This demo presents Spam Miner, an online system designed for real-time monitoring and characterization of spam traffic over the Internet. Our system is based on high-level abstractions such as spam message attributes, spam campaigns and spamming strategies. A campaign is a cluster of messages that are generated from a single message template; campaign identification is a challenging problem because it has to handle spammer evolution, while seeking for a spam similarity function that combines different message characteristics and for strategies that efficiently process large volumes of spams. Moreover, spam campaigns need to be identified on-the-fly, to allow incident response teams and security specialists to react to the threat adequately. Spam Miner addresses campaign identification as a data clustering problem and campaigns are identified dynamically using a novel incremental approach based on the concept of Frequent Pattern Tree. Spam Miner is being used by NIC.br (Brazilian Network Information Center) and mined more than 350 million spam messages, detecting meaningful clusters and patterns, and helping the organization to better understand the spam problem in Brazil and how the Brazilian Internet infrastructure is being abused by spammers.