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Complex event processing (CEP) is an essential functionality for cross-reality environments. Through CEP, we can turn raw sensor data generated in the real world into more meaningful information that has some significance for the virtual world. In this article, the authors present DejaVu, a general-purpose event processing system built at ETH Zurich. SmartRFLib, a cross-reality application, builds on DejaVu and enables real-time event detection over RFID data streams feeding a virtual library on second life. 相似文献
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Aurora: a new model and architecture for data stream management 总被引:43,自引:0,他引:43
Daniel?J.?AbadiEmail author Don?Carney Ugur??etintemel Mitch?Cherniack Christian?Convey Sangdon?Lee Michael?Stonebraker Nesime?Tatbul Stan?Zdonik 《The VLDB Journal The International Journal on Very Large Data Bases》2003,12(2):120-139
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 human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS currently under construction at Brandeis University, Brown University, and M.I.T. We first provide an overview of the basic Aurora model and architecture and then describe in detail a stream-oriented set of operators.Received: 12 September 2002, Accepted: 26 March 2003, Published online: 21 July 2003Edited by Y. Ioannidis 相似文献
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Retrospective on Aurora 总被引:2,自引:0,他引:2
Hari Balakrishnan Magdalena Balazinska Don Carney Uğur Çetintemel Mitch Cherniack Christian Convey Eddie Galvez Jon Salz Michael Stonebraker Nesime Tatbul Richard Tibbetts Stan Zdonik 《The VLDB Journal The International Journal on Very Large Data Bases》2004,13(4):370-383
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 experience. Finally, we discuss our initial ideas on a follow-on project, called Borealis, whose goal is to eliminate the limitations of Aurora as well as to address new key challenges and applications in the stream-processing domain.Received: 21 October 2003, Accepted: 16 April 2004, Published online: 14 September 2004Edited by: J. Gehrke and J. Hellerstein. 相似文献
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Alexandru Moga Irina Botan Nesime Tatbul 《The VLDB Journal The International Journal on Very Large Data Bases》2011,20(6):867-892
This paper addresses the problem of minimizing the staleness of query results for streaming applications with update semantics
under overload conditions. Staleness is a measure of how out-of-date the results are compared with the latest data arriving
on the input. Real-time streaming applications are subject to overload due to unpredictably increasing data rates, while in
many of them, we observe that data streams and queries in fact exhibit “update semantics” (i.e., the latest input data are
all that really matters when producing a query result). Under such semantics, overload will cause staleness to build up. The
key to avoid this is to exploit the update semantics of applications as early as possible in the processing pipeline. In this
paper, we propose UpStream, a storage-centric framework for load management over streaming applications with update semantics.
We first describe how we model streams and queries that possess the update semantics, providing definitions for correctness
and staleness for the query results. Then, we show how staleness can be minimized based on intelligent update key scheduling
techniques applied at the queue level, while preserving the correctness of the results, even for complex queries that involve
sliding windows. UpStream is based on the simple idea of applying the updates in place, yet with great returns in terms of
lowering staleness and memory consumption, as we also experimentally verify on the Borealis system. 相似文献
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Query processing in sensor networks 总被引:1,自引:0,他引:1
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