@misc{ieeecluster2017,
  author = {Eisha Nathan and Anita Zakrzewska and Chunxing Yin and Jason Riedy},
  title = {A New Direction for Streaming Graph Analysis},
  howpublished = {IEEE Cluster},
  month = sep,
  dom = 6,
  year = 2017,
  address = {Honolulu, HI},
  opttags = {graph analysis; parallel algorithms},
  projtag = {hpda, memory-centric, grateful, crnch-rg},
  abstract = {Applications in computer network security, social media analysis, and other areas rely on analyzing a changing environment.  The data is rich in relationships and lends itself to graph analysis.  Traditional static graph analysis cannot keep pace with network security applications analyzing nearly one million events per second and social networks like Facebook collecting 500 thousand comments per second.  Streaming frameworks like STINGER support ingesting up three million of edge changes per second but there are few streaming analysis kernels that keep up with these rates.  Here we introduce a new, non-stop model and use it to decouple the analysis from the data ingest.},
  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}
}

{"_id":"Y7k2v9QZwsezrgnbE","bibbaseid":"nathan-zakrzewska-yin-riedy-anewdirectionforstreaminggraphanalysis-2017","downloads":0,"creationDate":"2018-08-21T18:11:53.699Z","title":"A New Direction for Streaming Graph Analysis","author_short":["Nathan, E.","Zakrzewska, A.","Yin, C.","Riedy, J."],"year":2017,"bibtype":"misc","biburl":"http://lovesgoodfood.com/jason/CV/ejr.bib","bibdata":{"bibtype":"misc","type":"misc","author":[{"firstnames":["Eisha"],"propositions":[],"lastnames":["Nathan"],"suffixes":[]},{"firstnames":["Anita"],"propositions":[],"lastnames":["Zakrzewska"],"suffixes":[]},{"firstnames":["Chunxing"],"propositions":[],"lastnames":["Yin"],"suffixes":[]},{"firstnames":["Jason"],"propositions":[],"lastnames":["Riedy"],"suffixes":[]}],"title":"A New Direction for Streaming Graph Analysis","howpublished":"IEEE Cluster","month":"September","dom":"6","year":"2017","address":"Honolulu, HI","opttags":"graph analysis; parallel algorithms","projtag":"hpda, memory-centric, grateful, crnch-rg","abstract":"Applications in computer network security, social media analysis, and other areas rely on analyzing a changing environment. The data is rich in relationships and lends itself to graph analysis. Traditional static graph analysis cannot keep pace with network security applications analyzing nearly one million events per second and social networks like Facebook collecting 500 thousand comments per second. Streaming frameworks like STINGER support ingesting up three million of edge changes per second but there are few streaming analysis kernels that keep up with these rates. Here we introduce a new, non-stop model and use it to decouple the analysis from the data ingest.","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","bibtex":"@misc{ieeecluster2017,\n author = {Eisha Nathan and Anita Zakrzewska and Chunxing Yin and Jason Riedy},\n title = {A New Direction for Streaming Graph Analysis},\n howpublished = {IEEE Cluster},\n month = sep,\n dom = 6,\n year = 2017,\n address = {Honolulu, HI},\n opttags = {graph analysis; parallel algorithms},\n projtag = {hpda, memory-centric, grateful, crnch-rg},\n abstract = {Applications in computer network security, social media analysis, and other areas rely on analyzing a changing environment. The data is rich in relationships and lends itself to graph analysis. Traditional static graph analysis cannot keep pace with network security applications analyzing nearly one million events per second and social networks like Facebook collecting 500 thousand comments per second. Streaming frameworks like STINGER support ingesting up three million of edge changes per second but there are few streaming analysis kernels that keep up with these rates. Here we introduce a new, non-stop model and use it to decouple the analysis from the data ingest.},\n keywords = {hpda, graph analysis, streaming data, memory-centric, novel architectures},\n ejr-proj = {high-performance-data-analysis, graph-analysis, novel-arch},\n ejr-grant = {hpda, iarpa-emu, grateful}\n}\n\n","author_short":["Nathan, E.","Zakrzewska, A.","Yin, C.","Riedy, J."],"key":"ieeecluster2017","id":"ieeecluster2017","bibbaseid":"nathan-zakrzewska-yin-riedy-anewdirectionforstreaminggraphanalysis-2017","role":"author","urls":{},"keyword":["hpda","graph analysis","streaming data","memory-centric","novel architectures"],"metadata":{"authorlinks":{}}},"search_terms":["new","direction","streaming","graph","analysis","nathan","zakrzewska","yin","riedy"],"keywords":["hpda","graph analysis","streaming data","memory-centric","novel architectures"],"authorIDs":[],"dataSources":["yxjEg4KYBKLdX7BAz","iovMFeXD2oi65krsd"]}