Towards Approximate Event Processing in a Large-Scale Content-Based Network
Event matching is a critical component of large-scale content-based publish/subscribe systems. However, most existing methods suffer from a dramatic performance degradation when the system scales up. In this paper, the authors present TAMA (TAble MAtch), a highly efficient content-based event matching and forwarding engine. The authors consider range-based attribute constraints that are widely used in real-world applications. TAMA employs approximate matching to provide fast event matching against an enormous amount of subscriptions. To this end, TAMA uses a hierarchical indexing table to store subscriptions. Event matching in TAMA becomes the query to this table, which is substantially faster than traditional methods.