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This paper describes the basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications. Monitoring applications differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from(More)
This paper introduces monitoring applications, which we will show differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application(More)
Borealis is a second-generation distributed stream processing engine that is being developed at Brandeis University , Brown University, and MIT. Borealis inherits core stream processing functionality from Aurora [14] and distribution functionality from Medusa [51]. Bo-realis modifies and extends both systems in non-trivial and critical ways to provide(More)
This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping(More)
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(More)
Many stream-based applications are naturally distributed. Applications are often embedded in an environment with numerous connected computing devices with heterogeneous capabilities. As data travels from its point of origin (e.g., sensors) downstream to applications, it passes through many computing devices, each of which is a potential target of(More)
Many stream-based applications have sophisticated data processing requirements and real-time performance expectations that need to be met under asynchronous, time-varying data streams. In order to address these challenges, we propose novel operator scheduling approaches that specify (1) which operators to schedule (2) in which order to schedule the(More)
This document summarizes the research conducted in two interrelated projects. The Aurora project being implemented at Brown and Brandeis under the direction of U˘ gur C ¸ etintemel, Mitch Cherniack, Michael Stonebraker and Stan Zdonik strives to build a single-site high performance stream processing engine. It has an innovative collection of operators,(More)