session 5.6 towards a semantic outlier detection framework in wireless sensor networks

SEMANTiCS 2017, Amsterdam, The Netherlands, September 11-14, 2017

Towards a Semantic Outlier DetectionFramework in Wireless Sensor Networks

Iker Esnaola-Gonzalez, Jesús Bermúdez, Izaskun Fernandez, Santiago Fernandez, Aitor Arnaiz

© IK4-TEKNIKER 2017

Agenda

• Introduction

• Role of Semantics in Outlier

Detection

• The SemOD Framework

• Temperature Sensor use case

• Results

• Conclusions


Introduction

• Current datasets suffer from:

• Noisy data

• Missing data

• Outliers

• …

• Consequences:

• Complicate knowledge extraction

• Low quality mining results

• Inaccurate conclusions

• Solution: Preprocessing techniques


Introduction

• Outlier Detection:

• Spotting data that stand out

among other and do not have

the expected behaviour.

• What to do with them?

• Isolate and act on them (Fraud

Detection)

• Filter them out (Data Analytics)

• …


Role of Semantics in Outlier Detection

• Underlying semantics of data can

be exploited to detect outliers

• Is a 44ºC measurement an outlier?

It depends on the context:

• Location

• Time

• Season

• …


The SemOD Framework

• The Semantic Outlier Detection

(SemOD) Framework for WSNs

• Assists the data scientist in:

• Outlier Detection

• Outlier Classification


The SemOD Framework

• 3 main components

• The EEPSA Ontology

• The SemOD Method

• The SemOD Query


The SemOD Framework

• Use case: 3 Temperature sensors

located in IK4-Tekniker building

(Eibar, Spain)

• EEPSA Ontology for Semantic

Annotation


The SemOD Framework

• Infers sensors vulnerabilities

• For each vulnerability, a

SemOD Method is proposed

• SemOD Method: guide to

identify outliers caused by that

vulnerability


• 1st step: Sensor’s sun exposure

• Determines periods when sensor

may be exposed to sun

• The EEPSA Ontology infers them

• Depends on sensor location and

orientation

Temperature Sensor use case



• 2nd step: Sunshine constraint

• Determines if sensor receives

sunshine when enough sun

• Derived from nearby sensor’s

solar irradiance and illuminance



• 3rd step: SemOD Query generation

• Fills SemOD Query pattern with

information of previous steps

• Classifies measurements as

outliers caused due to sensor’s

sun exposure

CONSTRUCT {?obs1 rdf:type

eepsa:OutlierCausedBySolarRadiation }

FROM <myGRAPH >

WHERE {

?sensor1 sosa:observedProperty

m3-lite:Temperature .

?sensor2 sosa:observedProperty

m3-lite:Illuminance ;

eepsa:hasUnitOfMeasure m3-lite:Lux .

?obs1 sosa:isObservedBy ?sensor1 ;

eepsa:obsTime ?time ;

eepsa:obsDate ?date .

?obs2 sosa:isObservedBy ?sensor2 ;

eepsa:obsTime ?time ;

eepsa:obsDate ?date ;

sosa:hasSimpleResult ?illu

…


Results

SENSOR T17

*Classic method: Rapidminer’s Detect Outlier (Densities) operator


Conclusions

• SemOD Framework: Assistance in

Outlier Detection and Classification

• Exploit underlying semantics of

data, not just values.

• Not exclusive and complementary

to other outlier detection methods

• Applicable to multiple domains


Thank you for your attention

Iker [email protected]

session 5.6 towards a semantic outlier detection framework in wireless sensor networks

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