Title
Automatic Algorithm Transformation for Efficient Multi-Snapshot Analytics on Temporal Graphs.
Abstract
Analytical graph algorithms commonly compute metrics for a graph at one point in time. In practice it is often also of interest how metrics change over time, e.g., to find trends. For this purpose, algorithms must be executed for multiple graph snapshots. We present Single Algorithm Multiple Snapshots (SAMS), a novel approach to execute algorithms concurrently for multiple graph snapshots. SAMS automatically transforms graph algorithms to leverage similarities between the analyzed graph snapshots. The automatic transformation interleaves algorithm executions on multiple snapshots, synergistically shares their graph accesses and traversals, and optimizes the algorithm's data layout. Thus, SAMS can amortize the cost of random data accesses and improve memory bandwidth utilization---two main cost factors in graph analytics. We extensively evaluate SAMS using six well-known algorithms and multiple synthetic as well as real-world graph datasets. Our measurements show that in multi-snapshot analyses, SAMS offers runtime improvements of up to two orders of magnitude over traditional snapshot-at-a-time execution.
Year
DOI
Venue
2017
10.14778/3090163.3090166
PVLDB
Field
DocType
Volume
Data mining,Graph,Algorithm transformation,Graph database,Memory bandwidth,Computer science,Theoretical computer science,SPQR tree,Factor cost,Analytics,Snapshot (computer storage),Database
Journal
10
Issue
ISSN
Citations 
8
2150-8097
4
PageRank 
References 
Authors
0.43
19
5
Name
Order
Citations
PageRank
Manuel Then1434.73
Timo Kersten241.45
Stephan G眉nnemann383369.26
Alfons Kemper43519769.50
Thomas Neumann52523156.50