PGX.D

Hong, Ser Gi; Depner, Siegfried; Manhardt, Thomas; Lugt, Jan Van Der; Verstraaten, Merijn; Chafi, Hassan

doi:10.1145/2807591.2807620

Cited by 62 publications

(3 citation statements)

References 19 publications

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“…Distributed in-memory systems. Many systems [2, 13,17,23,27,29,30,33,38,43,52,68,[70][71][72]75] exist for distributed CPU-only graph analytics. All these systems are based on variants of the vertex programming model.…”

Section: Related Workmentioning

confidence: 99%

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

et al. 2018

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This paper introduces a new approach to building distributedmemory graph analytics systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies, and programming models. The key to this approach is Gluon, a communication-optimizing substrate. Programmers write applications in a shared-memory programming system of their choice and interface these applications with Gluon using a lightweight API. Gluon enables these programs to run on heterogeneous clusters and optimizes communication in a novel way by exploiting structural and temporal invariants of graph partitioning policies. To demonstrate Gluon's ability to support different programming models, we interfaced Gluon with the Galois and Ligra shared-memory graph analytics systems to produce distributed-memory versions of these systems named D-Galois and D-Ligra, respectively. To demonstrate Gluon's ability to support heterogeneous processors, we interfaced Gluon with IrGL, a state-of-the-art single-GPU system for * Both authors contributed equally.

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Section: Related Workmentioning

confidence: 99%

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

et al. 2018

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“…Thus, the overall time complexity of LogRank algorithm is

O (k \cdot N + N \cdot \log N)

. Note that if we use some parallel methods to calculate PageRank, for example, PGX, 42 the time complexity of PageRank can be reduced to O ( k · E ) where E is the number of edges in the graph. We leave this improvement for future work.…”

Section: Logrank: a Graph‐based Log Ranking Modelmentioning

confidence: 99%

Sampling business process event logs using graph‐based ranking model

Liu

Pei

Cheng

et al. 2020

Concurrency and Computation

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Summary Modern information systems are continuously collecting and storing large volumes of business process event logs. The analysis of event logs can provide valuable insights for business process re‐engineering and enhancement. Process discovery, as one of the most challenging event log analysis techniques, aims to discover a business process model from an event log. Many process discovery approaches have been proposed in the past two decades, however, most of them suffer from efficiency problem when dealing with large‐scale event logs. Motivated by PageRank, we propose LogRank, a graph‐based ranking model, for event log sampling in this paper. The LogRank is capable of sampling a large‐scale event log to a smaller size that can be efficiently handled by existing discovery approaches. To support real‐life applications, we instantiate the LogRank model for two typical types of event logs, that is, simple event logs and lifecycle event logs. To quantify the quality of a sample log with respect to the original one, we introduce a general evaluation framework that can be instantiated for different quality metrics. The proposed sampling approach has been implemented in the open‐source process mining toolkit ProM. By experiments with both synthetic and real‐life event logs, we demonstrate that the proposed LogRank‐based sampling approach provides an effective means to improve process discovery efficiency as well as guaranteeing high quality of discovered models.

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“…Interested readers are referred to [118]. Beside RDF stores in the cloud there also exist distributed graph databases like Sparksee 5 or Titan 6 as well as distributed graph processing frameworks like PGX.D [61] or PEGASUS [66]. They are not described in this manuscript since they have not been presented as part of an RDF store, yet.…”

Section: Introductionmentioning

confidence: 99%

Storing and Querying Semantic Data in the Cloud

Janke

Staab

2018

Lecture Notes in Computer Science

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In the last years, huge RDF graphs with trillions of triples were created. To be able to process this huge amount of data, scalable RDF stores are used, in which graph data is distributed over compute and storage nodes for scaling efforts of query processing and memory needs. The main challenges to be investigated for the development of such RDF stores in the cloud are: (i) strategies for data placement over compute and storage nodes, (ii) strategies for distributed query processing, and (iii) strategies for handling failure of compute and storage nodes. In this manuscript, we give an overview of how these challenges are addressed by scalable RDF stores in the cloud. 8 We adapted the definition of an RDF molecule in [38] to allow for paths with a length ≥ 1. 9 The term anchor vertex was taken from [79]. 10 dom(µ) refers to the set of variables of this binding.

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PGX.D

Cited by 62 publications

References 19 publications

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics

Sampling business process event logs using graph‐based ranking model

Storing and Querying Semantic Data in the Cloud

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