NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter



Using Hadoop for Fraud Detection and Prevention

Hadoop can be vital for solving the fraud detection problem because:

  • Sampling does not work for rare events since the chance of missing a positive fraud case leads to significant deterioration of model quality.
  • Hadoop can solve much harder problems by leveraging multiple cores across thousands of machines and search through much larger problem domains.
  • Hadoop can be combined with other tools to manage moderate to low response latency requirements.

Nicely summarized by a commenter:

So the main point is “Cloudera has developed a tool, Flume, that can load billions of events into HDFS within a few seconds and analyze them using MapReduce.”?

And the suggestion to use ALL logs?

Or is there anything deeper that I am missing?

Original title and link for this post: Using Hadoop for Fraud Detection and Prevention (published on the NoSQL blog: myNoSQL)