Deploying And Managing Fuseki Servers
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Deploying and Managing Fuseki Servers
"Deploying and Managing Fuseki Servers" "Deploying and Managing Fuseki Servers" is a comprehensive and authoritative guide for professionals seeking to master the deployment, configuration, and administration of Apache Jena Fuseki servers. Beginning with the essentials of semantic web architecture, the book provides a structured exploration of RDF principles, SPARQL protocol, and the critical functions of the Fuseki platform within modern data architectures. Readers are introduced to the Jena framework’s core components, positioning Fuseki as a pivotal technology for linked data, enterprise integration, and knowledge graph solutions. The book offers clear, step-by-step coverage of installation and operational best practices, including platform-specific considerations, automated provisioning, clustering, and secure configuration. Detailed chapters delve into robust dataset management, advanced security controls, resource optimization, and performance scaling, ensuring that practitioners can confidently deliver scalable and resilient SPARQL endpoints. Special attention is paid to configuration management, dataset federation, backup strategies, monitoring, and disaster recovery, providing practical guidance for sustaining long-term, enterprise-grade deployments. For developers and system architects, the volume presents advanced topics such as custom SPARQL functions, federated queries, integration with enterprise authentication providers, continuous delivery pipelines, and telemetry. Troubleshooting methodologies, operational runbooks, and participation in the open-source Jena community round out this indispensable resource. With in-depth insights and actionable strategies throughout, "Deploying and Managing Fuseki Servers" equips readers to build secure, high-performance, and future-proof semantic data infrastructures.
Knowledge Science, Engineering and Management
This book constitutes the refereed proceedings of the 9th International Conference on Knowledge Science, Engineering and Management, KSEM 2016, held in Passau, Germany, in October 2016. The 49 revised full papers presented together with 2 keynotes were carefully selected and reviewed from 116 submissions. The papers are organized in topical sections on Clustering and Classification; Text Mining and Lexical Analysis; Content and Document Analysis; Enterprise Knowledge; Formal Semantics and Fuzzy Logic; Knowledge Engineering; Knowledge Enrichment and Visualization; Knowledge Management; Knowledge Retrieval; Knowledge Systems and Security; Neural Networks and Artificial Intelligence; Ontologies; and Recommendation Algorithms and Systems.
Data Quality Management with Semantic Technologies
Christian Fürber investigates the useful application of semantic technologies for the area of data quality management. Based on a literature analysis of typical data quality problems and typical activities of data quality management processes, he develops the Semantic Data Quality Management framework as the major contribution of this thesis. The SDQM framework consists of three components that are evaluated in two different use cases. Moreover, this thesis compares the framework to conventional data quality software. Besides the framework, this thesis delivers important theoretical findings, namely a comprehensive typology of data quality problems, ten generic data requirement types, a requirement-centric data quality management process, and an analysis of related work.