The measurement process Uncertainty

SANY D2.3.4 Specification of the Sensor Service Architecture V3 Doc.V3.1 Copyright © 2007-2009 SANY Consortium Page 90 of 233

6.7.7 Quality control and management

Quality control and management is concerned with meta-information needed to enhance quality of information and services as well as to increase trust in information, data and services. Quality control and management is needed when certain criteria need to be fulfilled by data andor services. The SensorSA currently focuses on the following quality aspects of data: - Information about the measurement and data preparation process, e.g. measurement principle, calibration, spatial and temporal resolution - Uncertainty of measurements or model calculations, e.g. absolute and relative errors of measurement data or computational errors of data processing services. - Quality assurance of measurements, e.g. information about whether the measurements have been validated by machines or by humans. Each of these aspects is more or less relevant for a given application scenario. Often this level of detail is not necessary in order to classify the quality of data. The SensorSA allows an application designer to use the parts that are specifically relevant to their application.

6.7.7.1 The measurement process

The process used to take measurements obviously has a big influence on the quality of the gathered observations. Since for most applications this information is important when processing the observations, information about the measurement process has to be provided together with the observations. Examples of things that influence the measurement process can be: - Environmental conditions when taking the measurement - Type, manufacturer, model, etc of the measurement device - Operating parameters of the measurement device - Status of the measurement device error conditions, etc - Calibration processes applied to the measurement device - Amount of processing that has been applied to the data whether raw or filtered data Although this information is very important, it is very dissimilar in different application domains. Even within application domains e.g. air quality differences exist because of different legal regulations in different countries, for example. Thus only a generic data model can be specified to describe the measurement process. The SensorSA uses the schema defined by the OGC SensorML specification Botts, 2005 for the description of measurement processes. Furthermore, information that is specific to each measurement shall be encoded using the Observations Measurement schema defined in Cox, 2007 and described in section 7.2. SANY D2.3.4 Specification of the Sensor Service Architecture V3 Doc.V3.1 Copyright © 2007-2009 SANY Consortium Page 91 of 233

6.7.7.2 Uncertainty

All data in SensorSA has an associated uncertainty depending on the available meta- information on how the data was observed measured or derived from other data sources. We first address measurement uncertainty and then uncertainty of general data. Following ISO GUM 1993, Barry N. Taylor and Chris E. Kuyatt 1994 and UKAS 2007, measurement uncertainties may be classified into two categories: - Type A: uncertainty arising from a random effect; evaluated by statistical methods - Type B: uncertainty arising from a systematic effect, evaluated by other methods A common way of evaluating a type A uncertainty is to compute the standard deviation of the mean of a series of independent observations. A second common technique is an analysis of variance ANOVA and random effects in data in dependence of experimental parameters. Type B uncertainty is evaluated using scientific judgement. A typical cause is measurement bias due to the calibration of the measurement instrument or its behaviour in given environmental conditions e.g. temperature, air pressure, or over time deterioration of instrument, measurement drift. It is evaluated based on information about the instrument and environment. The measurement values may be corrected to compensate for known systematic effects. Note the distinction between the terms error of a measurement and uncertainty. Error is the differe nce between the measured value and the in general unknown „true value‟ of the measured property. Uncertainty is a quantified description of the doubt about the measurement result. The error of a measurement may be small, even though the uncertainty is large. In SensorSA data arises not only from sensor measurements and observations, but also from data processing with specific services, e.g. a kriging algorithm to generate a spatial coverage from a set of measurement points, or a time series analysis to produce a temporal interpolation. The results of such data processing steps are themselves uncertain, on the one hand due to the uncertainty of the input data, on the other hand due to the probabilistic or approximate nature of the processing itself. Uncertainty of data is typically expressed with one of the following - Probability density function, e.g. a normal distribution with known mean and variance. The data value would then lie within one standard deviation of the mean with probability 68 and within two standard deviations with probability 95. - Intervals the data value lies in [a,b]. This does not a-priori assume a uniform distribution on this interval; this would however be the case if the distribution of maximum entropy were chosen. An important special case is when then the measurement instrument can assert that the data value is below or above a given threshold, but can provide no further information. - Statistics such as standard deviation and moments, or quantiles the data value lies in [a,b] with probability 95. SANY D2.3.4 Specification of the Sensor Service Architecture V3 Doc.V3.1 Copyright © 2007-2009 SANY Consortium Page 92 of 233 Within the SensorSA, the uncertainty of data sets is described using the UncertML Williams et al, 2007. UncertML, which was developed within the INTAMAP project 12 , allows the information modeller to describe the uncertainty of a specific data set in an interchangeable way using an XML document conforming to the UncertML schema. This XML document can be embedded in a SensorML document to express information about the uncertainty of some process. In addition, UncertML can also be embedded in an Observation Measurement document Cox, 2007 to express the uncertainty of a specific sensor observation.

6.7.7.3 Quality assurance