SNIF TOOL: sniffing for patterns in continuous streams

Continuous time-series sequence matching, specifically, matching a numeric live stream against a set of predefined pattern sequences, is critical for domains ranging from fire spread tracking to network traffic monitoring. While several algorithms exist for similarity matching of static time-series data, matching continuous data poses new, largely unsolved challenges including online real-time processing requirements and system resource limitations for handling infinite streams. In this work, we propose a novel live stream matching framework, called n-Snippet Indices Framework (in short, SNIF), to tackle these challenges. SNIF employs snippets as the basic unit for matching streaming time-series. The insight is to perform the matching at two levels of granularity: bag matching of subsets of snippets of the live stream against prefixes of the patterns, and order checking for maintaining successive candidate snippet bag matches. We design a two-level index structure, called SNIF index, ...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Abhishek Mukherji, Elke A. Rundensteiner, David C. Brown, Venkatesh Raghavan
• Keywords: CIKM 2008, Information Management, Live Stream, Similarity Matching, Stream Matching Framework