S4: Distributed Stream Computing Platform

By Leonardo Neumeyer, Bruce Robbins, Anish Nair, A. Kesari. 2010 IEEE International Conference on Data Mining Workshops, 2010

983 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2f7f5d0e989c74d6279e2620e10e8d0b0c021cb7

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Citations in the corpus

Benchmarking Streaming Computation Engines: Storm, Flink and Spark Streaming (2016, methodology): “For example, at Yahoo, the platform of choice has been Apache Storm, which replaced the internally developed S4 platform [8].”
Distributed data stream processing and edge computing: A survey on resource elasticity and future directions (2017, positive): “The Simple Scalable Streaming System (S4) [59] is a distributed stream processing engine that uses the ac-”
Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017, background): “This is the target of some recent management frameworks, such as S4 [113] and D-Streams [114].”
Online Machine Learning in Big Data Streams (2018, background): “ease software development and debugging. While Map-Reduce is designed for batch processing, distributed data stream processing needs other solutions, such as communication between processing elements [174] via an interconnection topology [219]. For an outlook, mostly batch distributed data mining so”
The rise of "big data" on cloud computing: Review and open research issues (2015, background): “One of the most famous and powerful real time process-based big data tools is simple scalable streaming system (S4) [28].”
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (2014, background): “One of the most famous platforms is Storm, and others include S4 [128], SQLstream [5], Splunk [155], Apache Kafka [14], and SAP Hana [93] (see Table 2).”
Mining big data: current status, and forecast to the future (2013, background): “• Apache S4 [26]: platform for processing continuous data streams.”
In-Memory Big Data Management and Processing: A Survey (2015, methodology): “S4 (Simple Scalable Streaming System) [52] is a fully decentralized, distributed stream processing engine inspired by the MapReduce [283] and Actors model [99].”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, methodology): “• Stream Processing Model: S4 [36] and Storm [35] are two distributed stream processing platforms that run on the JVM.”
The role of big data in smart city (2016, background): “Many technologies can help process and act on real-time streaming unstructured data in real time such as Spark, Storm, and S4 (Neumeyer et al., 2010).”
Discretized streams: fault-tolerant streaming computation at scale (2013, methodology): “We ﬁrst report the raw scaling performance of Spark Streaming, and then compare it against two widely used streaming systems, S4 from Yahoo! and Storm from Twitter [29, 37].”
Twitter Heron: Stream Processing at Scale (2015, background): “The need for highly-scalable stream processing systems has lead to the creation of a number of recent systems, including [2, 3, 5, 9, 15, 17, 18].”
Machine Learning With Big Data: Challenges and Approaches (2017, background): “The importance of real-time processing in today’s era of sensors, mobile devices, and IoT has resulted in the emergence of a number of streaming systems; examples include Twitter’s Storm [52] and Yahoo’s S4 [53].”
Supporting Scalable Analytics with Latency Constraints (2015, background): “Recent fast data systems [2, 6, 8, 16, 24, 31, 32, 35, 37, 38, 39] do not take latency or job characteristics as input, and require the user to manually set the system parameters.”