Historical Graph Management in Dynamic Environments

Bok, Kyoungsoo; Kim, Gihoon; Lim, Jongtae; Yoo, Jaesoo

doi:10.3390/electronics9060895

Cited by 6 publications

(7 citation statements)

References 33 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: 88%

“…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: 88%

Section: Introductionmentioning

confidence: 99%

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

Lee,

Shin,

Choi

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…High accuracy and compression rates are provided by pattern extraction techniques, such as [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%

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

Lee,

Shin,

Choi

et al. 2024

Preprint

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With recent advancements in network technology and spread of Internet, 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 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 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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“…A graph is a data structure for representing the multiple relationships between objects [1,2]. In recent real applications, dynamic graphs are generated, in which the vertices and edges constituting the graph are constantly changing [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Cost Model Based Incremental Processing in Dynamic Graphs

et al. 2022

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Incremental graph processing has been developed to reduce unnecessary redundant calculations in dynamic graphs. In this paper, we propose an incremental dynamic graph-processing scheme using a cost model to selectively perform incremental processing or static processing. The cost model calculates the predicted values of the detection cost and processing cost of the recalculation region based on the past processing history. If there is a benefit of the cost model, incremental query processing is performed. Otherwise, static query processing is performed because the detection cost and processing cost increase due to the graph change. The proposed incremental scheme reduces the amount of computation by processing only the changed region through incremental processing. Further, it reduces the detection and disk I/O costs of the vertex, which are calculated by reusing the subgraphs from the previous results. The processing structure of the proposed scheme stores the data read from the cache and the adjacent vertices and then performs only memory mapping when processing these graph. It is demonstrated through various performance evaluations that the proposed scheme outperforms the existing schemes.

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Historical Graph Management in Dynamic Environments

Cited by 6 publications

References 33 publications

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

Graph Stream Compression Scheme Based on Pattern Dictionary Using Provenance

Cost Model Based Incremental Processing in Dynamic Graphs

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