Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 5 of 5 for “"distributed graph processing"”.
-
Approximate failure recovery in distributed graph processing systems
Distributed graph processing systems are an emerging area of big data systems. As graphs continue to grow in size and prevalence, these systems must become faster and more scalable. However, after failures, distributed graph processing systems either largely rely on proactive fault tolerance …
-
Topology-aware distributed graph processing for tightly-coupled clusters
… Specifically, we look at a class of distributed machine learning systems called distributed graph processing systems, and run them on NCSA Blue Waters. Partitioning the graph is key to achieving performance in distributed graph processing systems. We present new topology-aware …
-
Zorro: zero-cost reactive failure recovery in distributed graph processing
Distributed graph processing frameworks have become increasingly popular for processing large graphs. However, existing frameworks either lack the ability to recovery from failures or support proactive recovery methods. Proactive recovery methods like checkpointing incur high overheads during …
-
An experimental comparison of partitioning strategies in distributed graph processing
… of choosing among partitioning strategies in distributed graph processing systems. To this end, we evaluate and characterize both the performance and resource usage of different partitioning strategies under various popular distributed graph processing systems, applications, input graphs, and …
-
Scaling overlapping community detection algorithms
… the underlying structure of communities in graphs with a greater degree of correctness. This specific area of graph mining is known as community detection. It is one of the most critical components of graph mining. It helps in understanding the underlying properties of the graph. While a lot …