Cascade-aware partitioning of large graph databases

Demirci, Gündüz Vehbi; Ferhatosmanoğlu, Hakan; Aykanat, Cevdet

doi:10.1007/s00778-018-0531-8

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…They used the database model, loaded in an RDF triple store, which is a type of graph store. In addition, the authors in [67] developed an algorithm to solve the graph partition problem with cascade recognition. In [68], a model based on graphs is proposed to manage data generated by networks of multimedia sensors, which was built based on the topology of these networks.…”

Section: A Rq-1 What Are Nosql Database Modeling Procedures Based On ...mentioning

confidence: 99%

NoSQL Database Modeling and Management: A Systematic Literature Review

Aguilar Vera,

Naal Jácome,

Díaz Mendoza

et al. 2023

Rev. Fac. Ing.

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The NoSQL databases that emerged this century were created to solve the limitations of relational database systems due to the different types of data that have appeared for information processing. In this paper, we present the results of a secondary study carried out to find and synthesize the research made up to now on modeling processes, characteristics of the used types of data, and management tools for NoSQL Databases. Currently, four types are recognized and classified according to the data model they use: key-value, document-oriented, column-based, and graph-based. With this study, it was possible to identify that the most frequently type of NoSQL database model is that of documents because it offers greater flexibility and versatility compared to the other three models. Although it offers more complex search methods, in terms of data, column and document schemas are the ones that usually describe their characteristics. It was also possible to observe a trend in the use of the column-oriented model and the document-oriented model in the management tools, and, although they all comply with the basic functionalities, the differences lie in the way in which the information is stored and the way they can be accessed.

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Section: A Rq-1 What Are Nosql Database Modeling Procedures Based On ...mentioning

confidence: 99%

NoSQL Database Modeling and Management: A Systematic Literature Review

Aguilar Vera,

Naal Jácome,

Díaz Mendoza

et al. 2023

Rev. Fac. Ing.

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Privacy-Preserving Approximate Top-k Nearest Keyword Queries over Encrypted Graphs

Shen

Wang

et al. 2021

2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)

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Buffered Streaming Graph Partitioning

Faraj

Schulz

2022

ACM J. Exp. Algorithmics

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Partitioning graphs into blocks of roughly equal size is a widely used tool when processing large graphs. Currently there is a gap observed in the space of available partitioning algorithms. On the one hand, there are streaming algorithms that have been adopted to partition massive graph data on small machines. In the streaming model, vertices arrive one at a time including their neighborhood and then have to be assigned directly to a block. These algorithms can partition huge graphs quickly with little memory, but they produce partitions with low solution quality. On the other hand, there are offline (shared-memory) multilevel algorithms that produce partitions with high quality but also need a machine with enough memory to partition huge networks. In this work, we make a first step to close this gap by presenting an algorithm that computes significantly improved partitions of huge graphs using a single machine with little memory in streaming setting. First, we adopt the buffered streaming model which is a more reasonable approach in practice. In this model, a processing element can store a buffer of nodes alongside with their edges before making assignment decisions. When our algorithm receives a batch of nodes, we build a model graph that represents the nodes of the batch and the already present partition structure. This model enables us to apply multilevel algorithms and in turn, on cheap machines, compute much higher quality solutions of huge graphs than previously possible. To partition the model graph, we develop a multilevel algorithm that optimizes an objective function that has previously shown to be effective for the streaming setting. Surprisingly, this also removes the dependency on the number of blocks k from the running time compared to the previous state-of-the-art. Overall, our algorithm computes, on average, \(75.9\% \) better solutions than \(\text{{\sf Fennel}} \) [35] using a very small buffer size. In addition, for large values of k our algorithm becomes faster than \(\text{{\sf Fennel}} \) .

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Cascade-aware partitioning of large graph databases

Cited by 3 publications

References 42 publications

NoSQL Database Modeling and Management: A Systematic Literature Review

NoSQL Database Modeling and Management: A Systematic Literature Review

Privacy-Preserving Approximate Top-k Nearest Keyword Queries over Encrypted Graphs

Buffered Streaming Graph Partitioning

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