Open AccessProceedings Article
TelegraphCQ: Continuous Dataflow Processing for an Uncertain World.
Sirish Chandrasekaran,Owen Cooper,Amol Deshpande,Michael J. Franklin,Joseph M. Hellerstein,Wei Hong,Sailesh Krishnamurthy,Samuel Madden,Vijayshankar Raman,Frederick Reiss,Mehul A. Shah +10 more
TLDR
The next generation Telegraph system, called TelegraphCQ, is focused on meeting the challenges that arise in handling large streams of continuous queries over high-volume, highly-variable data streams and leverages the PostgreSQL open source code base.Abstract:
Increasingly pervasive networks are leading towards a world where data is constantly in motion. In such a world, conventional techniques for query processing, which were developed under the assumption of a far more static and predictable computational environment, will not be sufficient. Instead, query processors based on adaptive dataflow will be necessary. The Telegraph project has developed a suite of novel technologies for continuously adaptive query processing. The next generation Telegraph system, called TelegraphCQ, is focused on meeting the challenges that arise in handling large streams of continuous queries over high-volume, highly-variable data streams. In this paper, we describe the system architecture and its underlying technology, and report on our ongoing implementation effort, which leverages the PostgreSQL open source code base. We also discuss open issues and our research agenda.read more
Citations
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TinyDB: an acquisitional query processing system for sensor networks
TL;DR: This work evaluates issues in the context of TinyDB, a distributed query processor for smart sensor devices, and shows how acquisitional techniques can provide significant reductions in power consumption on the authors' sensor devices.
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Mining time-changing data streams
TL;DR: An efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner is proposed, called CVFDT, which stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate.
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Data streams: algorithms and applications
TL;DR: Data Streams: Algorithms and Applications surveys the emerging area of algorithms for processing data streams and associated applications, which rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity.
Proceedings Article
The Design of the Borealis Stream Processing Engine
Daniel J. Abadi,Yanif Ahmad,Magdalena Balazinska,Mitch Cherniack,Jeong-Hyon Hwang,Wolfgang Lindner,Anurag S. Maskey,Alexander Rasin,Esther Ryvkina,Nesime Tatbul,Ying Xing,Stan Zdonik +11 more
TL;DR: 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.
Book
Data Streams: Algorithms and Applications
TL;DR: In this paper, the authors present a survey of basic mathematical foundations for data streaming systems, including basic mathematical ideas, basic algorithms, and basic algorithms and algorithms for data stream processing.
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