ZipG

Khandelwal, Anurag; Yang, Zongheng; Ye, Evan; Agarwal, Rachit; Stoica, Ion

doi:10.1145/3035918.3064012

Cited by 30 publications

(3 citation statements)

References 25 publications

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“…Further future work will be applying compression techniques as described in [26] or using query optimization techniques like [60] to improve the performance of our distributed RDF store for arbitrary graph covers Koral. Part of our contribution are the tools CEP and Koral which are open source available on the Web for further investigation of distributed RDF data management challenges.…”

Section: Resultsmentioning

confidence: 99%

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Impact analysis of data placement strategies on query efforts in distributed RDF stores

Janke

Staab

Thimm

2018

Journal of Web Semantics

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In the last years, scalable RDF stores in the cloud have been developed, where graph data is distributed over compute and storage nodes for scaling efforts of query processing and memory needs. One main challenge in these RDF stores is the data placement strategy that can be formalized in terms of graph covers. These graph covers determine whether (a) the triples distribution is well-balanced over all storage nodes (storage balance) (b) different query results may be computed on several compute nodes in parallel (vertical parallelization) and (c) individual query results can be produced only from triples assigned to few-ideally one-storage node (horizontal containment). We analyse the impact of three most commonly used graph cover strategies in these terms and found out that balancing query workload reduces the query execution time more than reducing data transfer over network. To this end, we present our novel benchmark and open source evaluation platform Koral.

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Section: Resultsmentioning

confidence: 99%

“…Thus, all triples with the same subject are located in the same graph chunk. This graph cover strategy is used, for instance, by Virtuoso Clustered Edition [4], YARS2 [5,6], ZipG [26], Clustered TDB [7] and Trinity.RDF [8].…”

Section: Hash Covermentioning

confidence: 99%

Impact analysis of data placement strategies on query efforts in distributed RDF stores

Janke

Staab

Thimm

2018

Journal of Web Semantics

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“…Some other approaches follow a different philosophy, that is, providing access to the compressed graph with a wide range of complex operations, or even a query language, at the cost of sub-optimal compression ratios. This is the case for example of ZipG [11], a distributed graph storage system aims at compactly storing a graph, including semantic information on its nodes and edges, while allowing access to this information via a minimal but rich API.…”

Section: Introductionmentioning

confidence: 99%

Zuckerli: A New Compressed Representation for Graphs

et al. 2020

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Zuckerli is a scalable compression system meant for large real-world graphs. Graphs are notoriously challenging structures to store efficiently due to their linked nature, which makes it hard to separate them into smaller, compact components. Therefore, effective compression is crucial when dealing with large graphs, which can have billions of nodes and edges. Furthermore, a good compression system should give the user fast and reasonably flexible access to parts of the compressed data without requiring full decompression, which may be unfeasible on their system. Compared to WebGraph, the de-facto standard in compressing real-world graphs, Zuckerli improves multiple aspects by using advanced compression techniques and novel heuristic graph algorithms. Zuckerli can produce both a compressed representation for storage and one which allows fast direct access to the adjacency lists of the compressed graph without decompressing the entire graph. We validate its effectiveness on real-world graphs with up to a billion nodes and 90 billion edges, conducting an extensive experimental evaluation of both compression density and decompression performance. We show that Zuckerli-compressed graphs are 10% to 29% smaller, and more than 20% in most cases, with a resource usage for decompression comparable to that of WebGraph.

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