SEKE 2012 Proceedings - Knowledge Systems Institute

with specific data granularity and quantitative values, have to
be built for specific parts of the season and diurnal cycles.
Data Properties can be specified to docum ent scientific
knowledge about processes or to identify anomalies in
scientific sensor data. D ata properties specified to detect
anomalies in sensor data are typically specific about the sensor
names and thresholds over which the data should be evaluated,
can be interpreted and used to evaluate data by SDVe without
further manipulation to the property, and can be used to
document the scientific processes. D ata properties that are
specified for the sole purpose of documenting processes are
typically general in their descriptions, e.g., only describe
which sensor reading will be evaluated and how it will be
used, but do not include the speci fic name or threshold values
to be evaluated. Data propertie s for documenting processes
might also include computationa l methods that need to be
applied to data before the data can be evaluated. These types
of data properties are not suitable for the current version of
SDVe.
VII. RELATED WORK
Some of the approaches that are frequently used to detect
anomalies in sensor data are described in this section.
The Intelligent Outlier Detection Algorithm (IODA) [16] is
a technique used to perform quality control on tim e series
data. IODA uses statistics, graph theory, image processing and
decision trees to determine if data are correct. The I ODA
algorithm compares incoming data, which are treated as
images, to common patterns of failure. SDVe differs from this
approach in that it is a scientist-centered approach in which the
anomaly detection process is based on the expert scientific
knowledge captured by the data properties.
Dereszynski & Dietterich [1 7] use a Dynamic Bayesian
Network (DBN) [18] approach to automatic data cleaning for
individual air temperature data streams. The D BN combines
discrete and conditional linear-Gaussian random variables to
model the air temperature as a function of diurnal, seasonal,
and local trend effects. The approach uses a general fault
model to classify different type of anomalies. SDVe differs
from this approach in that no mathematical or logic knowledge
has to be acquired by the scientists to verify their data as it is
the case with the DBN.
EdiRe [19] is a software tool for eddy covariance and
microclimatological measurement analysis. EdiRe is adaptable
to most eddy covariance raw da ta formats and microclimate
data, however it requires processing routines to be developed
and redesigned to address the different areas associated with
data analysis as opposed to SDVe that do not required any
software implementation or recompilation.
VIII. SUMMARY
Scientists collecting large amounts of complex
environmental sensor data need to be assured that their data
are correct in a timely fashion. The SDve prototype tool adapts
software engineering techniqu es to allow scientists to verify
sensor datasets against predefined form ally specified data
properties. SDVe automatically verifies datasets and raises
flags whenever anomalies occur in the datasets. SD Ve has
been used to identify anomalies in eddy covariance data.
ACKNOWLEDGMENT
The authors would like to thank Dr. Deanna Pennington,
Dr. Craig Tweedie and Aline Jaim es for their invaluable input
towards this work.
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