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Enabling Window-Based Monotonic Graph Analytics with Reusable Transitional Results for Pattern-Consistent Queries
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AI
DOI：10.14778/3681954.3681979.png)
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Abstract
En 中文
Evolving graphs consisting of slices are large and constantly changing. For example, in Alipay, the graph generates hundreds of millions of new transaction records every day. Analyzing the graph within a temporary window is time-consuming due to the heavy merging of slices. Fortunately, we have discovered that most queries exhibit consistent patterns and possess monotonic properties. As a result, transitional results can be computed within slice generation for reuse. Accordingly, we develop MergeGraph enabling window-based monotonic graph analytics with reusable transitional results for pattern-consistent queries. MergeGraph has three advantages over previous works. First, it is the first system specifically tailored for window-based monotonic graph analytics with pattern-consistent queries. Second, it effectively utilizes transitional results from different slices concurrently. Third, MergeGraph boasts a high degree of expressiveness, supporting a broad spectrum of monotonic graph queries. Experimental results demonstrate that MergeGraph delivers significant performance benefits. In evaluating four typical graph applications, MergeGraph achieves an average speedup of 11.30x compared to state-of-the-art methods.
Keywords:
NEURAL-NETWORKS
EFFICIENT
WAREHOUSE
FRAMEWORK
SEARCH
WALKS
Journal
P
IF:
3.3
Papers:
553
Citations:
1.2W