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Stream processing: History, Applications, Research & Measurement

In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm that views streams, or sequences of events in time, as the central input and output objects of computation. Stream processing encompasses dataflow programming, reactive programming, and…

Language: English [EN]
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Stream processing topic overview

The analysis highlights History, Applications, Research and Measurement as prominent areas in the source structure around Stream processing.

Related topics
96
Source areas
7
Connected nodes
103
Extracted relationships
206
Concept neighborhoods
50
Bridge connections
103

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Stream programming libraries and languages · 41 topics
Overview · 25 topics
Research · 13 topics
Comparison to prior parallel paradigms · 9 topics
Historical examples and evolution · 4 topics
Applications · 2 topics
Code examples · 2 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Applications

Code examples

Comparison to prior parallel paradigms

Research

Historical examples and evolution

Stream programming libraries and languages

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Stream processing connects Entity context

The extracted context around Stream processing shows recurring relationship patterns in the source. For example, Stream processing → ACOTES Programming Model, An, Application Modelling Group, Ateji PX, Auto-Pipe, BeepBeep, Brook, C/C, CAL, CAL Actor Language, Cal2Many, Catalonia, Chicoutimi, CPU, CPUs, CUDA, Denver, Developed, DUP, Formal Computer Science Lab Another extracted example is Stream processing → AMD FireStream, APIs, Architectures, ATI R200, ATI R300, Dynamic Control Flow, Early, Early Vertex Programmability, Fixed-Function Era, Fragment/pixel, GPGPU, GPU, GPUs, Hardware, High-Performance Computing, HPC, Later, Microarchitectures, Modern, Nvidia NV20. Use these groups to spot repeated connection types before inspecting the individual relationships.

Stream processing

Top relations

related to Academic and open-source environments · 60
Stream processing → ACOTES Programming Model, An, Application Modelling Group, Ateji PX, Auto-Pipe, BeepBeep, Brook, C/C, CAL, CAL Actor Language, Cal2Many, Catalonia, Chicoutimi, CPU, CPUs, CUDA, Denver, Developed, DUP, Formal Computer Science Lab
related to Graphics processing units (GPUs) · 29
Stream processing → AMD FireStream, APIs, Architectures, ATI R200, ATI R300, Dynamic Control Flow, Early, Early Vertex Programmability, Fixed-Function Era, Fragment/pixel, GPGPU, GPU, GPUs, Hardware, High-Performance Computing, HPC, Later, Microarchitectures, Modern, Nvidia NV20
related to Commercial and proprietary architectures · 24
Stream processing → AccelerEyes, Declarative Engine, Embiot, Floodgate, Gamebryo, Google, GPU, Hewlett-Packard Cambridge Research Laboratory, IBM, IBM's, Intel, Jacket, MATLAB, OpenHMPP, PeakStream, RapidMind, SPADE, Stanford Brook, System, Telchemy
related to Managed cloud streaming services · 16
Stream processing → Amazon Web Services, Apache Beam, AWS, Cloud, Eventador, Google Cloud Dataflow, IBM Streams, IoT, Kafka, Kinesis, Microsoft Azure Stream Analytics, MongoDB Atlas Stream Processing, SPL, SQL, SQLStreamBuilder, Streaming Analytics
related to Distributed and cloud-scale execution ecosystems · 13
Stream processing → An, Apache Flink, Apache Kafka, Apache Samza, Apache Spark API, Apache Spark Streaming, Apache Storm, Distributed Logging Fabrics, Frameworks, Infrastructure, Micro-Batch Stream Processing, Modern, Native Continuous Stream Processing
related to Hardware-in-the-loop issues · 9
Stream processing → Although, Because, Consequently, Early, However, Inter-processor, PCI Express, To, While
related to Distributed event and complex event processing (CEP) engines · 8
Stream processing → An, Apache NiFi, Apama, Siddhi, Software AG, SQL-like, Wallaroo, WSO2 Stream Processor
related to Processor architecture comparisons · 8
Stream processing → ALUs, Consequently, CPU, CPUs, Historically, Stream, Structurally, To
related to Parallel stream paradigm (SIMD/MIMD) · 7
Stream processing → Although, ALUs, Compilers, In, Instead, Minimizing, This
related to Research · 6
Stream processing → Academic, AT, Concurrently, Early, Following, Stanford University

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

stream processing data hardware memory streaming execution programming streams kernel parallel software graphics units engine distributed model operations within language

Stream processing relationships Subject–Predicate–Object triples

TTTA extracted 206 structured relationships around Stream processing. Examples in this analysis include programming models → instance of → The software stack for these systems includes components and scoreboarding to initiate direct memory access → instance of → Stream processing hardware can use techniques. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
programming modelsinstance ofThe software stack for these systems includes components0.80text
query languagesinstance ofThe software stack for these systems includes components0.80text
for expressing computationinstance ofThe software stack for these systems includes components0.80text
scoreboarding to initiate direct memory accessinstance ofStream processing hardware can use techniques0.80text
arithmetic logic units.During the 1980s stream processing was explored within dataflow programminginstance ofwhile the reduced reliance on hardware cached I/O decreases the memory footprint required by specialized computational units0.80text
AltiVecinstance ofa configuration standard in architectures0.80text
Streaming SIMD Extensionsinstance ofa configuration standard in architectures0.80text
standard CPUsinstance ofthe stream processing model enforces structured dataflow and explicit memory management that permits significantly higher execution efficiency.When implemented on general-purpos…0.80text
stream processing abstractions frequently yielded limited performance gainsinstance ofthe stream processing model enforces structured dataflow and explicit memory management that permits significantly higher execution efficiency.When implemented on general-purpos…0.80text
with some historical studies noting an execution speedup of only approximately 1.5xinstance ofthe stream processing model enforces structured dataflow and explicit memory management that permits significantly higher execution efficiency.When implemented on general-purpos…0.80text
Cinstance ofStream programming libraries and languagesMost stream processing frameworks build upon established general-purpose languages0.80text
Stream processinghas applicationStream0.60section

Related concept clusters Concept neighborhoods

The concept neighborhoods around Stream processing bring nearby vocabulary together. In this analysis, examples include Stream, Hardware and Data. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Stream processing
    • Stream
    • Hardware
    • Data
    • Distributed
    • Streaming
    • Kernel
    • Engine
    • Execution
    • Programming
    • Event
    • Paradigm
    • Architectures
  • stream processing
    • Stream
    • Hardware
    • Data
    • Distributed
    • Execution
    • Engine
    • Graphics
    • Streaming
    • Kernel
    • Streams
    • Computation
    • Programming
  • programming paradigm
    • Computation
    • Model
    • Stream
    • University
    • Developed
    • Language
    • Streaming
    • Dataflow
    • Paradigm
    • Programming
    • Streams
    • Hardware
  • computation
    • Paradigm
    • Distributed
    • Programming
    • Parallel
    • Event
    • Execution
    • Architectures
    • Processing
    • Software
    • Units
    • Engine
    • Graphics
  • dataflow programming
    • Model
    • Computation
    • Stream
    • University
    • Developed
    • Language
    • Streaming
    • Dataflow
    • Paradigm
    • Programming
    • Streams
    • Hardware
  • reactive programming
    • Model
    • Computation
    • Stream
    • University
    • Developed
    • Language
    • Streaming
    • Dataflow
    • Paradigm
    • Streams
    • Hardware
    • Designed
  • distributed
    • Computation
    • Event
    • Streams
    • Architectures
    • Engine
    • Processing
    • Streaming
    • Programming
    • Paradigm
    • Stream
    • Processor
    • Architecture
  • data processing
    • Stream
    • Hardware
    • Streams
    • Data
    • Processing
    • Distributed
    • Execution
    • Engine
    • Graphics
    • Programming
    • Elements
    • Kernel

Connections between topic areas Semantic bridges

For Stream processing, one of the stronger structural bridges in this analysis connects Stream processing with Stream programming libraries and languages. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Stream processing — Stream programming libraries and languages · splits 62 ⟂ 42
Stream processing — Overview · splits 78 ⟂ 26
Stream processing — Research · splits 90 ⟂ 14
Stream processing — Comparison to prior parallel paradigms · splits 94 ⟂ 10
Stream processing — Historical examples and evolution · splits 99 ⟂ 5
Stream processing — Applications · splits 101 ⟂ 3
Stream processing — Code examples · splits 101 ⟂ 3

Map overview Semantic statistics

Stream processing

Nodes104
Edges103
Triples206
Avg. degree1.98
Density0.019231
Components1

Source & methodology

TTTA analyzes the structure around Stream processing to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Applications, Research & Measurement, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Stream processing · EN edition · Analysis: TopicsToTalkAbout

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