Digital Library[ Search Result ]
A Greedy Rule Allocation Algorithm for Efficient Distributed Complex Event Processing
http://doi.org/10.5626/JOK.2019.46.12.1222
Complex event processing (CEP) is defined as event processing for multiple stream sources to infer events that suggest complicated circumstances. As the size of stream data becomes larger, CEP engines have been parallelized to benefit from distributed computing. However, distributed CEP could duplicate redundant stream data and increase latency without consideration about the computational cost on each engine after the allocation of stream data and CEP rules. In this paper, we suggest an efficient rule allocation algorithm to prevent such situations. This algorithm determines event rules priorities for the allocation, wherein the rule with higher priority is allocated first to the engine that minimizes the increase of the value of the proposed cost function. We prove the superiority of our algorithm in two tests. In the optimization verification test, our algorithm achieves the results closest to the optimal results compared with the other algorithms. In the performance test, our algorithm shows lower latency and data replication ratio in the distributed CEP system using real world dataset and event rules.
CEP Rule Distribution Algorithm for In-network Processing in an IoT Network Environment
http://doi.org/10.5626/JOK.2018.45.7.722
As the number of IoT devices increases, data coming from devices are also increasing exponentially. The data generated from devices are stored and managed through a system structure using the database. However, to manage the surging data, the existing database is limited in terms of maintenance costs and in real time. Too overcome these limitations, Complex Event Processing (CEP), which processes data as much as possible within the network, has emerged, and data processing is being carried out using this strategy. In this paper, we propose a CEP Rule distribution algorithm which can reduce server burden and guarantee network performance through distribution of the CEP Rule in an IoT environment. To prove this, we perform a small experiment using open source, such as the OpenWSN and TelosB node, and verify the mitigation of server load and the performance of data processing according to the algorithm.
Implementation of Real-time Data Stream Processing for Predictive Maintenance of Offshore Plants
In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr