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Querying and reasoning over large scale building data sets: an outline of a performance benchmark

Pieter Pauwels UGent, Tarcisio Mendes de Farias, Chi Zhang, Ana Roxin, Jakob Beetz, Jos De Roo and Christophe Nicolle (2016) International Workshop on Semantic Big Data.
abstract
The architectural design and construction domains work on a daily basis with massive amounts of data. Properly managing, exchanging and exploiting these data is an ever ongoing challenge in this domain. This has resulted in large semantic RDF graphs that are to be combined with a significant number of other data sets (building product catalogues, regulation data, geometric point cloud data, simulation data, sensor data), thus making an already huge dataset even larger. Making these big data available at high performance rates and speeds and into the correct (intuitive) formats is therefore an incredibly high challenge in this domain. Yet, hardly any benchmark is available for this industry that (1) gives an overview of the kind of data typically handled in this domain; and (2) that lists the query and reasoning performance results in handling these data. In this article, we therefore present a set of available sample data that explicates the scale of the situation, and we additionally perform a query and reasoning performance benchmark. This results not only in an initial set of quantitative performance results, but also in recommendations in implementing a web-based system relying heavily on large semantic data. As such, we propose an initial benchmark through which new upcoming data management proposals in the architectural design and construction domains can be measured.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
in
International Workshop on Semantic Big Data
article number
11
pages
6 pages
publisher
Association for Computing Machinery
conference name
International Workshop on Semantic Big Data
conference location
San Francisco, California
conference start
2016-06-27
conference end
2016-07-01
ISBN
978-1-4503-4299-5
DOI
10.1145/2928294.2928303
language
English
UGent publication?
yes
classification
C1
copyright statement
I have transferred the copyright for this publication to the publisher
id
8041790
handle
http://hdl.handle.net/1854/LU-8041790
alternative location
http://dl.acm.org/citation.cfm?doid=2928294.2928303
date created
2016-07-28 13:56:37
date last changed
2016-12-21 15:41:05
@inproceedings{8041790,
  abstract     = {The architectural design and construction domains work on a daily basis with massive amounts of data. Properly managing, exchanging and exploiting these data is an ever ongoing challenge in this domain. This has resulted in large semantic RDF graphs that are to be combined with a significant number of other data sets (building product catalogues, regulation data, geometric point cloud data, simulation data, sensor data), thus making an already huge dataset even larger. Making these big data available at high performance rates and speeds and into the correct (intuitive) formats is therefore an incredibly high challenge in this domain. Yet, hardly any benchmark is available for this industry that (1) gives an overview of the kind of data typically handled in this domain; and (2) that lists the query and reasoning performance results in handling these data. In this article, we therefore present a set of available sample data that explicates the scale of the situation, and we additionally perform a query and reasoning performance benchmark. This results not only in an initial set of quantitative performance results, but also in recommendations in implementing a web-based system relying heavily on large semantic data. As such, we propose an initial benchmark through which new upcoming data management proposals in the architectural design and construction domains can be measured.},
  articleno    = {11},
  author       = {Pauwels, Pieter and Mendes de Farias, Tarcisio and Zhang, Chi and Roxin, Ana and Beetz, Jakob and De Roo, Jos and Nicolle, Christophe},
  booktitle    = {International Workshop on Semantic Big Data},
  isbn         = {978-1-4503-4299-5},
  language     = {eng},
  location     = {San Francisco, California},
  pages        = {6},
  publisher    = {Association for Computing Machinery},
  title        = {Querying and reasoning over large scale building data sets: an outline of a performance benchmark},
  url          = {http://dx.doi.org/10.1145/2928294.2928303},
  year         = {2016},
}

Chicago
Pauwels, Pieter, Tarcisio Mendes de Farias, Chi Zhang, Ana Roxin, Jakob Beetz, Jos De Roo, and Christophe Nicolle. 2016. “Querying and Reasoning over Large Scale Building Data Sets: An Outline of a Performance Benchmark.” In International Workshop on Semantic Big Data. Association for Computing Machinery.
APA
Pauwels, Pieter, Mendes de Farias, T., Zhang, C., Roxin, A., Beetz, J., De Roo, J., & Nicolle, C. (2016). Querying and reasoning over large scale building data sets: an outline of a performance benchmark. International Workshop on Semantic Big Data. Presented at the International Workshop on Semantic Big Data, Association for Computing Machinery.
Vancouver
1.
Pauwels P, Mendes de Farias T, Zhang C, Roxin A, Beetz J, De Roo J, et al. Querying and reasoning over large scale building data sets: an outline of a performance benchmark. International Workshop on Semantic Big Data. Association for Computing Machinery; 2016.
MLA
Pauwels, Pieter, Tarcisio Mendes de Farias, Chi Zhang, et al. “Querying and Reasoning over Large Scale Building Data Sets: An Outline of a Performance Benchmark.” International Workshop on Semantic Big Data. Association for Computing Machinery, 2016. Print.