Stream-Query Compilation with Ontologies

Özgür Lütfü Özçep; Ralf Möller; Christian Neuenstadt

doi:10.1007/978-3-319-26350-2_40

Stream-Query Compilation with Ontologies

Özgür Lütfü Özçep, Ralf Möller, Christian Neuenstadt

Institute of Information Systems

Abstract

Rational agents perceiving data from a dynamic environment and acting in it have to be equipped with capabilities such as decision making, planning etc. We assume that these capabilities are based on query answering with respect to (high-level) streams of symbolic descriptions, which are grounded in (low-level) data streams. Queries need to be answered w.r.t. an ontology. The central idea is to compile ontology-based stream queries (continuous or historical) to relational data processing technology, for which efficient implementations are available. We motivate our query language STARQL (Streaming and Temporal ontology Access with a Reasoning-Based Query Language) with a sensor data processing scenario, and compare the approach realized in the STARQL framework with related approaches regarding expressivity.

Original language	English
Title of host publication	AI 2015: Advances in Artificial Intelligence
Editors	Bernhard Pfahringer, Jochen Renz
Number of pages	7
Volume	9457
Place of Publication	Cham
Publisher	Springer International Publishing
Publication date	22.11.2015
Pages	457-463
ISBN (Print)	978-3-319-26349-6
ISBN (Electronic)	978-3-319-26350-2
DOIs	https://doi.org/10.1007/978-3-319-26350-2_40
Publication status	Published - 22.11.2015
Event	28th Australasian Joint Conference on Artificial Intelligence - Canberra, Australia Duration: 30.11.2015 → 04.12.2015 Conference number: 157849

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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10.1007/978-3-319-26350-2_40

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@inproceedings{1446a858d437464e89a9711247ca47f7,

title = "Stream-Query Compilation with Ontologies",

abstract = "Rational agents perceiving data from a dynamic environment and acting in it have to be equipped with capabilities such as decision making, planning etc. We assume that these capabilities are based on query answering with respect to (high-level) streams of symbolic descriptions, which are grounded in (low-level) data streams. Queries need to be answered w.r.t. an ontology. The central idea is to compile ontology-based stream queries (continuous or historical) to relational data processing technology, for which efficient implementations are available. We motivate our query language STARQL (Streaming and Temporal ontology Access with a Reasoning-Based Query Language) with a sensor data processing scenario, and compare the approach realized in the STARQL framework with related approaches regarding expressivity.",

author = "{\"O}z{\c c}ep, {{\"O}zg{\"u}r L{\"u}tf{\"u}} and Ralf M{\"o}ller and Christian Neuenstadt",

year = "2015",

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doi = "10.1007/978-3-319-26350-2_40",

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publisher = "Springer International Publishing",

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editor = "Bernhard Pfahringer and Jochen Renz",

booktitle = "AI 2015: Advances in Artificial Intelligence",

note = "28th Australasian Joint Conference on Artificial Intelligence, AI 2015 ; Conference date: 30-11-2015 Through 04-12-2015",

}

Özçep, ÖL , Möller, R & Neuenstadt, C 2015, Stream-Query Compilation with Ontologies. in B Pfahringer & J Renz (eds), AI 2015: Advances in Artificial Intelligence. vol. 9457, Lecture Notes in Computer Scienc, vol. 9457, Springer International Publishing, Cham, pp. 457-463, 28th Australasian Joint Conference on Artificial Intelligence, Canberra, Australian Capital Territory, Australia, 30.11.15. https://doi.org/10.1007/978-3-319-26350-2_40

Stream-Query Compilation with Ontologies. / Özçep, Özgür Lütfü ; Möller, Ralf; Neuenstadt, Christian.
AI 2015: Advances in Artificial Intelligence. ed. / Bernhard Pfahringer; Jochen Renz. Vol. 9457 Cham: Springer International Publishing, 2015. p. 457-463 (Lecture Notes in Computer Scienc; Vol. 9457).

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

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AB - Rational agents perceiving data from a dynamic environment and acting in it have to be equipped with capabilities such as decision making, planning etc. We assume that these capabilities are based on query answering with respect to (high-level) streams of symbolic descriptions, which are grounded in (low-level) data streams. Queries need to be answered w.r.t. an ontology. The central idea is to compile ontology-based stream queries (continuous or historical) to relational data processing technology, for which efficient implementations are available. We motivate our query language STARQL (Streaming and Temporal ontology Access with a Reasoning-Based Query Language) with a sensor data processing scenario, and compare the approach realized in the STARQL framework with related approaches regarding expressivity.

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ER -

Stream-Query Compilation with Ontologies

Abstract

DFG Research Classification Scheme

UN SDGs

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