@misc{an12-streaming-ms,
  file = {material/siam-an-2012.pdf},
  author = {David A. Bader and David Ediger and Jason Riedy},
  ejr-withauthor = {David A. Bader and David Ediger},
  title = {Streaming Graph Analytics for Massive Graphs},
  howpublished = {SIAM Annual Meeting},
  dom = 10,
  month = jul,
  year = 2012,
  url = {http://www.slideshare.net/jasonriedy/streaming-graph-analytics-for-massive-graphs},
  optrole = {presentation},
  opttags = {siam; streaming data; parallel algorithms},
  address = {Minneapolis, MN},
  abstract = {Emerging real-world graph problems include detecting community structure in large social networks, improving the resilience of the electric power grid, and detecting and preventing disease in human populations. The volume and richness of data combined with its rate of change renders monitoring properties at scale by static recomputation infeasible. We approach these problems with massive, fine-grained parallelism across different shared memory architectures both to compute solutions and to explore the sensitivity of these solutions to natural bias and omissions within the data.},
  projtag = {intel-sting, cassmt},
  keywords = {hpda, parallel algorithm, graph analysis, streaming data},
  ejr-proj = {high-performance-data-analysis, graph-analysis},
  ejr-grant = {intel-sting, cassmt}
}

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