Future Technologies for Data Management, Processing and Application

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Bol This Reprint brings together eleven contributions that address this pathway from complementary perspectives. The papers examine data resource evaluation, knowledge graph reasoning, recommendation, secure data exchange, implicit authentication, multimodal speech processing, image distillation, adaptive forecasting, synthetic data generation, microgrid reliability assessment, and memory system optimization. Collectively, they show that data-oriented electronic systems require coordinated advances in data quality, domain-aware processing, security, reliability, and resource-efficient implementation.This perspective is consistent with earlier research emphasizing that data quality should be understood from the viewpoint of data consumers rather than only as a technical property. It also reflects the broader transition from conventional database management toward dataspaces capable of organizing heterogeneous and distributed information resources, as well as the growing role of big data analytics in decision-making and system optimization. Related studies on electronics have similarly highlighted the importance of reliable information interaction, timely data delivery, and multisource information fusion in connected and sensor-based environments. Building on these directions, this Special Issue Reprint illustrates how data technologies can be developed across both algorithmic and system-level contexts.

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This Reprint brings together eleven contributions that address this pathway from complementary perspectives. The papers examine data resource evaluation, knowledge graph reasoning, recommendation, secure data exchange, implicit authentication, multimodal speech processing, image distillation, adaptive forecasting, synthetic data generation, microgrid reliability assessment, and memory system optimization. Collectively, they show that data-oriented electronic systems require coordinated advances in data quality, domain-aware processing, security, reliability, and resource-efficient implementation.This perspective is consistent with earlier research emphasizing that data quality should be understood from the viewpoint of data consumers rather than only as a technical property. It also reflects the broader transition from conventional database management toward dataspaces capable of organizing heterogeneous and distributed information resources, as well as the growing role of big data analytics in decision-making and system optimization. Related studies on electronics have similarly highlighted the importance of reliable information interaction, timely data delivery, and multisource information fusion in connected and sensor-based environments. Building on these directions, this Special Issue Reprint illustrates how data technologies can be developed across both algorithmic and system-level contexts.


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Merk MDPI AG
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  • 9783725880317
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