A Machine Learning Approach to SPARQL Query Performance Prediction

Autor: Rakebul Hasan, Fabien Gandon

Publikováno v: The 2014 IEEE/WIC/ACM International Conference on Web Intelligence
The 2014 IEEE/WIC/ACM International Conference on Web Intelligence, Aug 2014, Warsaw, Poland
WI-IAT (2)
HAL

International audience; In this paper we address the problem of predicting SPARQL query performance. We use machine learning techniques to learn SPARQL query performance from previously executed queries. Traditional approaches for estimating SPARQL q

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07cd60e5e29b08fa11b627ee060dacee
https://hal.inria.fr/hal-01075484

Predicting SPARQL Query Performance and Explaining Linked Data

Autor: Rakebul Hasan

Publikováno v: 11th Extended Semantic Web Conference (ESWC2014)
11th Extended Semantic Web Conference (ESWC2014), May 2014, Crete, Greece. pp.795-805, ⟨10.1007/978-3-319-07443-6_53⟩
Lecture Notes in Computer Science ISBN: 9783319074429
ESWC

International audience; As the complexity of the Semantic Web increases, efficient ways to query the Semantic Web data is becoming increasingly impor-tant. Moreover, consumers of the Semantic Web data may need expla-nations for debugging or understan

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ab45d26c402c1cc0fea3af37a3e0781
https://hal.inria.fr/hal-01075488/file/eswc2014-dc.pdf

SPARQL Query Result Explanation for Linked Data

Autor: Rakebul Hasan, Endris, Kemele M., Fabien Gandon

Publikováno v: HAL
Semantic Web Collaborative Spaces Workshop 2014 (SWCS 2014), co-located with the 13th International Semantic Web Conference (ISWC 2014)
Semantic Web Collaborative Spaces Workshop 2014 (SWCS 2014), co-located with the 13th International Semantic Web Conference (ISWC 2014), Oct 2014, Riva del Garda, Italy

International audience; In this paper, we present an approach to explain SPARQL query results for Linked Data using why-provenance. We present a non-annotation-based algorithm to generate why-provenance and show its feasibility for Linked Data. We pr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db372b3165bbaaaf95bbf615e65be056

Collaborative Encoding of Asbru Clinical Protocols

Autor: Silvia Miksch, Marco Rospocher, Antonella Ferro, Claudio Eccher, Andreas Seyfang, Rakebul Hasan, Chiara Ghidini

Publikováno v: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783642236341
eHealth

Encoding guidelines and treatment protocols in formal and computer-executable form is not a trivial task and requires the collaboration between clinicians and knowledge engineers. In this paper, we describe CliP-MoKi, a Semantic Media Wiki (SMW)-base

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7918447a6277ad7fffcd866f9e983eb
http://hdl.handle.net/11562/990195

Predicting SPARQL Query Execution Time and Suggesting SPARQL Queries Based on Query History

Autor: Rakebul Hasan, Fabien Gandon

Publikováno v: HAL
[Research Report] RR-8392, Inria. 2013

In this paper first we address the problem of predicting SPARQL query execution time. Accurately predicting query execution time enables effective workload management, query scheduling, and query optimization. We use machine learning techniques to pr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::962b7b01348fdd8988aa991d86feb60a
https://hal.inria.fr/hal-00880314