Dear Colleagues,

The data volume from a variety of sources is exploding at an increasingly rapid rate. As a result, it is essential to collect data from multiple different sources and to manage such massive amounts of data in distributed environments such as on-premise data servers or cloud services such as Amazon AWS, MS Azure, or Google GCP. To address big data challenges, distributed computing techniques have been developed on top of a general-purpose big data framework providing distributed data ingestion (e.g., Apache Flume), distributed file systems (e.g., HDFS), MapReduce-like computation models (e.g., Apache Spark), and large-scale stream-processing (e.g., Apache Kafka). Recently, container-orchestration frameworks (e.g., Kubernetes) have been also widely applied to provide big data services.

With the necessity of distributed or cloud computing, traditional approaches of data ingestion, management, and analysis on various data types such as geo-spatial data, images, and log data need to be developed for the distributed or cloud environments. Machine learning or deep learning-based techniques also need to consider their scalability for multiple distributed or partitioned models. Federated learning is the representative example to distribute a single model into multiple local models and to aggregate them to a global model.

In this Special Issue, we focus on big data management and analysis that require distributed or cloud computing. Topics of interests include, but are not limited to the following:

• Big data management and analysis on the distributed or cloud environments
• Data sciences with distributed or cloud computing
• Distributed data collection and ingestion
• Machine learning or deep learning-based data analysis with distributed computing
• Federated learning to distributed computing
• Multi-modal data analysis
• Distributed container-based computing
• Distributed sensor data integration and analysis

Prof. Dr. Hyuk-Yoon Kwon
Dr. Kisung Lee
Guest Editors

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• Big data management and analysis
• Data sciences
• Distributed and cloud computing
• Federated learning
• Multi-modal data analysis
• Distributed data ingestion
• Distributed containers