Query Processing, Approximation, and Resource Management in a Data Stream Management System

By Rajeev Motwani, Jennifer Widom, Arvind Arasu, Brian Babcock, Shivnath Babu, Mayur Datar, Gurmeet Singh Manku, Christopher Olston, J. Rosenstein, Rohit Varma. Conference on Innovative Data Systems Research, 2003

770 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d1894be899516751edb9eb57fe45287ecdbf3120

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Citations in the corpus

Towards a Big Data Analytics Framework for IoT and Smart City Applications (2015, background): “Some of the known early academic projects are TelegraphCQ [ 8], TinyDB [ 36], STREAM [ 41], Aurora/Borealis [Error! Reference source not found.”
The Design of the Borealis Stream Processing Engine (2005, background): “To date systems have exclusively focused on either a server-heavy environment [14, 17, 32] or a sensor-heavy environment [31].”
TinyDB: an acquisitional query processing system for sensor networks (2005, methodology): “,single-node) location to store a streaming view of recent data similar to materialization points in other streaming systems like Aurora, TelegraphCQ, or STREAM [Carney et al. 2002; Chandrasekaran et al. 2003; Motwani et al. 2003], or materialized views in conventional databases.”
Distributed top-k monitoring (2003, background): “Recently, much attention has been focused on online monitoring applications, in which continuous queries operate in near real-time over data streams such as call records, sensor readings, web usage logs, network packet traces, etc. [CCC + 02, CDTW00, MSHR02 , MWA + 03].”
Flux: an adaptive partitioning operator for continuous query systems (2003, background): “Another direction has been to exploit weaker semantics and sacrifice result quality for cases in which the system cannot be scaled to match peak workloads [18, 4].”
Streaming Pattern Discovery in Multiple Time-Series (2005, methodology): “Data stream systems have been prototyped [1, 29, 9] and deployed in practice [11].”
The CQL continuous query language: semantic foundations and query execution (2006, methodology): “If the system uses a query-evaluation strategy based on materializing the windows specified in a query, then filtering before applying the window instead of after is preferable since it reduces steady-state memory overhead [33].”
Processing Sliding Window Multi-Joins in Continuous Queries over Data Streams (2003, methodology): “As surveyed in [4, 15], there has been a great deal of recent interest in developing novel data management techniques and adapting traditional database technology to the data stream model; Cougar [32], Aurora [7], TelegraphCQ [8, 22], and STREAM [4, 23] are some examples.”
Approximate counts and quantiles over sliding windows (2004, background): “Tuple-based windows [22] correspond exactly to fixed-size windows.”
Operator Scheduling in a Data Stream Manager (2003, background): “Their initial scheduling goal involves minimization of the intermediate queue sizes [14], an issue that we do not directly address in this paper.”
Fault-tolerance in the Borealis distributed stream processing system (2005, background): “Stream processing engines (SPEs) (also known as data stream managers [1, 30] or continuous query processors [14]) are a class of software systems that handle the data processing requirements mentioned above.”
Linear Road: A Stream Data Management Benchmark (2004, background): “Linear Road has been endorsed as an SDMS benchmark by the developers of both the Aurora [1] (out of Brandeis University, Brown University and MIT) and STREAM [8] (out of Stanford University) stream systems.”
High-availability algorithms for distributed stream processing (2005, background): “emerging class of applications that require sophisticatedan timely processing of high-volume data streams, often originating in distributed environments.”