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EAGr: Supporting Continuous Ego-centric Aggregate Queries over Large Dynamic Graphs
[article]
2014
arXiv
pre-print
In this work, we present EAGr, a system for supporting large numbers of continuous neighborhood-based ("ego-centric") aggregate queries over large, highly dynamic, and rapidly evolving graphs. Examples of such queries include computation of personalized, tailored trends in social networks, anomaly/event detection in financial transaction networks, local search and alerts in spatio-temporal networks, to name a few. Key challenges in supporting such continuous queries include high update rates
arXiv:1404.6570v1
fatcat:h5mkhfn7srfexd2bsiamisrshi