This document discusses using provenance information to help assess data quality. It proposes representing sensor observations and their provenance as linked data and using this information to evaluate quality metrics like accuracy, timeliness, and relevance. The work done so far involves representing observations and quality requirements as linked data and generating initial quality scores. Future work will focus on implementing quality rules that examine provenance information and enabling quality scores to be reused.

1. A Role for Provenance in Quality
Assessment
Chris Baillie, Pete Edwards, and Edoardo Pignotti
c.baillie@abdn.ac.uk

2. Overview
 Motivation
 Evaluating Data Quality
 A Role for Provenance
 Future work
c.baillie@abdn.ac.uk

3. Motivation
 “we don’t know whether the information we find [on the Web]
is accurate or not. So we have to teach people how to assess
what they’ve found’’
Vint Cerf, 2010
 Web of Documents has become the Web of documents,
services, data, and people.
 Anyone can publish anything so we need a way to evaluate
quality.
 We are investigating these issues within the Internet of Things
 Sensors now at the centre of many applications
c.baillie@abdn.ac.uk

4. Example Scenario
c.baillie@abdn.ac.uk

5. Evaluating Data Quality
Quality Scores
-Quality is a multi-
Entity (and context) dimensional construct
To evaluate quality, we - Accuracy
must examine the - Timeliness
context around data - Relevance
F(E, R) = Q
WIQA Framework
examines data content, Data Requirements
context, and external -Furber and Hepp (2011)
ratings use rules to identify
(Bizer et al. 2009) quality problems
c.baillie@abdn.ac.uk

6. Representing Sensor Observations
 Linked Data: “recommended best practice for exposing,
sharing, and connecting pieces of data using URIs and RDF”
c.baillie@abdn.ac.uk

7. Performing Quality Assessment
CONSTRUCT {
_:b0 a QualityScore .
_:b0 score ?qs .
( E distanceFromRoute X )
_:b0 dqm:ruleViolation _:b1 .
Rrelevance = 1-
100 _:b1 a DataRequirementViolation .
_:b1 dqm:affectedInstance ?instance .
} WHERE {
?instance a Observation .
?instance distanceFromRoute ?distance .
LET (?qs := (1 - (?distance / 100))) .
}
c.baillie@abdn.ac.uk

8. Quality Assessment Results
c.baillie@abdn.ac.uk

9. Observation Provenance
 Provenance is a critical part of observation context
 Describes the entities, agents, and activities involved in
data creation:
 How was the observation value measured?
 Who controlled the sensing process?
 How has the observation been transformed since it was
created?
 W3C Prov-O model provides linked data representation
of provenance

10. Observation Provenance
Entity
"Observation 2"
wasGeneratedBy
Activity
"Map matching"
used
Agent
"Chris"
Entity
"Observation 1"
wasAssociatedWith
wasGeneratedBy
Activity
"Sensing Process"
used
Entity
"iPhoneSensor"

11. Quality Score Provenance

12. Work To Date
 Developed Quality Assessment Framework that enables:
 Linked data representation of sensor observations
 Definition of quality requirements using SPARQL rules
 Generation of quality scores via reasoning
Future Work
 Implementation of quality rules that examine provenance
 Investigate quality score re-use

13. Any questions?
Come and see the IRP demo (D9) to see quality
assessment in action.

14. Implementation
Quality Rules
Observation Reasoner Relevance
Triple (SPIN) Rule
Store
Timeliness
Rule
Apache Tomcat Accuracy
Rule
Observation Quality
Service Service Availability
Rule