Mining and Profiling Data Streams

Dear Colleagues,

Nowadays, data flows are continuously generated from heterogeneous hardware and software sources. There exist many examples of data stream providers, such as traffic sensors, health sensors, transaction logs, and activity logs. Typically, data providers send many data in extremely short time intervals, creating a continuous stream of data that must be rapidly processed. In this scenario, mining useful information and properties from data, such as statistics, semantic relationships, and distinct patterns, can support both data processing and analytics activities in different application domains, ranging from scientific to financial contexts. This Special Issue aims at providing methods and techniques for mining and profiling data streams, in order to extract metadata, to correctly process and manage data, and to perform unsupervised learning activities. Moreover, approaches and tools for supporting data preparation steps (e.g., data filtering, cleaning, and transformation), for avoiding the severe degradation in quality of mined results, are also welcomed. We invite researchers to contribute original and unique papers. Topics include, but are not limited to, the following areas:

• Data mining from data streams;
• Big data mining;
• Continuous queries;
• Data stream models;
• Deep learning with streaming data;
• Distributed and social stream mining;
• Explainable AI for predictive models;
• Foundations of learning from data streams;
• Graph stream mining;
• High-performance computing environments for big data streams;
• Incremental online learning algorithms;
• Internet of Things (IoT);
• Knowledge discovery and data profiling on data streams;
• Languages for stream query;
• Learning from heterogeneous, imbalanced, and multiple data streams;
• Medical data streams;
• Online model selection;
• Real-time analytics;
• Real-time and real-world applications using stream data;
• Scalable algorithms;
• Smart data mining with compact models;
• Temporal, spatial, and spatio-temporal data mining;
• Anomaly detection;
• Visualization techniques for data streams;
• Continuous and incremental data profiling;
• Data profiling over data stream management systems;
• Data preparation with streaming data.

Dr. Loredana Caruccio
Dr. Stefano Cirillo
Guest Editors

Manuscript Submission Information

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• data profiling
• continuous profiling
• metadata
• data mining and knowledge discovery
• unsupervised learning