Provenance compression scheme based on graph patterns for large RDF documents

Bok, Kyoungsoo; Han, Jieun; Lim, Jongtae; Yoo, Jaesoo

doi:10.1007/s11227-019-02926-2

Cited by 7 publications

(7 citation statements)

References 36 publications

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“…Our previous work introduced the idea of leveraging provenance metadata for the pattern-based compression of large RDF graphs [29][30][31]. The present study extends this approach to the more challenging context of graph streams, where the dynamic nature of data requires adaptive pattern mining and incremental compression.…”

Section: Related Workmentioning

confidence: 87%

“…High accuracy and compression rates are provided by pattern extraction techniques, such as those in [14][15][16][23][24][25][26]; however, their long processing times during graph compression also render them difficult to be applied in real-time environments. As a solution to these issues, research on the use of provenance has been proposed [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

Lee,

Shin,

Choi

et al. 2024

Applied Sciences

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With recent advancements in network technology and the increasing popularity of the internet, the use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges, occur over time. Additionally, owing to the need for the efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and the complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time.

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Section: Related Workmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

Lee,

Shin,

Choi

et al. 2024

Applied Sciences

Self Cite

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“…RDF compression techniques ( Álvarez-García et al, 2011;Bok et al, 2019;Fernández et al, 2013;Meier, 2008;Pan et al, 2014;Pichler et al, 2010) are devised, as well as, Big Data tools are exploited in (Du et al, 2012;Khadilkar et al, 2012;Mami et al, 2016;Nie et al, 2012;Papailiou et al, 2013;Punnoose et al, 2012;Schätzle et al, 2013) to efficiently process RDF data. Furthermore, column-oriented stores (Idreos et al, 2012;MacNicol and French, 2004;Stonebraker et al, 2005;Zukowski et al, 2006) exploit fully decomposed storage model (Copeland and Khoshafian, 1985) to scale-up to large datasets, and data factorization based query optimization techniques are proposed in (Bakibayev et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…A compressed RDF structure, k 2triples, presented by Álvarez-García et al ( Álvarez-García et al, 2011), vertically partitions RDF triples, and utilizes k 2 -trees (Brisaboa et al, 2009) to create indexes for each partition. Bok et al (Bok et al, 2019) present RDF provenance compression technique by exploiting dictionary encoding. These approaches provide effective solutions for RDF data compression.…”

Section: Rdf Data Compressionmentioning

confidence: 99%

“…Therefore, efficient representations of semantic sensor data are required to store and process large amounts of sensor data using different RDF implementations. Rule-based (Joshi et al, 2013;Meier, 2008;Pichler et al, 2010) and binary ( Álvarez-García et al, 2011;Bok et al, 2019;Fernández et al, 2013;Pan et al, 2014) compression techniques for RDF data effectively reduce the size of the data. Distributed and parallel processing frameworks for Big Data are exploited in several approaches (Du et al, 2012;Khadilkar et al, 2012;Mami et al, 2016;Nie et al, 2012;Papailiou et al, 2013;Punnoose et al, 2012;Schätzle et al, 2012;Schätzle et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Compact Representations for Efficient Storage of Semantic Sensor Data

Karim¹,

Vidal²,

Auer³

2020

Preprint

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Nowadays, there is a rapid increase in the number of sensor data generated by a wide variety of sensors and devices. Data semantics facilitate information exchange, adaptability, and interoperability among several sensors and devices. Sensor data and their meaning can be described using ontologies, e.g., the Semantic Sensor Network (SSN) Ontology. Notwithstanding, semantically enriched, the size of semantic sensor data is substantially larger than raw sensor data. Moreover, some measurement values can be observed by sensors several times, and a huge number of repeated facts about sensor data can be produced. We propose a compact or factorized representation of semantic sensor data, where repeated measurement values are described only once. Furthermore, these compact representations are able to enhance the storage and processing of semantic sensor data. To scale up to large datasets, factorization based, tabular representations are exploited to store and manage factorized semantic sensor data using Big Data technologies. We empirically study the effectiveness of a semantic sensor's proposed compact representations and their impact on query processing. Additionally, we evaluate the effects of storing the proposed representations on diverse RDF implementations. Results suggest that the proposed compact representations empower the storage and query processing of sensor data over diverse RDF implementations, and up to two orders of magnitude can reduce query execution time.

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RDF Serialization and Archival

Fernández

Martínez‐Prieto

2022

Encyclopedia of Big Data Technologies

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Provenance compression scheme based on graph patterns for large RDF documents

Cited by 7 publications

References 36 publications

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

Compact Representations for Efficient Storage of Semantic Sensor Data

RDF Serialization and Archival

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