Stream-temporal Querying with Ontologies

Ralf Möller; Christian Neuenstadt; Özgür L. Özçep

Stream-temporal Querying with Ontologies

Ralf Möller, Christian Neuenstadt, Özgür L. Özçep

Institute of Information Systems

Abstract

Recent years have seen theoretical and practical efforts on temporalizing and streamifying ontology-based data access (OBDA). This paper contributes to the practical efforts with a description/evaluation of a prototype implementation for the stream-temporal query language framework STARQL. STARQL serves the needs for industrially motivated scenarios, providing the same interface for querying historical data (reactive diagnostics) and for querying streamed data (continuous monitoring, predictive analytics). We show how to transform STARQL queries w.r.t. mappings into standard SQL queries, the difference between historical and continuous querying relying only in the use of a static window table vs. an incrementally updated window table. Experiments with a STARQL prototype engine using the PostgreSQL DBMS show the implementability and feasibility of our approach.

Original language	English
Title of host publication	HiDeSt '15---Proceedings of the First Workshop on High-Level Declarative Stream Processing (co-located with KI 2015)
Editors	Daniela Nicklas, Özgür L. Özçep
Number of pages	14
Volume	1447
Publisher	CEUR-WS.org
Publication date	01.09.2015
Pages	42-55
Publication status	Published - 01.09.2015
Event	1st Workshop on High-Level Declarative Stream Processing, HiDeSt 2015 - co-located with the 38th German AI Conference, KI 2015 - Dresden, Germany Duration: 21.09.2015 → 25.09.2015 Conference number: 115610

DFG Research Classification Scheme

409-06 Information Systems, Process and Knowledge Management

UN SDGs

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

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Cite this

@inproceedings{cdc6d59d6c554062a1219977a187006b,

title = "Stream-temporal Querying with Ontologies",

abstract = "Recent years have seen theoretical and practical efforts on temporalizing and streamifying ontology-based data access (OBDA). This paper contributes to the practical efforts with a description/evaluation of a prototype implementation for the stream-temporal query language framework STARQL. STARQL serves the needs for industrially motivated scenarios, providing the same interface for querying historical data (reactive diagnostics) and for querying streamed data (continuous monitoring, predictive analytics). We show how to transform STARQL queries w.r.t. mappings into standard SQL queries, the difference between historical and continuous querying relying only in the use of a static window table vs. an incrementally updated window table. Experiments with a STARQL prototype engine using the PostgreSQL DBMS show the implementability and feasibility of our approach.",

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

year = "2015",

month = sep,

day = "1",

language = "English",

volume = "1447",

series = "CEUR Workshop Proceedings",

publisher = "CEUR-WS.org",

pages = "42--55",

editor = "Daniela Nicklas and {\"O}z{\c c}ep, {{\"O}zg{\"u}r L.}",

booktitle = "HiDeSt '15---Proceedings of the First Workshop on High-Level Declarative Stream Processing (co-located with KI 2015)",

note = "1st Workshop on High-Level Declarative Stream Processing, HiDeSt 2015 - co-located with the 38th German AI Conference, KI 2015 ; Conference date: 21-09-2015 Through 25-09-2015",

}

Möller, R, Neuenstadt, C & Özçep, ÖL 2015, Stream-temporal Querying with Ontologies. in D Nicklas & ÖL Özçep (eds), HiDeSt '15---Proceedings of the First Workshop on High-Level Declarative Stream Processing (co-located with KI 2015). vol. 1447, CEUR Workshop Proceedings, vol. 1447, CEUR-WS.org, pp. 42-55, 1st Workshop on High-Level Declarative Stream Processing, HiDeSt 2015 - co-located with the 38th German AI Conference, KI 2015, Dresden, Saxony, Germany, 21.09.15. <http://ceur-ws.org/Vol-1447/paper4.pdf>

Stream-temporal Querying with Ontologies. / Möller, Ralf; Neuenstadt, Christian; Özçep, Özgür L.
HiDeSt '15---Proceedings of the First Workshop on High-Level Declarative Stream Processing (co-located with KI 2015). ed. / Daniela Nicklas; Özgür L. Özçep. Vol. 1447 CEUR-WS.org, 2015. p. 42-55 (CEUR Workshop Proceedings; Vol. 1447).

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

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AU - Möller, Ralf

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AB - Recent years have seen theoretical and practical efforts on temporalizing and streamifying ontology-based data access (OBDA). This paper contributes to the practical efforts with a description/evaluation of a prototype implementation for the stream-temporal query language framework STARQL. STARQL serves the needs for industrially motivated scenarios, providing the same interface for querying historical data (reactive diagnostics) and for querying streamed data (continuous monitoring, predictive analytics). We show how to transform STARQL queries w.r.t. mappings into standard SQL queries, the difference between historical and continuous querying relying only in the use of a static window table vs. an incrementally updated window table. Experiments with a STARQL prototype engine using the PostgreSQL DBMS show the implementability and feasibility of our approach.

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M3 - Conference contribution

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BT - HiDeSt '15---Proceedings of the First Workshop on High-Level Declarative Stream Processing (co-located with KI 2015)

A2 - Nicklas, Daniela

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Y2 - 21 September 2015 through 25 September 2015

ER -

Stream-temporal Querying with Ontologies

Abstract

DFG Research Classification Scheme

UN SDGs

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