Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible Universal Resource Identifiers as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation, and Web technologies. As a consequence, the RDF data model is used in a variety of applications today for integrating knowledge and information: in open Web or government data via the Linked Open Data initiative, in scientific domains such as bioinformatics, and more recently in search engines and personal assistants of enterprises in the form of knowledge graphs.

Managing such large volumes of RDF data is challenging due to the sheer size, heterogeneity, and complexity brought by RDF reasoning. To tackle the size challenge, distributed architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications requiring distributed architectures for the scalability, fault tolerance, and elasticity features it provides. At the same time, interest in massively parallel processing has been renewed by the MapReduce model and many follow-up works, which aim at simplifying the deployment of massively parallel data management tasks in a cloud environment.

In this book, we study the state-of-the-art RDF data management in cloud environments and parallel/distributed architectures that were not necessarily intended for the cloud, but can easily be deployed therein. After providing a comprehensive background on RDF and cloud technologies, we explore four aspects that are vital in an RDF data management system: data storage, query processing, query optimization, and reasoning. We conclude the book with a discussion on open problems and future directions.

Autorentext

Zoi Kaoudi is a Senior Researcher in the DIMA group at the Technische Universitat Berlin (TUB). She has previously worked as a Scientist in the Qatar Computing Research Institute (QCRI) of the Hamad Bin Khalifa University in Qatar, in IMIS-Athena Research Center as a research associate, and Inria as a postdoctoral researcher. She received her Ph.D. from the National and Kapodistrian University of Athens in 2011. Her research interests include cross-platform data processing, machine learning systems, and distributed RDF query processing andreasoning. Recently she has been the proceedings chair of EDBT 2019, co-chaired the TKD poster track co-located with ICDE 2018, and co-organized the MLDAS 2019 held in Qatar. She has co-authored articles in both database and Semantic Web communities and served as amember of a Program Committee for several international database conferences.Ioana Manolescu is a senior Inria researcher, and the lead of the CEDAR team (joint between Inria Saclay and theLIX lab of Ecole polytechnique) in France. The CEDAR team research focuses on rich data analytics at cloud scale. Ioana is a member of the PVLDB Endowment Board of Trustees and has served for four years (including as president) of the ACM SIGMOD Jim Gray Ph.D. dissertation committee. Recently, she has been a general chair of the IEEE ICDE 2018 conference, an associate editor for PVLDB 2017 and 2018, and the program chair of SSDBBM 2016. She has co-authored more than 130 articles in international journals and conferences, and contributed to several books. Her main research interests include data models and algorithms for computational fact-checking, performance optimizations for semi structured data and the Semantic Web, and distributed architectures for complex large data.Stamatis Zampetakis is an R&D engineer at TIBCO Orchestra Networks and a PMC member of Apache Calcite. Previously, he was a postdoctoral researcher at Inria, from where he also received his Ph.D. in 2015. Before that, he worked in FORTH-ICS as a research assistant. His research interests are in the broad area of query optimization with emphasis on RDF query processing and visualization.

Inhalt
Introduction.- Preliminaries.- Cloud-Based RDF Storage.- Cloud-Based SPARQL Query Processing.- SPARQL Query Optimization for the Cloud.- RDFS Reasoning in the Cloud.- Concluding Remarks.- Bibliography.- Authors' Biographies.