Adaptive Stream Query Processing Approach for Linked Stream Data: (Extended Abstract)

Zia Ush Shamszaman

Adaptive Stream Query Processing Approach for Linked Stream Data: (Extended Abstract)

Zia Ush Shamszaman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Over the last few years, numerous efforts [1–4, 7] have been proposed based on
SPARQL-like query languages on harvesting Linked Stream Data (LSD) processing in RDF and related formats. While each existing processor has advantages,
neither of them wins in diverse settings. They differ on a wide range of aspects
including the execution method, operational semantics, streaming operators and
more. Considering state-of-the-art solutions, recent evaluations by [5, 6, 8] show
that C-SPARQL [2] suffers from duplicate results for simple queries and misses
some certain output in complex queries but provides more correct results than
others. On the otherhand CQELS [7] performs better than others in terms of
throughput and functionalities [6]. This diversity in output result is true for
other processors including EP-SPARQL [1] and StreamingSPARQL [3].

Original language	English
Title of host publication	Web Reasoning and Rule Systems
Subtitle of host publication	8th International Conference, RR 2014, Athens, Greece, September 15-17, 2014. Proceedings
Editors	Roman Kontchakov, Marie-Laure Mugnier
Publisher	Springer, Cham.
Pages	251-252
Number of pages	2
ISBN (Electronic)	9783319111131
ISBN (Print)	9783319111124
Publication status	Published - 6 Sept 2014
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Science and Business Media Deutschland GmbH
ISSN (Print)	0302-9743

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https://link.springer.com/book/10.1007/978-3-319-11113-1?page=2#toc

Cite this

Shamszaman, Z. U. (2014). Adaptive Stream Query Processing Approach for Linked Stream Data: (Extended Abstract). In R. Kontchakov, & M.-L. Mugnier (Eds.), Web Reasoning and Rule Systems: 8th International Conference, RR 2014, Athens, Greece, September 15-17, 2014. Proceedings (pp. 251-252). (Lecture Notes in Computer Science). Springer, Cham.. https://link.springer.com/book/10.1007/978-3-319-11113-1?page=2#toc

@inproceedings{eeb9798998e149ed8ad0eec202be2721,

title = "Adaptive Stream Query Processing Approach for Linked Stream Data: (Extended Abstract)",

abstract = "Over the last few years, numerous efforts [1–4, 7] have been proposed based on SPARQL-like query languages on harvesting Linked Stream Data (LSD) processing in RDF and related formats. While each existing processor has advantages, neither of them wins in diverse settings. They differ on a wide range of aspects including the execution method, operational semantics, streaming operators and more. Considering state-of-the-art solutions, recent evaluations by [5, 6, 8] show that C-SPARQL [2] suffers from duplicate results for simple queries and misses some certain output in complex queries but provides more correct results than others. On the otherhand CQELS [7] performs better than others in terms of throughput and functionalities [6]. This diversity in output result is true for other processors including EP-SPARQL [1] and StreamingSPARQL [3].",

author = "Shamszaman, \{Zia Ush\}",

year = "2014",

month = sep,

day = "6",

language = "English",

isbn = "9783319111124",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Cham.",

pages = "251--252",

editor = "Kontchakov, \{Roman \} and Mugnier, \{Marie-Laure \}",

booktitle = "Web Reasoning and Rule Systems",

}

Shamszaman, ZU 2014, Adaptive Stream Query Processing Approach for Linked Stream Data: (Extended Abstract). in R Kontchakov & M-L Mugnier (eds), Web Reasoning and Rule Systems: 8th International Conference, RR 2014, Athens, Greece, September 15-17, 2014. Proceedings. Lecture Notes in Computer Science, Springer, Cham., pp. 251-252. <https://link.springer.com/book/10.1007/978-3-319-11113-1?page=2#toc>

Adaptive Stream Query Processing Approach for Linked Stream Data: (Extended Abstract). / Shamszaman, Zia Ush.
Web Reasoning and Rule Systems: 8th International Conference, RR 2014, Athens, Greece, September 15-17, 2014. Proceedings. ed. / Roman Kontchakov; Marie-Laure Mugnier. Springer, Cham., 2014. p. 251-252 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Adaptive Stream Query Processing Approach for Linked Stream Data

T2 - (Extended Abstract)

AU - Shamszaman, Zia Ush

PY - 2014/9/6

Y1 - 2014/9/6

N2 - Over the last few years, numerous efforts [1–4, 7] have been proposed based on SPARQL-like query languages on harvesting Linked Stream Data (LSD) processing in RDF and related formats. While each existing processor has advantages, neither of them wins in diverse settings. They differ on a wide range of aspects including the execution method, operational semantics, streaming operators and more. Considering state-of-the-art solutions, recent evaluations by [5, 6, 8] show that C-SPARQL [2] suffers from duplicate results for simple queries and misses some certain output in complex queries but provides more correct results than others. On the otherhand CQELS [7] performs better than others in terms of throughput and functionalities [6]. This diversity in output result is true for other processors including EP-SPARQL [1] and StreamingSPARQL [3].

AB - Over the last few years, numerous efforts [1–4, 7] have been proposed based on SPARQL-like query languages on harvesting Linked Stream Data (LSD) processing in RDF and related formats. While each existing processor has advantages, neither of them wins in diverse settings. They differ on a wide range of aspects including the execution method, operational semantics, streaming operators and more. Considering state-of-the-art solutions, recent evaluations by [5, 6, 8] show that C-SPARQL [2] suffers from duplicate results for simple queries and misses some certain output in complex queries but provides more correct results than others. On the otherhand CQELS [7] performs better than others in terms of throughput and functionalities [6]. This diversity in output result is true for other processors including EP-SPARQL [1] and StreamingSPARQL [3].

M3 - Conference contribution

SN - 9783319111124

T3 - Lecture Notes in Computer Science

SP - 251

EP - 252

BT - Web Reasoning and Rule Systems

A2 - Kontchakov, Roman

A2 - Mugnier, Marie-Laure

PB - Springer, Cham.

ER -

Adaptive Stream Query Processing Approach for Linked Stream Data: (Extended Abstract)

Abstract

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