Linear Road: A Stream Data Management Benchmark


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 Database) systems. Linear Road has been endorsed as an SDMS benchmark by the developers of both the Aurora [1] (out of Brandeis University, Brown University and MIT) and STREAM [8] (out of Stanford University) stream systems. Linear Road simulates a toll system for the motor vehicle expressways of a large metropolitan area. The tolling system uses “variable tolling” [6, 11, 9]: an increasingly prevalent tolling technique that uses such dynamic factors as traffic congestion 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 and the other using Aurora. Our results show that a dedicated Stream Data Management System can outperform a Relational Database by at least a factor of 5 on streaming data applications. ∗ This material is based on work supported by the National Science Foundation under Grant Nos. IIS-0118173 and IIS-9817799 (*), IIS0086057 (†), IIS-0325525 (‡) and IIS-0086002 (§). Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 30th VLDB Conference, Toronto, Canada, 2004

Extracted Key Phrases

8 Figures and Tables

Citations per Year

324 Citations

Semantic Scholar estimates that this publication has 324 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Arasu2004LinearRA, title={Linear Road: A Stream Data Management Benchmark}, author={Arvind Arasu and Mitch Cherniack and Eduardo F. Galvez and David Maier and Anurag Maskey and Esther Ryvkina and Michael Stonebraker and Richard Tibbetts}, booktitle={VLDB}, year={2004} }