Sokchomrern Ean
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Sokchomrern Ean has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Bunrong Leang
Department of Computer Science, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Korea
1 shared publication - Ga-Ae Ryu
Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea
1 shared publication - Kwan-Hee Yoo
Department of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea
1 shared publication
Latest Publications
- Open Access
Improvement of Kafka Streaming Using Partition and Multi-Threading in Big Data Environment
The large amount of programmable logic controller (PLC) sensing data has rapidly increased in the manufacturing environment. Therefore, a large data store is necessary for Big Data platforms. In this paper, we propose a Hadoop ecosystem for the support of many features in the manufacturing industry. In this ecosystem, Apache Hadoop and HBase are used as Big Data storage and handle large scale data. In addition, Apache Kafka is used as a data streaming pipeline which contains many configurations and properties that are used to make a better-designed environment and a reliable system, such as Kafka offset and partition, which is used for program scaling purposes. Moreover, Apache Spark closely works with Kafka consumers to create a real-time processing and analysis of the data. Meanwhile, data security is applied in the data transmission phase between the Kafka producers and consumers. Public-key cryptography is performed as a security method which contains public and private keys. Additionally, the public-key is located in the Kafka producer, and the private-key is stored in the Kafka consumer. The integration of these above technologies will enhance the performance and accuracy of data storing, processing, and securing in the manufacturing environment.
Open AccessImprovement of Kafka Streaming Using Partition and Multi-Threading in Big Data Environment
The large amount of programmable logic controller (PLC) sensing data has rapidly increased in the manufacturing environment. Therefore, a large data store is necessary for Big Data platforms. In this paper, we propose a Hadoop ecosystem for the support of many features in the manufacturing industry. In this ecosystem, Apache Hadoop and HBase are used as Big Data storage and handle large scale data. In addition, Apache Kafka is used as a data streaming pipeline which contains many configurations and properties that are used to make a better-designed environment and a reliable system, such as Kafka offset and partition, which is used for program scaling purposes. Moreover, Apache Spark closely works with Kafka consumers to create a real-time processing and analysis of the data. Meanwhile, data security is applied in the data transmission phase between the Kafka producers and consumers. Public-key cryptography is performed as a security method which contains public and private keys. Additionally, the public-key is located in the Kafka producer, and the private-key is stored in the Kafka consumer. The integration of these above technologies will enhance the performance and accuracy of data storing, processing, and securing in the manufacturing environment.