TelegraphCQ: continuous dataflow processing

By Sirish Chandrasekaran, Owen Cooper, Amol Deshpande, M. Franklin, Joseph M Hellerstein, W. Hong, S. Krishnamurthy, Samuel Madden, Frederick Reiss, Mehul A. Shah. ACM SIGMOD Conference, 2003

790 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5208060771fd213eefd827e3e1260b939f1aed6d

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Citations in the corpus

Issues in data stream management (2003, methodology): “Designing disk-based data structures and indices to exploit access patterns of stream archives is an open problem [12].”
MillWheel: Fault-Tolerant Stream Processing at Internet Scale (2013, background): “Much of the inspiration for streaming systems can be traced back to the pioneering work done on streaming database systems, such as TelegraphCQ [6], Aurora [2], and STREAM [24].”
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 Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing (2015, background): “query systems [1, 14, 15], windowing [22], data streams [24], time domains [28], semantic models [9]), to the more recent forays in low-latency processing such as Spark Streaming [34], MillWheel, and Storm [5], modern consumers of data wield remarkable amounts of power in shaping and taming massive-”
Combining Design and Performance in a Data Visualization Management System (2017, methodology): “To extract compound events from low-level event streams we could leverage a number of automata-based approaches that identify complex patterns in event streams [7, 13, 28, 30, 47, 58]; regular expression-based languages such as Proton [30] are used in the user interface literature to recognize user ”
The Design of the Borealis Stream Processing Engine (2005, background): “Several groups have developed working prototypes [1, 4, 16] and many papers have been published on detailed aspects of the technology such as data models [2, 5, 46], scheduling [8, 15], and load shedding [9, 20, 44].”
Focus: Querying Large Video Datasets with Low Latency and Low Cost (2018, background): “, [1,18,22,25,31,32,61,66,86, 87, 95]) and specific to video stream analytics (e.”