Abstract
Incremental graph processing has been developed to reduce unnecessary redundant calculations in dynamic graphs. In this paper, we propose an incremental dynamic graph-processing scheme using a cost model to selectively perform incremental processing or static processing. The cost model calculates the predicted values of the detection cost and processing cost of the recalculation region based on the past processing history. If there is a benefit of the cost model, incremental query processing is performed. Otherwise, static query processing is performed because the detection cost and processing cost increase due to the graph change. The proposed incremental scheme reduces the amount of computation by processing only the changed region through incremental processing. Further, it reduces the detection and disk I/O costs of the vertex, which are calculated by reusing the subgraphs from the previous results. The processing structure of the proposed scheme stores the data read from the cache and the adjacent vertices and then performs only memory mapping when processing these graph. It is demonstrated through various performance evaluations that the proposed scheme outperforms the existing schemes.
Highlights
A graph is a data structure for representing the multiple relationships between objects [1,2]
To reduce the processing cost incurred by detecting the adjacent vertices in addition to the disk I/O cost, we propose a technique to reuse the previous results as well as a cache strategy to accelerate incremental processing
If there is a benefit in the cost model, the proposed scheme, known as incremental gatherapply-scatter, is performed to incrementally process the region affected by graph changes
Summary
A graph is a data structure for representing the multiple relationships between objects [1,2]. In other words, when a subgraph is changed, the static processing scheme calculates the entire graph, including the part that has not changed Because this scheme performs unnecessary, i.e., redundant processing, it is extremely time consuming. Owing to the problem of performing unnecessary, duplicate computations, the static processing schemes cannot provide real-time analysis results. If the subgraph changed by the dynamic graph is large, the detection and processing costs may increase. We propose an incremental dynamic graph-processing scheme called iGAS, which modifies the GAS model to perform incremental processing to provide realtime analysis results. The proposed scheme uses the cost model to selectively perform incremental processing or static processing based on the degree of change in the graph.
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