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- Leidėjas: Springer Nature Switzerland AG
- Metai: 2026
- Puslapiai: 339
- ISBN-10: 3032265266
- ISBN-13: 9783032265265
- Kalba: Anglų
Frontiers in Data Engineering and Retrieval Systems (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
A product of the International Conference on Data Management and Information Retrieval (Polkowice, Poland), this volume explores the technological, algorithmic, and applied dimensions of data management and information retrieval. It spans a wide range of topics, including data warehousing, semantic search, knowledge discovery, federated learning, data ethics, multilingual search engines, and AI-driven indexing. It focuses on how large-scale data infrastructures interface with advanced retrieval systems, machine learning models, and user-centric design. The volume is grounded in real-world applications across various domains such as healthcare, urban planning, public administration, and e-commerce, offering rich examples of how data and retrieval models intersect in practical environments.
This volume makes a substantial contribution to the field by presenting a unique blend of foundational research and domain-specific innovation. It highlights how modern information systems are evolving from basic search and indexing to adaptive, explainable, and federated retrieval models. In contrast to earlier publications focusing narrowly on retrieval models or big data systems in isolation, this volume integrates both areas into a shared framework emphasizing interoperability, user experience, privacy, and sustainability. It positions ICDMIR as a forward-looking conference that not only tracks academic progress but also shapes the discourse on how data should be organized, accessed, and understood in an increasingly complex digital world.
- Leidėjas: Springer Nature Switzerland AG
- Metai: 2026
- Puslapiai: 339
- ISBN-10: 3032265266
- ISBN-13: 9783032265265
- Kalba: Anglų
A product of the International Conference on Data Management and Information Retrieval (Polkowice, Poland), this volume explores the technological, algorithmic, and applied dimensions of data management and information retrieval. It spans a wide range of topics, including data warehousing, semantic search, knowledge discovery, federated learning, data ethics, multilingual search engines, and AI-driven indexing. It focuses on how large-scale data infrastructures interface with advanced retrieval systems, machine learning models, and user-centric design. The volume is grounded in real-world applications across various domains such as healthcare, urban planning, public administration, and e-commerce, offering rich examples of how data and retrieval models intersect in practical environments.
This volume makes a substantial contribution to the field by presenting a unique blend of foundational research and domain-specific innovation. It highlights how modern information systems are evolving from basic search and indexing to adaptive, explainable, and federated retrieval models. In contrast to earlier publications focusing narrowly on retrieval models or big data systems in isolation, this volume integrates both areas into a shared framework emphasizing interoperability, user experience, privacy, and sustainability. It positions ICDMIR as a forward-looking conference that not only tracks academic progress but also shapes the discourse on how data should be organized, accessed, and understood in an increasingly complex digital world.
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