adroit
发表于 2025-3-27 00:10:09
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obsolete
发表于 2025-3-27 04:03:06
Hands-On Experiencesable for educational purposes due to its neat design. Readers only interested in surveying existing big graph analytics systems may safely skip this chapter, while researchers and practitioners who are interested in using Pregel-like system and building their own Big Data systems could benefit from reading this chapter.
出没
发表于 2025-3-27 05:33:33
Shared Memory Abstractiont although the programming interface of these systems simulates a shared memory environment, the underlying execution engine is not shared memory. We focus on introducing the various models implementing shared memory abstraction, and leave readers to explore the system usage by following their respective system websites.
火海
发表于 2025-3-27 12:41:52
Introduction, have witnessed a surging interest in developing big graph analytics systems. Tens of systems have been developed for processing big graphs. Although they enrich the tools available for users to analyze big graphs, it is difficult for beginners of this field to gather up the threads of various syste
surrogate
发表于 2025-3-27 15:41:15
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露天历史剧
发表于 2025-3-27 19:38:54
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countenance
发表于 2025-3-27 23:12:02
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obeisance
发表于 2025-3-28 04:09:57
Block-Centric Computationwith a large diameter. This chapter describes a novel block-centric computation model that overcomes the weaknesses of vertex-centric computation, and that significantly speeds up iterative graph computation. We also include a hands-on tutorial on how to get started with Blogel, a state-of-the-art b
obsolete
发表于 2025-3-28 09:34:51
Subgraph-Centric Graph Miningng, rendering the distributed execution communication-intensive. However, graph mining tasks are often computation-intensive, and cannot be efficiently executed with a data-intensive system. The vertex-centric API is also unsuitable for writing a graph mining algorithm that often checks subgraphs ra
档案
发表于 2025-3-28 14:08:40
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