Ugur Çetintemel

Learn More
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)
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]. Borealis modifies and extends both systems in non-trivial and critical ways to provide(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)
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)
Stream-processing systems are designed to support an emerging class of applications that require sophisticated and timely processing of high-volume data streams, often originating in distributed environments. Unlike traditional data-processing applications that require precise recovery for correctness, many stream-processing applications can tolerate and(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)
Applications that require real-time processing of high-volume data steams are pushing the limits of traditional data processing infrastructures. These stream-based applications include market feed processing and electronic trading on Wall Street, network and infrastructure monitoring, fraud detection, and command and control in military environments.(More)
The Aurora system [1] is an experimental data stream management system with a fully functional prototype. It includes both a graphical development environment, and a runtime system. We propose to demonstrate the Aurora system with its development environment and runtime system, with several example monitoring applications developed in consultation with(More)
This experience paper summarizes the key lessons we learned throughout the design and implementation of the Aurora stream-processing engine. For the past 2 years, we have built five stream-based applications using Aurora. We first describe in detail these applications and their implementation in Aurora. We then reflect on the design of Aurora based on this(More)
Accurate query performance prediction (QPP) is central to effective resource management, query optimization and query scheduling. Analytical cost models, used in current generation of query optimizers, have been successful in comparing the costs of alternative query plans, but they are poor predictors of execution latency. As a more promising approach to(More)