=Paper= {{Paper |id=None |storemode=property |title=Deriving Semantic Sensor Metadata from Raw Measurements |pdfUrl=https://ceur-ws.org/Vol-904/paper4.pdf |volume=Vol-904 |dblpUrl=https://dblp.org/rec/conf/semweb/CalbimonteYJCA12 }} ==Deriving Semantic Sensor Metadata from Raw Measurements== https://ceur-ws.org/Vol-904/paper4.pdf

Deriving Semantic Sensor Metadata from Raw
Measurements

Jean-Paul Calbimonte1 , Zhixian Yan2 , Hoyoung Jeung3 , Oscar Corcho1 , and
Karl Aberer2
1
OEG, Facultad de Informática,Universidad Politécnica de Madrid, Spain
jp.calbimonte@upm.es,ocorcho@fi.upm.es
2
LSIR, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
zhixian.yan@epfl.ch,karl.aberer@epfl.ch
3
SAP Research, Brisbane, Australia
hoyoung.jeung@sap.com

Abstract. Sensor network deployments have become a primary source
of big data about the real world that surrounds us, measuring a wide
range of physical properties in real time. With such large amounts of
heterogeneous data, a key challenge is to describe and annotate sensor
data with high-level metadata, using and extending models, for instance
with ontologies. However, to automate this task there is a need for en-
riching the sensor metadata using the actual observed measurements and
extracting useful meta-information from them.
This paper proposes a novel approach of characterization and extrac-
tion of semantic metadata through the analysis of sensor data raw ob-
servations. This approach consists in using approximations to represent
the raw sensor measurements, based on distributions of the observa-
tion slopes, building a classification scheme to automatically infer sensor
metadata like the type of observed property, integrating the semantic
analysis results with existing sensor networks metadata.

1 Introduction
Ubiquitous sensor networks are a primary source of observations from the phys-
ical world, from environmental measuring stations, participatory or citizen sens-
ing, to various sensor applications in traffic, media and health monitoring. Pub-
lishing sensor networks data on the web has the potential of increasing public
awareness and involvement on these different domains at a massive scale [1].
Cheap sensing devices can be easily configured and deployed, plugged to sensor
data platforms such as Cosm1 for exploitation, storage and querying.

The increasing availability of sensor data in the web introduces higher het-
erogeneity, which makes it more difficult for potential users to make sense out
of these data sources and be able to identify which ones are useful for their
applications. An example of this scenario is the Swiss Experiment2 project, a
1
Cosm, formerly Pachube https://cosm.com/
2
Swiss Experiment: http://www.swiss-experiment.ch/
platform that enables real-time publishing environmental data on the web, from
a large-scale federation of sensor networks, mainly in the Swiss Alps. The pub-
lished data is heterogeneous as it comes from different geographical locations,
with different time spans (e.g. observations collected during 1 year, 3 months,
etc.), as well as varying sampling rates (e.g. per minute, per 10 minutes). More-
over, the metadata for these sensor types is not always complete and coherent.
As an example, to indicate that a sensor measures temperature (i.e. the observed
property), different sensors use various tag names, like “temperature”, “temp”,
“t”, “msptemperature”, “tp”, etc. Although the data is available for anyone to
use, these noisy descriptions are not understandable enough and do not provide
semantic information about what this data is about.

In less-controlled scenarios than the Swiss Experiment, the problems of het-
erogeneity are even more noticeable. For instance in the Cosm web platform,
users tag their sensor data as means of metadata, identifying which types of
measurements they are publishing. Projects like the Air Quality Egg3 , aiming at
promoting air-quality participatory sensing, enable almost any citizen to pub-
lish measurements at web-scale. However, the user-provided metadata is often
incomplete. In many cases these tags are misleading or they are not provided at
all, making it very hard for other users to query or make use of this data.
To overcome this problem, establishing explicit semantics on the metadata
has been proposed in previous works, using sensor ontologies [2]. When using
these ontologies, sometimes it is needed to manually map the semantic informa-
tion from the sources to the new metadata model [3], which is a cumbersome and
error-prone task. In this paper we propose a novel approach of semantic sensor
analysis that infers semantic properties such as the type of observed property,
using the raw sensor observations as input. The main contributions of this paper
are the following:

– We propose a novel method for representing time series as distributions that
represent the slopes of a linear approximation of the initial numeric sensor
measurements.
– Based on the statistics of the observation slopes, we infer the type of observed
property of the sensor measurements. We use a classification method that
exploits the similarity of the slopes distributions.
– We provide a mechanism for enriching the sensor metadata, based on the
SSN Ontology [2], with the metadata inferred from the observation slopes.
– We build a self-contained evaluation system linking raw sensor measure-
ments to high-level semantics, and validate our method using two real-life
environmental sensor datasets, from the Swiss Experiment and AEMET4 (the
Spanish meteorological office).

The remainder of this paper is organized as follows: Section 2 describes the
global approach proposed for semantic analysis of sensor data. Section 3 studies
3
AirQuality Egg http://airqualityegg.wikispaces.com/
4
Agencia Estatal de Metereologı́a: http://www.aemet.es
the sensor data representation using slopes, whereas Section 4 focuses on build-
ing classification algorithm for inferring observed property types and integrating
them to the sensor metadata. In Section 5, we experimentally evaluate our ap-
proach. Section 6 summarizes existing related work. Finally, Section 7 includes
concluding remarks and points to future works.

2 From Raw Measurements to Semantic Metadata
Sensor data is typically represented as time series, describing the evolution over
time of a certain observed property. Raw sensor data without any metadata
that describes it, has limited use as it is hard to discover, integrate or interpret.
While in controlled environments the sensor metadata can be reasonably well
managed and controlled by the data owners, in the context of the sensor web,
where any citizen is able to produce and publish data, it becomes a more diffi-
cult task. While semantic metadata has been shown to be effective for managing
large sensor metadata repositories, current proposals require expensive manual
curation and tagging (see Section 6). However, these approaches do not look into
the data values, from which we can derive some of these metadata properties
using analysis and mining techniques.

We describe in Figure 1 our architecture for deriving semantic metadata from
sensor data measurements. The approach includes characterizing sensor time
series and extracting their observed property types to enrich sensor metadata,
and consists of four main layers:

– At the sensor deployment layer, sensor nodes provide initial measurements
in terms of real-time numerical values, e.g. temperature, humidity, etc.
– In the semantic sensor analysis layer, we first represent the sensor data
stream using linear approximations and calculate the observation slopes.
Based on the sensor slopes, we are able to compute similarity between sensor
data series, detecting the observed property types through classification, and
performing detection of these types with partial information.
– A semantic representation of the analysis component is integrated into the
semantic metadata. Using the SSN Ontology as a basis, and combined with
domain specific ontologies, this enriched metadata is made available for fur-
ther processing or querying.
– In the application layer, users can build tools and visualizations to query such
sensor data and receive results that include the new metadata computed by
the analysis layer.

The deployment layer is usually built using sensor or stream data manage-
ment systems. These systems centralize the data captured by the devices and
provide storage, query interfaces and streaming operators. As for the seman-
tic metadata, we built upon previous work on semantic management of sensor
networks [3], centered on the use of the SSN Ontology, coupled with domain
ontologies and vocabularies for quantities and units of measurements. For the
analysis of the time series, we propose a representation based on the slopes of
a linear approximation of the data, as described in Section 3. Then these repre-
sentations can be used to compare and find similarities among new and existing
time series, classifying them according to the detected observed property type,
etc. As a result, we are able to complete and query the sensor metadata, as
detailed in Section 4.

Fig. 1: Semantic Sensor Analysis Architecture

3 Sensor Data Representation with Slopes
In environmental time series, similar patterns can be observed periodically over
time. These patterns can be characteristic to a type of sensor data, and therefore
help to recognize it. If we represent a time series using a linear representation,
such as the one in Figure 2(b), the patterns of the data can be associated to the
angles of the linear segments or its corresponding slope. For instance, a steep
slope indicates a sudden increase of the measured property. The intuition is that
if these slopes are repetitive over time, we can build slope distributions that can
be representative of a type of time series. Using slopes makes it possible to find
similarities between time series that not necessarily have the same value ranges
but similar behavior, e.g such as the air temperature in two different locations.

(a) Linear approximation (b) Constructing the convex hulls and segments
Fig. 2: Piecewise linear representations
3.1 Piecewise Linear Representation

We can use linear segments to approximate a time series (Piecewise Linear Rep-
resentation, PLR), and analyze the trends by observing the angles that the
segments form. For instance in Figure 2(a), we use 2 segments to represent the
original 10 data points. Notice that the number of points for a segment can be
variable (adaptive approximations). We used the algorithm of [4] for the con-
struction of piecewise linear histograms.
Consider we have a time series of n data points X = x1 , x2 , ..., xn , and
we want to fit it in m << n segments. The algorithm maintains a set B of
buckets bi = hi , begi , endi , li , ri , hi , where hi is a convex hull of data points,
and (begi , li ), (endi , ri ) are the coordinates of the segment that best fits the
convex hull (the segment that bisects the thinnest bounding rectangle of hi [4]).
The slope of bi can be calculated as slope(bi ) = endrii−l −begi . The algorithm adds
i

elements to B from X, until there are no buckets available, and then it starts
to merge those adjacent buckets bi and bi+1 that combined produce the smallest
increase in total error. Merging is reduced to a convex hull merge of hi and hi+1 .
The algorithm iterates until all elements of X have been placed in a bucket. The
resulting set of segments of each bucket bi is the linear approximation of X.
For instance in Figure 2(b), the convex hull hi encloses 8 data points and its
minimum rectangle is bisected by the thick black segment defined by the points
(begi , li ), (endi , ri ). This is the linear representation for these 8 points. During
the computation of the linear representation, if merging hi with the next hull
hi+1 reduces the approximation error, they will form a new single hull with its
own bisecting segment. Once we apply this PLR algorithm we have the time
series represented as line segments, each with a distinctive slope.

3.2 Slope Distributions

To build the slope distributions, we first compute a linear approximation of the
time series, using the algorithm described in Section 3.1. It is possible to create
linear approximations of different accuracy, depending on the number of seg-
ments per unit of time. For instance for a time series of 30 days, if we use 4
segments per day, their slopes will reflect coarse-grained changes in the data
during each day. Time series of originally different sampling times, can be repre-
sented using the same segment/day rate, in order to be comparable. Obviously,
if the original sampling interval is greater than the number of segments/day, the
representation with that rate is not possible.
Once the linear representation is built, we can compute the slopes and ana-
lyze them. The slope or gradient space, bounded in the [∞, −∞] interval for the
possible angles [ π2 , − π2 ], can be divided in sectors, each represented with a sym-
bol αj from an alphabet A and we can assign each segment to its corresponding
symbol. We propose using the segment representation discussed in the previ-
ous section, to compute slope symbolizations, which characterize a time series as
a sequence S of symbols si from an alphabet A that correspond to a type of slope.
In this way, we characterize a time series by the type of variations present
in the sensor data, regardless of the data values. For example if we divide the
angle space in 4 sectors (labeled a, b, c, d), at intervals of π4 , we can match each
segment slope with one symbol. For instance in Figure 3 we have 4 segments,
whose symbolic representation is adac, by matching each slope with a symbol.

Fig. 3: Slopes symbolization. The angle space in this example is divided in 4 sectors,
each of π4 . According to which division the segments falls in, it is assigned a symbol.

Having this symbolic representation of the slopes, it is possible to compare
them to check if two series have similar slope patterns. One simple way to do so,
is to generate symbol distributions, or histograms that count how many symbols
of each type exist in a time series. So a distribution of a sequence S can be defined
as a set DS of elements dαj = | {si ∈ S, si = alphaj } |, for all symbols in A. For
the previous example, it would be a vector 2, 0, 1, 1, which can be normalized
by the total elapsed time, so that we can compare series encompassing different
time spans. A simple distance measure is the euclidean distance,
qP defined for two
n 2
distributions DS1 , DS2 of length n as: deucl (DS1 , DS2 ) = i (dS1 i − dS2 i )

3.3 Choosing the angle divisions
Although we can arbitrarily choose how to divide the angle space (e.g. 4 sectors
of π4 as in the previous example), the actual angles may be more concentrated in
some intervals than others. For instance time series with highly changing angles
such as wind speed, may have steeper gradients than a more stable series. Taking
into account this fact, we propose to analyze the training data sets to determine
an angle division that better represents the actual distribution of angles in the
training set. Using this distribution information, we can divide the angle space
in divisions that hold the same number of angles of the training data.

4 Deriving Semantic Metadata
After establishing how the data is segmented and symbolized, we can use the
symbol distributions for data analysis tasks to help understanding the semantics
of the data. Given a time series, if it does not contain appropriate metadata, the
potential user of this data can use already analyzed time series and compare the
new one with them. We show how this can be done using our symbolization and
a simple classification scheme, even with a partial subset of a time series.
4.1 Semantic Descriptions
A semantic description of an observation is a collection of statements that in-
cludes the observed property (e.g. humidity, pressure), feature of interest (e.g.
the air at some location), unit of measurement, among others. For instance, using
the vocabulary of the SSN Ontology [2], we describe a wind speed observation in
Listing 1. The observation, identified as swissex:WindSpeedObservation1, has
been observed by sensor swissex:SensorWind1 and reported a value of 6.245.
The sensor observed property type cf-property: wind speed (speed of the wind
feature) is defined in a domain specific vocabulary (in this case the Climate and
Forecast vocabulary defined by the W3C SSN-XG group5 ). Additional metadata
about this observation are omitted for brevity.
swissex : W i n d S p e e d O b s e r v a t i o n 1 rdf : type ssn : Observation ;
ssn : f e a t u r e O f I n t e r e s t cf - feature : wind ;
ssn : o b s e r v e d P r o p e r t y cf - property : wind_speed ;
ssn : o b s e r v a t i o n R e s u l t
[ rdf : type ssn : SensorOutput ;
ssn : hasValue [ qudt : numericValue " 6.245 " ^^ xsd : double ]];
ssn : observedBy swissex : SensorWind1 ;

Listing 1: Wind Speed observation in rdf according to the ssn ontology

Concretely, the cf-property:wind speed property indicates that this is an
observation of wind speed, and it has further semantic information in the Climate
& Forecast ontology, as seen in Listing 2. It states that it is a property of the
wind (cf-feature:wind) and is a property of the more general speed quantity
(qu:speed). In order to extract this information, the type of observed property
from an unannotated dataset, we propose the classification scheme in the next
subsection. The goal is basically to identify the ssn:observedProperty for a
time series.
cf - property : wind_speed rdf : type dim : V el oc it y Or Sp e ed ;
rdfs : label " wind speed " ;
ssn : isPropertyOf cf - feature : wind ;
qu : propertyType qu : scalar ;
qu : g e n e r a l Q u a n t i t y K i n d qu : speed .

Listing 2: Wind Speed property according to the Climate and Forecast vocabulary

4.2 Data Classification
Given two sets of time series, a training set already annotated according to the
type of data that is captured, and an unannotated test set, we are interested in
finding the observed property for the second set. Assume we have a collection
D of symbol distributions D1 , ..., Di , ..., Dn as a training set, each of them cor-
responding to a time series tsi , already classified with a type observed property
(e.g. “wind speed”). The classification task consists in finding the best property
for time series tstest in the test set.

We can use a simple k-nearest neighbor scheme, which has been successfully
used for time series classification [5,6]. First, the time series tstest is segmented
5
C&F vocabulary: http://purl.oclc.org/NET/ssnx/cf/cf-property
and symbolized. Then, we generate a symbol distribution Dtest , as described in
Section 3.2, which can be compared iteratively with each of the distributions Di
in D. From the k distributions closer to Dtest , we select the observed property
of the majority.

4.3 Using Partial Data Subsets
This classification technique may use all the complete time series for computing
the symbolization and the slope distribution. However, for types of data with
recurring patterns such as the ones present in environmental and meteorological
data, using a smaller subset of data can be enough to extract the feature that
help detecting the type of observed property. In that case for the construction of
the linear representation of the data, we simply choose a subset of the original
data: X = x1 , x2 , ..., xn , with a different n0 such that n0 < n.

4.4 Querying using the Analysis Results
After executing the classification, we can use the extracted information to com-
plete the sensor metadata, that is then available for querying. In Listing 4 we
show a simple sparql query that asks for sensors that measure air temperature.
SELECT ? sensor
WHERE {
? sensor a ssn : Sensor ;
ssn : observes cf - property : ai r_ t em pe ra t ur e .}

Listing 3: Query all sensors that measure air temperature

The streams produced by sensors can be seen as streaming datasets, whose
metadata can also be queried. The stream, identified by a URI, can be seen
as an unbounded dataset of observations, some of which are actually used to
compute the slope symbolizations and classification described above. The ob-
served properties obtained for the sensor (e.g. cf-property:air temperature)
are therefore the observed properties of the stream observations. We can also
query for more general types of data, for instance, the generic temperature prop-
erty. In Listing 4 we ask for all stream URIs of sensors that measure some type
of temperature.
SELECT ? stream ? o b s e r v e d P r o p e r t y
WHERE {
? sensor a ssn : Sensor ;
ssn : observes ? o b s e r ve d P r o p e r t y .
? stream ssn : isProducedBy ? sensor .
? o b s e r v e d P r o p e r t y qu : g e n e r a l Q u a n t i t y K i n d qu : temperature .}

Listing 4: Query all streams of sensors that measure air temperature

Furthermore, we can expose the similarity measurements computed between
the time series, so that users can also query this information. As an example,
in Listing 5 we use the Similarity Ontology6 (sim) to represent the computed
distance between two series, using our slope representation. Then we can query,
for instance the top 5 series similar to a given time series.
6
The Similarity Ontology: http://purl.org/ontology/similarity/
swissex : slopeSim1_2 a sim : Similarity ;
sim : subject swissex : timeseries1 ;
sim : object swissex : timeseries2 ;
sim : weight 0.32;
sim : method swissex : S l o p e D i s t r i b u t i o n D i s t a n c e .

Listing 5: Slope distribution similarity between two time series

This type of queries allows users not only to use the final results of a classifi-
cation task, but also to query more detailed information including the precision
of the computations. This information can be used to validate this metadata or
provide insight about the analysis process and the relationship of a sensor stream
with other streams. In the case of the early detection of the observed property
of a time series, the user may be interested in knowing, for example, how many
days of data are typically used for classifying those sensors that measure wind
speed 6.
SELECT ? sensor ? dur
WHERE {
? sensor a ssn : Sensor ;
ssn : observes cf - property : wind_speed .
? timeseries ssn : isProducedBy ? sensor .
? timeseries swissex : duration [ qu : numer icalVal ue ? dur ].}

Listing 6: Query the number of data days used for classifying wind speed sensors

5 Experimentation
The main goal of these experiments is to show that the proposed sensor data
representation using slopes can be used to characterize sensor data and extract
sensor metadata corresponding to the types of observed properties. First we show
how the classification behaves with two real life data sets, in terms of precision.
Next, we are interested in experimenting with smaller subsets of data samples,
and observing how the classification behaves with less data, as we know there are
repeating data patterns. Finally, we compare our approach with a classification
using the widely used SAX symbolic representation of the data [5].

To validate the classification approach presented in Section 4.2, we imple-
mented and applied it to two different datasets in the environmental domain:
one from the Swiss Experiment7 and another form AEMET. The data is hetero-
geneous as it comes from different geographical locations, some have different
time spans (e.g. observations collected during 1 year, 3 months, etc), others
have different sampling rates.Also the number of sensors per observation type
varies (e.g. 78 for temperature, only 4 for snow height). Due to the conditions
of the deployments, some of them experimental and others deployed in harsh
environments, this dataset contains a considerable amount of noise in the data.
The AEMET dataset consists of sensor data from 100 weather stations man-
aged by the Spanish meteorological office. The data is heterogeneous, coming
from stations all over Spain, and was originally collected in intervals of 10 min-
utes. It contains, in general, less noise and anomalies than the Swiss Experiment
dataset, as it comes from stations daily used for meteorological forecasts.
7
The dataset is available at: http://lsirpeople.epfl.ch/qvhnguye/benchmark/
5.1 Classification in Swiss Experiment and AEMET
The goal of our first experiment consists in evaluating the effectiveness of the
classification in terms of precision and recall. The classifier is expected to assign
the correct label (the type of observed property, e.g. “humidity”) to time series
from a test set. The classifier uses a training set of time series and the evaluation
criteria is computed in terms on the number of true positives (tp), false positives
tp tp
(f p) and false negatives (f n): precision (p = tp+f p ), and recall (r = tp+tn ).

Swiss Experiment The heterogeneity of the Swiss Experiment dataset required
applying different parameters for the linear approximation step. Some time series
had very short sampling time intervals (e.g. every 2 seconds for pressure, for at
most two days), while others had very long ones (e.g. every half-an-hour for
several months). Hence, the approximations were very different in these cases
(hundreds of segments per day for short intervals, and only a few per day for
long ones).We applied a 5-fold cross validation scheme to divide our dataset in
training and test set, and then apply the nearest neighbor algorithm. We present
the confusion matrix in Table 4, for k = 5.

Fig. 4: Swiss Experiment confusion matrix, k=5. Column header abbrevia-
tions: ra:radiation, mo:moisture, te:temperature, wd:wind direction, ws:wind speed,
hu:humidity, ly:lysimeter, pr:pressure, co:CO2 , sh: snow height, vo:voltage

We can observe that the effectiveness of the classification varies among the
different types of data. The nearest neighbor scheme is also biased as the dataset
is highly unbalanced. Since we have comparatively much more samples of tem-
perature or wind speed, than for pressure or snow height, these last are less
likely find nearest neighbors of the same class. For instance for lysimeter and
snow height, almost no series are correctly identified, as we have a very small
number of series. Nevertheless, in the cases of pressure or CO2 the precision is
good regardless of the low number of series. This is a special case, since these
series have very different slope distributions, and also, have very short sampling
interval. Since their resolution is much smaller (e.g. every 2 seconds) than most
of the other series in the dataset, their comparison throws very large distances
that are quickly discarded.
In cases where the total number of time series was very small (e.g. only 4 for
snow height), the approach is clearly not effective. It requires a larger training
set to have an acceptable precision. Also, when the series are very irregular
(sometimes due to noise and false non-curated data in the original dataset),
they logically fail to be correctly classified.
AEMET For the AEMET dataset, we followed the same approach as with the
Swiss-Experiment. However, for the AEMET data, we had a larger number of
time series for every type of data, thus avoiding the problem of lack of training
data encountered in the previous tests. Moreover, the dataset sampling interval
is the same, making it easier to compare their slope distributions. We applied the
classification scheme with a 10-fold cross validation for this dataset. We provide
the confusion matrix for k = 5 in Table 5.

Fig. 5: AEMET confusion matrix, k=5. Column header abbreviations: st:soil temper-
ature, ba:battery, te:air temperature, wd:wind direction, ws:wind speed, hu:humidity,
wsx: wind speed (max), pr:pressure, wdx: wind direction (max), pre:precipitation.

We can notice that in this case the approach achieves better precision, as ex-
pected, since we avoided the problems of sampling times and unbalanced types
(the number of series per each type is similar or the same). However, it can be
observed that there are important false positives at some specific spots. For in-
stance the number of soil temperature series falsely identified as air temperature
is very high. This is in fact an expected result, since both are specializations
of the more general type temperature. Hence, both share patterns in the time
series, that are reflected in the slope distributions that are compared during the
classification process. The same situation can be seen between wind speed and
wind speed (max), and for wind direction and wind direction (max).

It is also interesting to see that if we consider the “unification” of simi-
lar types of data (e.g. wind speed and maximum wind speed ), the precision is
much higher (Figure 6). This suggests that the slope distributions are useful
for identifying similar data, because they have very similar slope distributions.
This is an expected behavior, for instance for wind speed and wind speed (max),
which are measurements of the same type of data. In order to discern between

Fig. 6: Precision in AEMET, not differencing the specific types wind speed (max) and
wind direction (max).
small differences like these, other characteristics of the data have to be taken
into account. In these cases where two types of observations are similar, we can
use a higher level definition of observed property. For instance, in the Climate
and Forecast vocabulary, the specific properties cf-property:air temperature
and cf-property:soil temperature both have qu:temperature as its general
quantity kind.

5.2 Classification with Partial Information
In this experiment we aim at showing how the classification precision varies
when using smaller subsets of the test data. As we discussed in Section 4.3, for
our environmental and meteorological datasets, recurrent slope patterns in the
data can be representative enough to compute the slope distribution, and make
it possible to classify the data. We have tested the classification reducing the
number of days-of-data used for computation. In Figure 7(a) and Figure 7(b) we
plot the precision for the AEMET and Swiss Experiment dataset series, for dif-
ferent subsets of the data (expressed in terms of the number of days of measured
data). In total we have around 200 days of observations, but we can see that
for some types of data we require much less and obtain similar precision in the
classification. This is the case especially with series that include very repetitive
patterns on a daily basis, but not for others that have a more unpredictable
behavior such as wind speed. In this case we see that it needs more days-of-data
than other types to increase the precision.

(a) AEMET (b) Swiss Experiment
Fig. 7: Classification precision, for different partial datasets, in terms of the days of
data used.

5.3 Comparison with SAX Classification
The goal of this experiment is to compare our approach with a classification
based on the widely used SAX representation of time series [5]. The compari-
son is based on the precision using both approaches. By classifying with SAX
we can verify how well our method behaves in comparison to a well established
technique. The SAX approach also produces a symbolization of the time series,
although the angles and slopes are not taken into account, as it uses a PAA ap-
proximation. We applied the same classification method used for our slope-based
representation. We show the classification precision for the Swiss Experiment and
AEMET datasets in Figure 8(a) and Figure 8(b) respectively.

(a) Swiss Experiment (b) AEMET
Fig. 8: Classification precision with SAX and the Slope representation.

As it can be seen, the classification throws similar results for both methods,
with small differences in AEMET, and slightly better for the slope-based ap-
proach in the Swiss experiment dataset. Using the slopes distributions shows to
be helpful at differencing time series with similar values but very different angles.
In the case of AEMET, the measured values are already enough to discern be-
tween two different types of observation, and hence the results are not improved
by the slope distribution. While the SAX representation has been exploited in
other ways, for example by considering substrings of a fixed size, instead of only
one symbol, this experiment shows that our approach is also able to extract
features that help characterizing a type of time series, and enabling its seman-
tic identification. A classification technique throws different results depending
on the type of data. Further amendments could be plugged to the classifica-
tion scheme, but they risk to be too specific to the characteristics of certain
datatypes, and such methods are outside of the scope of this work.

6 Related Work
Previous works on time series classification and mining, have studied different
approaches for summarizing and exploiting sensor raw data, and have been com-
plemented with semantic representations for sensor data management.

Data Approximations High level representations reduce the dimensionality
of time series data, in order to reduce the complexity of indexing and comparison
algorithms, using different techniques. These include piecewise constant and lin-
ear approximations (e.g. PAA[7], APCA[8], PLR[9]) that use constant and linear
segments respectively, to represent the original time series. Generally simple to
compute, either in batch mode and online using sliding window algorithms, these
methods offer accurate approximations of the original data. These representa-
tions have been widely used for tasks including similarity search, fuzzy queries,
dynamic time warping, clustering and classification [9].
While these approximations reduce dimensionality, some approaches intro-
duce a further step that consists in the symbolization of the time series. These
techniques, such as SAX [5], have shown to be space and time efficient for in-
dexing, classification and clustering, and also for additional tasks such as motif
discovery and visualization [10]. These symbolizations can be used to compute
distance measures that help in classification and clustering tasks [5]. Other works
have considered also the slopes of linear approximations such as the STS dis-
tance [11] for clustering time series.
SAX symbolization has also been used for sensor events detection [12] and
for creating high-level perception abstractions from the raw sensor data, by
matching SAX patterns with low-level thematic abstractions [13].

Time Series Classification Particularly, for the task of classification, differ-
ent techniques such as decision trees, neural networks and bayesian classifiers
have been used [6]. Classification approaches usually fall into the following three
categories: distance-based, feature-based and model-based[6]. Simple distance
measures such as euclidean, are very limited because they only consider one-
to-one matches in the time axis. Distance measures with more elastic matching
for the time axis, such as Dynamic Time Warping (DTW), have been proved
effective for similarity matching [14]. These have been coupled with k-nearest
neighbor (k-NN) classifiers, proving an effective combination for a number of
time series classification problems [15,16]. These techniques have space and time
computation limitations in some scenarios, and offer little explanation on why
a series belongs to a particular class [17]. Feature-based approaches try to find
properties that are representative of a type of series, in order to classify them.
Most of these approaches use a high level representation e.g. symbolization or
discretization methods, before extracting the features[6] while others work ex-
tracting representative subsequences (e.g. shapelets [17]).

Semantic Sensor Representations The task of modeling sensor data and
metadata with ontologies has been addressed by the semantic web research com-
munity in recent years. Early ontology proposals for describing wireless sensors
have been reviewed in [18]. However, the focus of most of these approaches was
on sensor meta information, while the description of observations was generally
overlooked. Besides some of these approaches lack ontology design best practices
of reuse and alignment with standards an reference ontologies. Others, including
the OntoSensor ontology [19], use the concepts defined in the OGC SensorML8
standard as a basis. More recent proposals like [20] and [21], also consider the
OGC Observations and Measurements (O&M) standard9 to represent observa-
tions captured by sensor networks.
Recently, through the W3C SSN-XG group, the semantic web and sensor
network communities have made an effort to provide a domain independent
ontology, generic enough to adapt to different use-cases, and compatible with
the OGC standards at the sensor and observation levels. The result, the ssn
ontology [2], is based on the stimulus-sensor-observation design pattern [22] and
the OGC standards.
8
SensorML. http://www.opengeospatial.org/standards/sensorml
9
OGC O&M: http://www.opengeospatial.org/standards/om
7 Conclusions and Future Work
We have described an approach for identifying the type of data from sensor data
sources, using a symbolic representation of the time series slopes. We have shown
how this representation can be used for enriching semantic sensor metadata.
We have shown specific use cases of time series data classification, providing
similarity measures, and metadata aggregation that can be queried in terms of
high-level standard ontologies. Finally, we evaluated our approach with real-life
datasets of the Swiss-Experiment project and AEMET.
We have shown through experimentation that this representation can be
useful for balanced datasets, as the classification gets biased when there are
small numbers of samples in the training set, for a particular type of data.
Moreover, our results show that this representation can help grouping data of the
same type, despite geographical locations, since it is based on the distribution of
slopes of a linear approximation. Therefore, it can identify similarities of related
types of data: e.g. air temperature and soil temperature. We have compared our
characterization of sensor data with a competitive approach, and showed that
for the chosen environmental datasets it effectively enables the extraction of
semantic metadata.
The proposed approach, however, was evaluated within the same dataset, and
in the future we will study its applicability in an inter-dataset classification. This
framework could be used in the future for other tasks such as clustering, or for
identifying simple patterns in streams of sensor data. Moreover, complex sym-
bolizations consisting of sequences of slopes could be considered, which would
represent more complete patterns that can be exploited. Also, we can consider
building a more complex representation that includes not only the slopes infor-
mation but also the value ranges, and even tags and labels provided the data
publishers. This may enable a more complete and accurate extraction of meta-
data that enriches the growing Semantic Sensor Web. As a final future path, we
may consider applying online execution of these techniques for real-time analysis.

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