Towards fully-fledged archiving for RDF datasets

Pelgrin, Olivier; Galárraga, Luis; Hose, Katja

doi:10.3233/sw-210434

Cited by 10 publications

(3 citation statements)

References 60 publications

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“…There are several ways to implement this idea in RDF, such as RDF reification [18], singleton properties [25], and named graphs [7]. However, these mechanisms have significant shortcomings [11,16,19,26].…”

Section: Preliminariesmentioning

confidence: 99%

Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches -- extended version

Abuoda¹,

Dell’Aglio²,

Keen³

et al. 2022

Preprint

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RDF and property graph models have many similarities, such as using basic graph concepts like nodes and edges. However, such models differ in their modeling approach, expressivity, serialization, and the nature of applications. RDF is the de-facto standard model for knowledge graphs on the Semantic Web and supported by a rich ecosystem for inference and processing. The property graph model, in contrast, provides advantages in scalable graph analytical tasks, such as graph matching, path analysis, and graph traversal. RDF-star extends RDF and allows capturing metadata as a first-class citizen. To tap on the advantages of alternative models, the literature proposes different ways of transforming knowledge graphs between property graphs and RDF. However, most of these approaches cannot provide complete transformations for RDF-star graphs. Hence, this paper provides a step towards transforming RDF-star graphs into property graphs. In particular, we identify different cases to evaluate transformation approaches from RDF-star to property graphs. Specifically, we categorize two classes of transformation approaches and analyze them based on the test cases. The obtained insights will form the foundation for building complete transformation approaches in the future.

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Section: Preliminariesmentioning

confidence: 99%

Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches -- extended version

Abuoda¹,

Dell’Aglio²,

Keen³

et al. 2022

Preprint

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show abstract

“…As such, this hybrid storage approach is a step towards solving the need for properly preserving RDF on the Web. A recent survey [21] has shown that existing RDF archiving solutions fail to handle large RDF archives with many versions. It was shown that the hybrid approach employed by OSTRICH is the only one capable of storing large RDF archives, but that it suffers from a scalability issue in terms of ingestion time for many versions.…”

Section: Taelman Et Al / Optimizing Storage Of Rdf Archives Using Bid...mentioning

confidence: 99%

Optimizing storage of RDF archives using bidirectional delta chains

Taelman

Mahieu

Vanbrabant

et al. 2022

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Linked Open Datasets on the Web that are published as RDF can evolve over time. There is a need to be able to store such evolving RDF datasets, and query across their versions. Different storage strategies are available for managing such versioned datasets, each being efficient for specific types of versioned queries. In recent work, a hybrid storage strategy has been introduced that combines these different strategies to lead to more efficient query execution for all versioned query types at the cost of increased ingestion time. While this trade-off is beneficial in the context of Web querying, it suffers from exponential ingestion times in terms of the number of versions, which becomes problematic for RDF datasets with many versions. As such, there is a need for an improved storage strategy that scales better in terms of ingestion time for many versions. We have designed, implemented, and evaluated a change to the hybrid storage strategy where we make use of a bidirectional delta chain instead of the default unidirectional delta chain. In this article, we introduce a concrete architecture for this change, together with accompanying ingestion and querying algorithms. Experimental results from our implementation show that the ingestion time is significantly reduced. As an additional benefit, this change also leads to lower total storage size and even improved query execution performance in some cases. This work shows that modifying the structure of delta chains within the hybrid storage strategy can be highly beneficial for RDF archives. In future work, other modifications to this delta chain structure deserve to be investigated, to further improve the scalability of ingestion and querying of datasets with many versions.

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“…Since the complex graph data archives ensure permanent, diversified, highly scaleable, and efficient analytical and VOLUME 11, 2023 query processing capabilities for future application demands for many emerging databases and graph processing frameworks [8]- [10], RDF (Resource Description Framework) graph data archives must be a use-case for DNA storage. DNA-based RDF graph data archives should, therefore, be able to retrieve partial information using an effective queryprocessing mechanism when analyzing advanced data analytics.…”

mentioning

confidence: 99%

DNA-Based Storage of RDF Graph Data: A Futuristic Approach to Data Analytics

Usmani,

Wiese

2023

IEEE Access

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Future data analytics will require enormous storage space for data-driven decisions, necessitating alternative storage sources for massive data archives. Storage solutions have always been in demand due to the limitations of existing media. Deoxyribonucleic Acid (DNA) is an emergent storage medium suitable for archival storage of rapidly increasing digital volumes. Due to its longevity, DNA storage technology has led to numerous applications to store and retrieve entire data. In this way, DNA synthesis and sequencing costs can be reduced by compressing data in full before it is stored. However, prior works have not used DNA storage to retrieve partial data from complex graphs, while taking advantage of cost-effective advanced analytics. In this paper, we present an efficient DNA-based query processing system to retrieve partial information using RDF graph data. Moreover, using binary search, we fetch and decode significantly fewer DNA strands to obtain partial information about RDF graph data based on SPARQL queries. Specifically, the experimental analysis shows that the average data retrieval per query as output is found less than 1% for RDF graphs with more than 1MB (Megabytes) in size, which consequently reduces a significant amount of sequencing costs.INDEX TERMS Data retrieval, DNA storage, RDF graph data model, SPARQL query processing. I. INTRODUCTION

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Towards fully-fledged archiving for RDF datasets

Cited by 10 publications

References 60 publications

Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches -- extended version

Transforming RDF-star to Property Graphs: A Preliminary Analysis of Transformation Approaches -- extended version

Optimizing storage of RDF archives using bidirectional delta chains

DNA-Based Storage of RDF Graph Data: A Futuristic Approach to Data Analytics

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