induced by distributed query evaluation and prune inefficient query execution plans. As such, our work lays the foundation for distributed CEP that is both, sound and efficient.

2

Learning from student browsing data on e-learning platforms: case study

Cansiz Sergen, Sudan Berk, Ogretici Emre, Siddik Aktas Mehmet

Annals of Computer Science and Information Systems

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2020

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Vol. 22

37--44

EN

Interpretation of the behaviors of students in e-learning platforms with machine learning models has become an emerging need in recent years. Increase in the number of registered students on e-learning platforms is one of the reasons for choosing machine learning models. Tracking, modeling and understanding student activities gets more complex when the number of students is increased. This study is focusing modeling student activities on e-learning platforms with Complex Event Processing (CEP), Association Rule Mining (ARM) and Clustering methods based on distributed software architecture. Within the scope of this study, different modules that work real-time have been developed. An admin panel has been also developed in order to control all modules and track the student actions. Performance results of modules also obtained and evaluated on distributed system architecture.

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Application of the Complex Event Processing system for anomaly detection and network monitoring

Frankowski G., Jerzak M., Miłostan M., Nowak T., Pawłowski M.

Computer Science

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2015

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Vol. 16 (4)

351--371

EN

Protection of infrastructures for e-science, including grid environments and NREN facilities, requires the use of novel techniques for anomaly detection and network monitoring. The aim is to raise situational awareness and provide early warning capabilities. The main operational problem that most network operators face is integrating and processing data from multiple sensors and systems placed at critical points of the infrastructure. From a scientific point of view, there is a need for the efficient analysis of large data volumes and automatic reasoning while minimizing detection errors. In this article, we describe two approaches to Complex Event Processing used for network monitoring and anomaly detection and introduce the ongoing SECOR project (Sensor Data Correlation Engine for Attack Detection and Support of Decision Process), supported by examples and test results. The aim is to develop methodology that allows for the construction of next-generation IDS systems with artificial intelligence, capable of performing signature-less intrusion detection.

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Complex Event Processing approach to automated monitoring of a particle accelerator and its control system

Grzegorczyk K., Baggiolini V., Zieliński K.

Computer Science

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2014

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Vol. 15 (4)

351--364

EN

This article presents the design and implementation of a software component for automated monitoring and diagnostic information analysis of a particle accelerator and its control system. The information that is analyzed can be seen as streams of events. A Complex Event Processing (CEP) approach to event processing was selected. The main advantage of this approach is the ability to continuously query data coming from several streams. The presented software component is based on Esper, the most popular open-source implementation of CEP. As a test bed, the control system of the accelerator complex located at CERN, the European Organization for Nuclear Research, was chosen. The complex includes the Large Hadron Collider, the world’s most powerful accelerator. The main contribution to knowledge is by showing that the CEP approach can successfully address many of the challenges associated with automated monitoring of the accelerator and its control system that were previously unsolved. Test results, performance analysis, and a proposal for further works are also presented.

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Towards effective social network system implementation

Škrabálek J., Kunc P., Nguyen F., Pitner T.

Control and Cybernetics

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2012

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Vol. 41, no. 4

835--849

EN

In this paper we present our latest research in the area of social network system implementation. Both business and technological aspects of social network system development are considered. There are many tools, languages and methods for developing large-size software systems and architectures represented by social network systems. However, no research has been done yet to uncover the reasons behind the selection and usage of such systems in terms of choosing the right architecture and data storage. We describe effective approach to developing specific parts of social Network systems with special attention to data layer (using Hadoop, HBase and Apache Cassandra), which forms the foundation of Any social network system and is highly demanding for performance and scalability.