Bounded-Memory Stream Processing

Özgür Lütfü Özçep

doi:10.1007/978-3-030-00111-7_32

Bounded-Memory Stream Processing

Institute of Information Systems

Abstract

Foundational work on stream processing is relevant for different areas of AI and it becomes even more relevant if the work concerns feasible and scalable stream processing. One facet of feasibility is treated under the term bounded memory. In this paper, streams are represented as finite or infinite words and stream processing is modelled with stream functions, i.e., functions mapping one or more input stream to an output stream. Bounded-memory stream functions can process input streams by using constant space only. The main result of this paper is a syntactical characterization of bounded-memory functions by a form of safe recursion.

Original language	English
Title of host publication	KI 2018: Advances in Artificial Intelligence
Editors	Frank Trollmann, Anni-Yasmin Turhan
Number of pages	14
Volume	11117
Place of Publication	Cham
Publisher	Springer International Publishing
Publication date	30.08.2018
Pages	377-390
ISBN (Print)	978-3-030-00110-0
ISBN (Electronic)	978-3-030-00111-7
DOIs	https://doi.org/10.1007/978-3-030-00111-7_32
Publication status	Published - 30.08.2018
Event	41st German Conference on Artificial Intelligence - Berlin, Germany Duration: 24.09.2018 → 28.09.2018 Conference number: 218679

DFG Research Classification Scheme

4.43-01 Theoretical Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-030-00111-7_32

Cite this

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title = "Bounded-Memory Stream Processing",

abstract = "Foundational work on stream processing is relevant for different areas of AI and it becomes even more relevant if the work concerns feasible and scalable stream processing. One facet of feasibility is treated under the term bounded memory. In this paper, streams are represented as finite or infinite words and stream processing is modelled with stream functions, i.e., functions mapping one or more input stream to an output stream. Bounded-memory stream functions can process input streams by using constant space only. The main result of this paper is a syntactical characterization of bounded-memory functions by a form of safe recursion.",

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language = "English",

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pages = "377--390",

editor = "Frank Trollmann and Anni-Yasmin Turhan",

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note = "41st German Conference on Artificial Intelligence<br/>, KI 2018 ; Conference date: 24-09-2018 Through 28-09-2018",

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Özçep, ÖL 2018, Bounded-Memory Stream Processing. in F Trollmann & A-Y Turhan (eds), KI 2018: Advances in Artificial Intelligence. vol. 11117, Lecture Notes in Computer Science, vol. 11117, Springer International Publishing, Cham, pp. 377-390, 41st German Conference on Artificial Intelligence
, Berlin, Berlin, Germany, 24.09.18. https://doi.org/10.1007/978-3-030-00111-7_32

Bounded-Memory Stream Processing. / Özçep, Özgür Lütfü.
KI 2018: Advances in Artificial Intelligence. ed. / Frank Trollmann; Anni-Yasmin Turhan. Vol. 11117 Cham: Springer International Publishing, 2018. p. 377-390 (Lecture Notes in Computer Science; Vol. 11117).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

TY - GEN

T1 - Bounded-Memory Stream Processing

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N1 - Conference code: 218679

PY - 2018/8/30

Y1 - 2018/8/30

N2 - Foundational work on stream processing is relevant for different areas of AI and it becomes even more relevant if the work concerns feasible and scalable stream processing. One facet of feasibility is treated under the term bounded memory. In this paper, streams are represented as finite or infinite words and stream processing is modelled with stream functions, i.e., functions mapping one or more input stream to an output stream. Bounded-memory stream functions can process input streams by using constant space only. The main result of this paper is a syntactical characterization of bounded-memory functions by a form of safe recursion.

AB - Foundational work on stream processing is relevant for different areas of AI and it becomes even more relevant if the work concerns feasible and scalable stream processing. One facet of feasibility is treated under the term bounded memory. In this paper, streams are represented as finite or infinite words and stream processing is modelled with stream functions, i.e., functions mapping one or more input stream to an output stream. Bounded-memory stream functions can process input streams by using constant space only. The main result of this paper is a syntactical characterization of bounded-memory functions by a form of safe recursion.

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DO - 10.1007/978-3-030-00111-7_32

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BT - KI 2018: Advances in Artificial Intelligence

A2 - Trollmann, Frank

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CY - Cham

T2 - 41st German Conference on Artificial Intelligence<br/>

Y2 - 24 September 2018 through 28 September 2018

ER -

Bounded-Memory Stream Processing

Abstract

DFG Research Classification Scheme

UN SDGs

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