@article{miao2015immortalgraph,
author = {Miao, Youshan and Han, Wentao and Li, Kaiwei and Wu, Ming and Prabhakaran, Vijayan and Chen, Enhong and Chen, Wenguang and Yang, Fan and Zhou, Lidong},
title = {ImmortalGraph: A System for Storage and Analysis of Temporal Graphs},
year = {2015},
month = {July},
abstract = {Temporal graphs that capture graph changes over time are attracting increasing interest from research communities, for functions such as understanding temporal characteristics of social interactions on a time-evolving social graph. ImmortalGraph is a storage and execution engine designed and optimized specifically for temporal graphs. Locality is at the center of ImmortalGraph’s design: temporal graphs are carefully laid out in both persistent storage and memory, taking into account data locality in both time and graph-structure dimensions. ImmortalGraph introduces the notion of locality-aware batch scheduling in computation, so that common “bulk” operations on temporal graphs are scheduled to maximize the benefit of in-memory data locality. The design of ImmortalGraph explores an interesting interplay among locality, parallelism, and incremental computation in supporting common mining tasks on temporal graphs. The result is a high-performance temporal-graph system that is up to 5 times more efficient than existing database solutions for graph queries. The locality optimizations in ImmortalGraph offer up to an order of magnitude speedup for temporal iterative graph mining compared to a straightforward application of existing graph engines on a series of snapshots.},
publisher = {ACM - Association for Computing Machinery},
url = {http://approjects.co.za/?big=en-us/research/publication/immortalgraph-a-system-for-storage-and-analysis-of-temporal-graphs/},
journal = {ACM Transactions on Storage (TOS)},
edition = {ACM Transactions on Storage (TOS)},
}