The design of the borealis stream processing engine

The design of the borealis stream processing engine (2005)

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by Daniel J. Abadi , Yanif Ahmad , Magdalena Balazinska , Mitch Cherniack , Jeong-hyon Hwang , Wolfgang Lindner , Anurag S. Maskey , Er Rasin , Esther Ryvkina , Nesime Tatbul , Ying Xing , Stan Zdonik

Venue:	In CIDR
Citations:	131 - 8 self

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Datum	Value	Source
TITLE	The design of the borealis stream processing engine	INFERENCE
AUTHOR NAME	Daniel J. Abadi	SVM HeaderParse 0.2
AUTHOR ADDR	1 MIT; Cambridge, MA	SVM HeaderParse 0.2
AUTHOR NAME	Yanif Ahmad	SVM HeaderParse 0.2
AUTHOR NAME	Magdalena Balazinska	SVM HeaderParse 0.2
AUTHOR NAME	Mitch Cherniack	SVM HeaderParse 0.2
AUTHOR NAME	Jeong-hyon Hwang	SVM HeaderParse 0.2
AUTHOR NAME	Wolfgang Lindner	SVM HeaderParse 0.2
AUTHOR NAME	Anurag S. Maskey	SVM HeaderParse 0.2
AUTHOR NAME	Er Rasin	SVM HeaderParse 0.2
AUTHOR NAME	Esther Ryvkina	SVM HeaderParse 0.2
AUTHOR NAME	Nesime Tatbul	SVM HeaderParse 0.2
AUTHOR NAME	Ying Xing	SVM HeaderParse 0.2
AUTHOR NAME	Stan Zdonik	SVM HeaderParse 0.2
AUTHOR AFFIL	2 Brown University	SVM HeaderParse 0.2
AUTHOR ADDR	Providence, RI	SVM HeaderParse 0.2
ABSTRACT	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 advanced capabilities that are commonly required by newly-emerging stream processing applications. In this paper, we outline the basic design and functionality of Borealis. Through sample real-world applications, we motivate the need for dynamically revising query results and modifying query specifications. We then describe how Borealis addresses these challenges through an innovative set of features, including revision records, time travel, and control lines. Finally, we present 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.	SVM HeaderParse 0.2
YEAR	2005	INFERENCE
VENUE	In CIDR	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	277--289	INFERENCE
CITATIONS	49 found	ParsCit 1.0