Addressing Streaming and Historical Data in OBDA Systems: Optique's Approach (Statement of Interest). Horrocks, I., Hubauer, T., Jiménez-Ruiz, E., Kharlamov, E., Koubarakis, M., M̈oller, R., Bereta, K., Neuenstadt, C., ̈Oz̧cep, ̈.O., Roshchin, M., Smeros, P., & Zheleznyakov, D. In Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD), 2013. Paper abstract bibtex 1 download In large companies such as Siemens and Statoil monitoring tasks are of great importance, e.g., Siemens does monitoring of turbines and Statoil of oil behaviour in wells. This tasks bring up importance of both streaming and historical (temporal) data in the Big Data challenge for industries. We present the Optique project that addresses this problem by developing an Ontology Based Data Access (OBDA) system that incorporates novel tools and methodologies for processing and analyses of temporal and streaming data. In particular, we advocate for modelling time time aware data by temporal RDF and reduce monitoring tasks to knowledge discovery and data mining.
@inproceedings{ kwnolod_wp5,
abstract = {In large companies such as Siemens and Statoil monitoring tasks are
of great importance, e.g., Siemens does monitoring of turbines and Statoil of oil
behaviour in wells. This tasks bring up importance of both streaming and historical (temporal) data in the Big Data challenge for industries. We present the
Optique project that addresses this problem by developing an Ontology Based
Data Access (OBDA) system that incorporates novel tools and methodologies for
processing and analyses of temporal and streaming data. In particular, we advocate for modelling time time aware data by temporal RDF and reduce monitoring
tasks to knowledge discovery and data mining.},
added-at = {2013-04-26T16:07:25.000+0200},
audience = {academic},
author = {Horrocks, Ian and Hubauer, Thomas and Jiménez-Ruiz, Ernesto and Kharlamov, Evgeny and Koubarakis, Manolis and M̈oller, Ralf and Bereta, Konstantina and Neuenstadt, Christian and ̈Oz̧{c}ep, ̈Ozgür and Roshchin, Mikhail and Smeros, Panayiotis and Zheleznyakov, Dmitriy},
biburl = {http://www.bibsonomy.org/bibtex/223dfd4317d59c272464b0562b26e07a3/ernesto.jimenez},
booktitle = {Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data (Know@LOD)},
interhash = {a418e84834dd9ec52c34c0cf07b4e9b5},
intrahash = {23dfd4317d59c272464b0562b26e07a3},
keywords = {myown optique-project optiqueproject},
partneroptique = {UOXF},
title = {{Addressing Streaming and Historical Data in OBDA Systems: Optique's Approach (Statement of Interest)}},
url = {http://www.cs.ox.ac.uk/files/5517/knowlod_wp5.pdf},
wpoptique = {WP5},
year = {2013},
yearoptique = {Y1}
}
Downloads: 1
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This tasks bring up importance of both streaming and historical (temporal) data in the Big Data challenge for industries. We present the\r\nOptique project that addresses this problem by developing an Ontology Based\r\nData Access (OBDA) system that incorporates novel tools and methodologies for\r\nprocessing and analyses of temporal and streaming data. 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