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The Design of the Borealis Stream Processing Engine
TLDR
This paper outlines the basic design and functionality of Borealis, and presents a highly flexible and scalable QoS-based optimization model that operates across server and sensor networks and a new fault-tolerance model with flexible consistency-availability trade-offs. Expand
Monitoring Streams - A New Class of Data Management Applications
TLDR
This paper presents Aurora, a new DBMS that is currently under construction at Brandeis University, Brown University, and M.I.T. and describes the basic system architecture, a stream-oriented set of operators, optimization tactics, and support for real-time operation. Expand
Load Shedding in a Data Stream Manager
TLDR
This paper examines a technique for dynamically inserting and removing drop operators into query plans as required by the current load, and addresses the problems of determining when load shedding is needed, where in the query plan to insert drops, and how much of the load should be shed at that point in the plan. Expand
Aurora: a new model and architecture for data stream management
TLDR
The basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications, are described and a stream-oriented set of operators are described. Expand
Plan-based complex event detection across distributed sources
TLDR
This paper presents an optimal but exponential-time dynamic programming algorithm and two polynomial-time heuristic algorithms, as well as their extensions for detecting multiple complex events with common sub-expressions, and describes the behavior and performance of the solutions. Expand
Retrospective on Aurora
TLDR
The key lessons learned throughout the design and implementation of the Aurora stream-processing engine are summarized and a follow-on project, called Borealis, is discussed, whose goal is to eliminate the limitations of Aurora as well as to address new key challenges and applications in the stream- processing domain. Expand
Neo: A Learned Query Optimizer
TLDR
Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them. Expand
Aurora: a data stream management system
TLDR
This work proposes to demonstrate the Aurora system with its development environment and runtime system, with several example monitoring applications developed in consultation with defense, financial, and natural science communities, and shows the effect of various system alternatives on various workloads. Expand
SECRET: A Model for Analysis of the Execution Semantics of Stream Processing Systems
TLDR
SECRET is a descriptive model that allows users to analyze the behavior of systems and understand the results of window-based queries for a broad range of heterogeneous SPEs. Expand
RIP: run-based intra-query parallelism for scalable complex event processing
TLDR
RIP - a Run-based Intra-query Parallelism technique for scalable pattern matching over event streams that distributes input events that belong to individual run instances of a pattern's Finite State Machine to different processing units, thereby providing fine-grained partitioned data parallelism. Expand
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