SSN2012 Deriving Semantic Sensor Metadata from Raw Measurements
1. 5th International Workshop on Semantic Sensor Networks at
ISWC2012
Deriving Semantic Sensor Metadata
from Raw Measurements
Jean-Paul Calbimonte1, Hoyoung Jeung2,
Zhixian Yan3, Oscar Corcho1, Karl Aberer3
1Ontology Engineering Group, Universidad Politécnica de Madrid
2SAP Research, Birsbane
3LSIR, EPFL Ecole Polytechnique Fédérale de Lausanne
jp.calbimonte@upm.es
Date: 14/11/2012
6. Semantic Sensor Web
“too much (streaming) data but not enough (tools to
gain and derive) knowledge”*
LinkedSensorData
Sensor data publishing
Linked Data LSM
Sense2Web
Semantic sensor metadata Sensor APIs
ETALIS
Semantic Sensor Network ontology Videk
SwissEx
BOTTARI,
UrbanMatch
AEMET
transporte.
linkeddata.es
SSN +CEP
…many many more working on this
* Sheth et al. 2008, Semantic Sensor Web
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7. SSN Ontology with other ontologies
tool for modeling our sensor data
~what we are observing
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14. Related Tasks
• Querying time series
• e.g. find a sub-sequence in a time series database
• Measuring time series similarity
• e.g. are these time series the same?
• Time series classification
• e.g. classify heart beat series: normal, murmur, et
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20. Linear Approximations
We care about the angles π/2
a π/4
a c b
d
0
a
c
-π/4
d
Divide the angle space in sectors
Distribution of angles in training set
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22. Use the representation for Classifying
Linear approximation
Compute distribution of the slopes
K-nearest neighbor classification
Training-Test datasets:
SwissExperiment
AEMET
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32. Conclusions
Classify Sensor Data
• Piecewise Linear Representation
• Segment slope distributions
• kNN classification
Generate Metadata
• Observed properties
• Potentially unknown metadata
Future work
• Combine with tag disambiguation?
• Use pattern mining for online queries
• Other techniques, shapelets, use
other parameters
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