@misc{acs-2017,
  author = {Jason Riedy},
  title = {High-Performance Analysis of Streaming Graphs},
  howpublished = {HPC Analytic Workshop},
  month = jun,
  year = 2017,
  dom = 28,
  address = {Hanover, MD},
  url = {https://www.slideshare.net/jasonriedy/highperformance-analysis-of-streaming-graphs-77348572},
  projtag = {hpda, grateful, memory-centric, crnch-rg},
  abstract = {Graph-structured data in social networks, finance, network security, and others not only are massive but also under continual change. These changes often are scattered across the graph. Stopping the world to run a single, static query is infeasible. Repeating complex global analyses on massive snapshots to capture only what has changed is inefficient. We discuss requirements for single-shot queries on changing graphs as well as recent high-performance algorithms that update rather than recompute results. These algorithms are incorporated into our software framework for streaming graph analysis, STINGER.},
  keywords = {hpda, graph analysis, streaming data, memory-centric, novel architectures},
  ejr-proj = {high-performance-data-analysis, graph-analysis, novel-arch},
  ejr-grant = {hpda, iarpa-emu, grateful}
}

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