Abstract
This paper specifies the Linear Road Benchmark for Stream Data Management Systems (SDMS). Stream Data Management Systems process streaming data by executing continuous and historical queries while producing query results in real-time. This benchmark makes it possible to compare the performance characteristics of SDMS' relative to each other and to alternative(e.g., relational) systems. Linear Road has been endorsed as an SDMS benchmark by both Aurora (out of Brandeis University, Brown University and MIT) and STREAM (out of Stanford University).Linear Road is inspired by the increasing prevalence of ``variable tolling'' on highway systems throughout the world. Variable tolling uses dynamically determined factors such as congestion levels and accident proximity to calculate toll charges. Linear Road specifies a variable tolling system for a fictional urban area including such features as accident detection and alerts, traffic congestion measurements, toll calculations and historical queries. After specifying the benchmark, we describe experimental results involving two implementations: one using a commercially available Relational Database Management System and the other using Aurora. Our results show that a dedicated Stream Data Management System can outperform a Relational Database Management System by at least a factor of 5 on streaming data applications.