Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

Ali, Waqas

doi:10.36227/techrxiv.12813698

Cited by 2 publications

(2 citation statements)

References 74 publications

(166 reference statements)

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“…In this section, we focus only on the second category. Scalable graph processing has been reviewed recently by [2,32,33,50]. Most of the approaches might be categorized by their main purpose such as; data storage, indexing, query languages and query execution.…”

Section: Scalable Graph Processingmentioning

confidence: 99%

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ABSTAT-HD: a scalable tool for profiling very large knowledge graphs

et al. 2021

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Processing large-scale and highly interconnected Knowledge Graphs (KG) is becoming crucial for many applications such as recommender systems, question answering, etc. Profiling approaches have been proposed to summarize large KGs with the aim to produce concise and meaningful representation so that they can be easily managed. However, constructing profiles and calculating several statistics such as cardinality descriptors or inferences are resource expensive. In this paper, we present ABSTAT-HD, a highly distributed profiling tool that supports users in profiling and understanding big and complex knowledge graphs. We demonstrate the impact of the new architecture of ABSTAT-HD by presenting a set of experiments that show its scalability with respect to three dimensions of the data to be processed: size, complexity and workload. The experimentation shows that our profiling framework provides informative and concise profiles, and can process and manage very large KGs.

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Section: Scalable Graph Processingmentioning

confidence: 99%

“…Several approaches have been proposed to adapt models and frameworks for graph processing to the distributed computing paradigm [2,32,33,50].…”

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confidence: 99%

ABSTAT-HD: a scalable tool for profiling very large knowledge graphs

et al. 2021

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A comprehensive overview of RDF for spatial and spatiotemporal data management

Qingzhe

Du³

et al. 2021

The Knowledge Engineering Review

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Currently, a large amount of spatial and spatiotemporal RDF data has been shared and exchanged on the Internet and various applications. Resource Description Framework (RDF) is widely accepted for representing and processing data in different (including spatiotemporal) application domains. The effective management of spatial and spatiotemporal RDF data are becoming more and more important. A lot of work has been done to study how to represent, query, store, and manage spatial and spatiotemporal RDF data. In order to grasp and learn the main ideas and research results of spatial and spatiotemporal RDF data, in this paper, we provide a comprehensive overview of RDF for spatial and spatiotemporal data management. We summarize spatial and spatiotemporal RDF data management from several essential aspects such as representation, querying, storage, performance assessment, datasets, and management tools. In addition, the direction of future research and some comparisons and analysis are also discussed in depth.

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Storage, Indexing, Query Processing, and Benchmarking in Centralized and Distributed RDF Engines: A Survey

Abstract: This paper is all about the different constructs of RDF data processing. This paper covers the storage, indexing, language, query planning.<br>

Cited by 2 publications

References 74 publications

ABSTAT-HD: a scalable tool for profiling very large knowledge graphs

ABSTAT-HD: a scalable tool for profiling very large knowledge graphs

A comprehensive overview of RDF for spatial and spatiotemporal data management

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