TelegraphCQ: Continuous Dataflow Processing for an Uncertain World

By Sirish Chandrasekaran, Owen Cooper, Amol Deshpande, M. Franklin, Joseph M Hellerstein, W. Hong, S. Krishnamurthy, Samuel Madden, Vijayshankar Raman, Frederick Reiss and 1 more. Conference on Innovative Data Systems Research, 2003

1,203 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/739f18de865a83d048e0e9e0dcb3b224e32691be

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Citations in the corpus

Towards Expressive Publish/Subscribe Systems (2006, background): “In comparison, full-fledged Data Stream Management Systems (DSMS) [2, 25, 11] have powerful query languages that allow them to express much more powerful subscriptions than stateful pub/sub systems; however, this limits their scalability with the number of subscriptions, and existing DSMSs only do l”
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.”
Cayuga: A General Purpose Event Monitoring System (2007, background): “Stream databases such as [13, 7, 12, 6] lie at the opposite end of the spectrum, trading performance for expressiveness.”
Processing Sliding Window Multi-Joins in Continuous Queries over Data Streams (2003, methodology): “TelegraphCQ is designed for adaptive query processing , where query plans are re-ordered throughout the lifetime of a continuous query in response to changes in the execution environment (e.g. fluctuating stream arrival rates).”
Declarative Support for Sensor Data Cleaning (2006, methodology): “To provide a simple and flexible means of programming cleaning infrastructures, ESP uses declarative processing and exploits recent advances in relational processing techniques for data streams [4] [9] [12].”
Flux: an adaptive partitioning operator for continuous query systems (2003, unknown): “While Flux can be used with a wide range of lookup-based operators, in this paper we focus on its application to operators found in data-stream processing systems like Telegraph [5] and Aurora [4].”
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.”
The CQL continuous query language: semantic foundations and query execution (2006, methodology): “This type of window in which L = T has been referred to as a tumbling window in previous work [16, 18].”
No pane, no gain: efficient evaluation of sliding-window aggregates over data streams (2005, background): “Managing and processing streams gives rise to challenges that have been extensively discussed and recognized [3, 4, 6, 7, 12].”
A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing (2012, background): “By moving the computation to the cloud, many applications which could not be accommodated before due to the lack of signi.cant computation capability and energy power of mobile devices, will be made possible, while leveraging the stable and ample capacity of cloud.”
Linear Road: A Stream Data Management Benchmark (2004, unknown): “Stream data management has become a highly active research area and has inspired the development of several prototype systems including Aurora [1], STREAM [8], TelegraphCQ [4] and Niagara [5].”
High-performance complex event processing over streams (2006, methodology): “Languages for stream processing [2][7][19] lack constructs to address nonoccurrences of events and become unwieldy for specifying complex order-oriented constraints.”
Efficient pattern matching over event streams (2008, methodology): “In each stream, all events have the same type, stock, that contains three attributes, symbol, price, volume, with respective value ranges [1-2], [1-1000], [1-1000].”