This document discusses ontology-based access to sensor data streams. It motivates the need to provide universal web-based access to sensor data as streams. Existing approaches are discussed for querying static sensor data and streaming data using different stream processing engines. The author proposes using ontology models to continuously query real-time sensor data streams. Several hypotheses are presented regarding representing streaming data as ontology instances, extending SPARQL for streaming queries, rewriting ontology-based streaming queries to engine-specific queries using mappings, and evaluating the overhead of query rewriting. The document concludes that the hypotheses were confirmed by enabling ontology-based access to streaming sources and characterizing sensor metadata.
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Phd
1. Ontology-based Access to
Sensor Data Streams
Jean-Paul Calbimonte
Supervisor: Oscar Corcho
Ontology Engineering Group
Facultad de Informática, Universidad Politécnica de Madrid
jp.calbimonte@upm.es
PhD Thesis Defense
18.4.2013
5. Sensor Networks and the Web
5
Sensor Networks
users
applications
data streams
Volume
Velocity
Variety WEB
Universal Web-based access to Sensor data
6. Querying the semantic sensor Web
6
e.g. publish sensor data as RDF/Linked Data?
URIs as names of things
HTTP URIs
useful information when URI
is dereferenced
Link to other URIs
users
applications
WEB
Use ontology models to continuously query real-
time data streams originated from sensors?
1
static vs. streams
one-off vs. continuous
7. Research questions & hypotheses
7
Ontology models to query real-time sensor data streams?
Access heterogeneous SPEs using ontologies as an
overarching data model?
SPARQL streaming extensions for querying data from SPEs
(stream processing engines)?
1
H1: Sensor streaming data instances of an ontology model
H2: SPARQL extensions streaming operators & continuous processing
H3: Ontology-based streaming queries rewritten to relational-based
queries using mappings
H4: Ontology-based streaming queries abstract expressions
concrete executable SPE queries
H5: Query rewriting Pull & Push delivery acceptable overhead
10. Characterizing semantic sensor metadata
10
users
applications
WEB
Characterizing sensor data, deriving semantic
metadata from the sensor observations
2
different publishers
different metadata
publish streams
Search/query relevant
data sources?
GSN
11. Research questions & hypotheses
11
Data representation suitable for extracting data features
that characterize a set of sensor streams?
Classification and mining techniques to characterize
sensor data streams?
2
H6: Sensor data series find characteristic patterns
make it recognizable among other types
H7: Slope representations semantic properties such as the type of data
learned with classification techniques
acceptable precision
12. Contributions
12
SPARQL extensions & formalization
rewriting to algebra expressions
using declarative mappings
results data translation
query evaluation pluggable to ≠ SPEs
query rewriting using R2RML mappings
data representation as slope distributions
characterize types of sensor data
classifying sensor time series
extract metadata features
derive semantic properties & R2RML
SPARQLStream
Sensor metadata characterization
QueryingMetadata
2
1
13. Limitations
13
L1: Rewriting medium sampling throughput, e.g. Env. monitoring
L2: Query expressivity is limited to underlying SPEs’.
L3: Adapters implemented for custom sources.
L4: Querying only simple entailment
L5: Arbitrarily noisy sensor series no accurate characterization.
L6: Classification number of sensor time series in training set
L7: Data characterization is not computed in real-time, but offline
14. 14
Outline
Motivation
Background
Conclusions
Semantic stream query processing
Sensor metadata characterization
Ontology-based Access to Sensor Data Streams
Hypotheses & contributions
Challenges
Data Streams Continuous queries Window
SPEs Ontology-based data access
17. Stream Processing Engines (SPE)
17
Data Stream Management Systems (DSMS)
Complex Event Processors (CEP)
Sensor Data Middleware
CQL/Strea
m
Borealis
TelegraphCQ
StreamMill
Cayuga
GEM CEDR
NiagaraCQ
Rapide
CosmHourglass
SStreamWare GSN
IBM InfoSphere
Sybase CEP
Microsoft StreamInsight
Oracle CEP
Esper
StreamBase
Diverse query languages
Different query capabilities
Different query models
18. Extracting data from relational databases
18
WEB
Ontology-based
data access
one-off SPARQL
queries
data as RDF
relational database
RDB to RDF
mappings
static data
D2R
Morph
ODEMapster Triplify
UltraWrap Mastro
R2RML
W3C SSN Ontology
19. Summary
19
Existing SPEs available and producing data streams
Ontology-based access only for stored data
SPARQL query language not suitable for streams
SPEs are highly heterogeneous in models and queries
20. 20
Outline
Motivation
Background
Conclusions
Semantic stream query processing
Sensor metadata characterization
Ontology-based Access to Sensor Data Streams
Hypotheses & contributions
SPARQLStream
Challenges
Query rewritingRDF Stream
Mappings using R2RML Execution over SPEs
21. RDF Streams
21
s,p,o
<aemet:observation1, qudt:hasNumericValue, “15.5”>
<aemet:observation1, ssn:observedBy, aemet:Sensor3>
For streams?
( s,p,o ,τ)
(<aemet:observation1, qudt:hasNumericValue, “15.5”>,34532)
timestamped triples
• Gutierrez et al. (2007) Introducing time into RDF. IEEE TKDE
• Rodríguez et al. (2009) Semantic management of streaming data. SSN
22. SPARQLStream extensions
22
SELECT (MAX(?temperature) AS ?maxtemp) ?sensor
WHERE {
?obs ssn:observedBy ?sensor.
?obs ssn:observationResult ?res.
?res aemet:hasAirTemperatureValue ?val.
?val qu:numericValue ?temperature.
}
GROUP BY ?sensor
SELECT (MAX(?temp) AS ?maxtemp) ?sensor
FROM NAMED STREAM <http://aemet.linkeddata.es/observations.srdf> [NOW-1 HOURS]
WHERE {
?obs ssn:observedBy ?sensor.
?obs ssn:observationResult ?res.
?res aemet:hasAirTemperatureValue ?val.
?val qu:numericValue ?temp.
}
GROUP BY ?sensor
SPARQLStream
Named streams
Time windows
Other approaches: Streaming SPARQL (2008), C-SPARQL (2009), CQELS
(2011), EP-SPARQL (2011), INSTANS (2012)
23. Streaming SPARQL execution approaches
23
Extend RDF for streaming data
Extend SPARQL for streaming RDF
Use a SPE internally for evaluation
Query rewriting to SPEs
RDF Streaming engine from scratch
Logic-programming based query evaluation
~Similarities
Divergence
streams
DSMSs
CEPs
Middleware
SPARQLStream
24. Mapping SPE schemas and ontologies
24
wan7
timed: datetime PK
sp_wind: float
timed sp_wind
1 3.4
2 5.6
3 11.2
4 1.2
5 3.1
.. …
Queries
SELECT sp_wind
FROM wan7 [NOW -5 HOUR]
WHERE sp_wind >10
SPE
SPE data schemas
ssn:Observation
Ontology models
SPARQLStream Queries
Stream-to-ontology
mappings
SELECT ?wspeed
FROM STREAM <SensorReadings.srdf> [NOW–5 HOUR]
WHERE {
?obs a ssn:ObservationValue;
qudt:numericalValue ?wspeed;
FILTER (?wspeed>10) }
35. Experiments AEMET
Confusion matrix AEMET
H6: Sensor data series
find characteristic patterns
make it recognizable among other types
35
Classification according to type
FPs on subclasses of the same property
36. Evaluation vs SAX
36
H7: Slope representations
type of data: semantic property
learned through classification
39. Conclusions
H1: Sensor streaming data instances of an ontology model
H2: SPARQL extensions streaming operators & continuous processing
H3: Ontology-based streaming queries rewritten to relational-based
queries using mappings
Mapping sensor data to ontology instances, e.g. SSN Ontology
SPARQLStream data model, extensions syntax, semantics
SPARQLStream semantics of query rewriting to relational steaming
algebra
usage of declarative mappings (W3C R2RML)
Calbimonte, Corcho & Gray. Enabling ontology-based access to streaming data sources. ISWC 2010
Gray, García-Castro, Kyzirakos, Karpathiotakis, Calbimonte, Page et al. A semantically enabled service
architecture for mashups over streaming and stored data. ESWC 2011
Gray, Sadler, Kit, Kyzirakos, Karpathiotakis, Calbimonte, Page, García-Castro, et al. A semantic sensor
web for environmental decision support applications. Sensors, MDPI, 2011
Calbimonte, Corcho & Gray. Ontology-based Access to Streaming Data. In Posters ESWC 2010
39
40. Conclusions
40
H4: Ontology-based streaming queries abstract expressions
concrete executable SPE queries
Instantiate, execute ≠ SPEs: SNEE (DSMS), Esper (CEP), GSN & Cosm (Middlwr)
Available implementation
application in different domains
H5: Query rewriting Pull & Push delivery evaluation overhead
SPARQLStream evaluation overhead wrt. native execution
Push & pull delivery evaluation
Calbimonte, Jeung, Corcho & Aberer. Enabling Query Technologies for the Semantic Sensor Web. IJSWIS 2012.
Calbimonte & Corcho. Evaluating SPARQL Queries over RDF Streams. Linked Data Management: Principles
and Techniques, CRC Press, 2013 (under review)
Zhang, Duc, Corcho & Calbimonte. SRBench: A Streaming RDF/SPARQL Benchmark. ISWC 2012.
Ruckhaus, Calbimonte, García-Castro & Corcho. Short Paper: From Streaming Data to Linked Data–A Case
Study with Bike Sharing Systems. ISWC SSN 2012
41. Conclusions
41
H6: Sensor data series analyze in order to find characteristic patterns
make it recognizable among other types
H7: Slope representations semantic properties such as the type of data
learned with classification techniques
acceptable precision
41
Raw observations analysis slope distribution representation
compared with SoA representations i.e. SAX
Evaluation of classification task real world datasets AEMET, SwissEx
in presence of noisy data
deriving semantic metadata
Calbimonte, Yan, Jeung, Corcho & Aberer. Deriving Semantic Sensor Metadata from Raw Measurements.
ISWC SSN 2012
Calbimonte, Jeung, Corcho, & Aberer. Semantic Sensor Data Search in a Large-Scale Federated Sensor
Network. ISWC SSN 2011
42. Future directions
42
WEB
SPARQLStream queries
Publishing Linked Stream Data
Currently static
SPARQL streaming
standards
Dereferencing streaming
data
Query Federation
Distributed sensor data
Static and streaming sources
Stream Reasoning
query rewriting, expanding queries
Expresiveness
Integrate with the Web of Data
Inferencing
43. Future directions
WEB
Sensor pattern classification
Combine with query
processing
Live data classification
Statistical & quality analysis Integrate statistic analyisis
Mappings to statistical models
Data quality filtering
Parallel Massive Stream Processing Online stream analysis
Scalable stream processing
S4, Storm, Streamcloud
Heterogeneity
43
44. Ontology-based Access to
Sensor Data Streams
Jean-Paul Calbimonte
Supervisor: Oscar Corcho
Ontology Engineering Group
Facultad de Informática, Universidad Politécnica de Madrid
18.4.2013
jp.calbimonte@upm.es
PhD Thesis Defense
52. RDF Streams and SPARQLStream
52
RDF Stream
Time window
Window-Stream
53. Mappings
53
Subject, predicate, object
Given a triple pattern t p = (sp, pp,op), the semantics of its evaluation over a
lational streams referenced by a set of mappings M , is given by eval (t p,M), wh
n algebra expression defined as:
eval (t p,M) = ρf s→sp,f p→pp,f o→opπf s,f p,f o(s)
where ρ is the relational rename operation and π is the relational projection
on. s is the stream referenced by the mapping µ = f i ndM appi ng(t p,M) and f s
,
e the functions of µ that generate the projection expressions for producing respec
e subject, predicate and object, for every tuple of s.
For the previous example, the evaluation of t p1 is given by:
eval (t p1,M) = ρf s→sp,f p→pp,f o→opπf s
µ1
(s1.ts),f
p
µ1
(),f o
µ1
()(s1)
The resulting algebra expression projects the s1.ts attribute, applying the f s
on to create the subject. The functions f
p
µ1
and f o
µ1
in this case are constants,
edicate and object are the same for all tuples of s1. For the evaluation of more co
Evaluate query
54. Rewrite to algebra
54
Then, the evaluation of gp can be represented as the following algebra expression:
eval (t p,M) = ωts,te,δ πf s
µ1
(s1) ✶ πf s
µ2
,f o
µ2
(s1) ✶ πf s
µ4
,f o
µ4
(s1) ✶πf s
µ5
,f o
µ5
(s1)
This expression can be represented as a tree (Figure 4.1), where the leaf nodes are the
streams and the other nodes are the relational streaming operators.
Figure 4.1: Tree representation of the evaluation of a SPARQL Stream query rewritten as an alge-
bra expression.
eval (t p, M ) = ωts,te,δ πf s
µ1
(s1) ✶ πf s
µ2
,f o
µ2
(s1) ✶ πf s
µ4
,f o
µ4
(s1) ✶πf s
µ5
,f o
µ5
(s1)
This expression can be represented as a tree (Figure 4.1), where the leaf nodes are th
streams and the other nodes are the relational streaming operators.
Figure 4.1: Tree representation of the evaluation of a SPARQL Stream query rewritten as an alg
bra expression.
59. Query Features
59
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17
1.Graph pattern
matching
A A,F,O A A,F A A,F,U A A A A A,F A,F,U A,F A,F,U A,F A,F A,F
2. Solution modifier P,D P,D P P P P P,D P P P,D P,D P P P,D P P P
3. Query form S S A S C S S S S S S S S S S S S
4. SPARQL 1.1 F,P A A,E,M
,F
A,S N A,E,M A,E,M A,S,M
,F
A,S,E,
M,F,P
A,E,M
,F,P
F,P A,E,M
,P
P P
5. Reasoning C R C A C
6. Streaming T T T T T T T,D T T T T T T T T
7. Dataset O O O O O O O O,S O,S O,S O,S O,S,G O,S,G O,S,G O,S,D O,S,G
,D
S
1. And, Filter, Union, Optional
2. Projection, Distinct
3. Select, Construct, Ask
4. Aggregate, Subquery, Negation, Expr in SELECT, assignMent,
Functions&operators, PropertyPath
5. subClassOf, subpRopertyOf, owl:sameAs
6. Time-based window, Istream, Dstream,Rstream
7. LinkedObservationData, LinkedSensorMetadata, GeoNames, Dbpedia