Fast data stream algorithms using associative memories

The primary goal of data stream research is to develop space and time efficient solutions for answering continuous online summarization queries. Research efforts over the last decade have resulted in a number of efficient algorithms with varying degrees of space and time complexities. While these techniques are developed in a standard CPU setting, many of their applications such as click-fraud detection and network-traffic summarization typically execute on special networking architectures called Network Processing Units (NPUs). These NPUs interface with a special type of associative memories, known as Ternary Content Addressable Memories (TCAMs), to provide gigabit rate forwarding at network routers. In this paper, we describe how the integrated architecture of NPU and TCAMs can be exploited towards achieving the goal of developing high-speed stream summarization solutions. We propose two TCAM-conscious solutions for the frequent elements problem in data streams and present a compreh...

• Type: Conference
• Year: 2007
• Where: SIGMOD
• Authors: Nagender Bandi, Ahmed Metwally, Divyakant Agrawal, Amr El Abbadi
• Keywords: Database, Keywords Data Streams, Online Summarization Queries, SIGMOD 2007, Stream Summarization Solutions