Semantic Discovery and Integration of Urban Data Streams

Semantic Discovery & Integration of
Urban Data Streams
Feng Gao, Ali Intizar and Alessandra Mileo

Smart City Applications- Overview
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* http://www.nec.com

Smart City Applications - IoE
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http://www.thepowerofplace.biz/2013/06/23/a-road-map-for-smart-cities-and-bim/

Smart City Applications - Infrastructure
• Physical Sensors
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• Mobile/Wearable Sensors
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• Virtual Sensors (Social Media)
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Smart City Applications - City Pulse
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Smart City Applications - Challenges
Federation of Heterogeneous Data Streams
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Real-time Information Extraction
and Event Detection
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Reliable Information Processing
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Federation of Heterogeneous Data Streams
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• Virtualisation
• Federation of heterogeneous data streams
• Processing user’s queries in terms of requirements rather
than hard-bind queries
• Optimal data source selection while taking users
constraints and preferences into account
• Automated composition of primitive data services/streams
into complex events
• Automated generations of queries from the complex
event’s composition plan
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ACEIS - Features
• Automated Complex Event Implementation System
• Enables users to provide requirements rather than hard
bind streams
• Automatically discovers the relevant data streams
• Selects optimal data stream after evaluating user’s
constraints and preferences
• On demand data federation using complex event patterns
• Transformation of complex event into stream queries
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ACEIS - Architecture
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Semantic Annotation
Application
Interface
ACEIS Core
Resource
Management
Knowledge Base
QoI/QoS
Stream
Description
Data Mgmt,
Indexing,
Caching
User Input
Event Request
Data
Federation
Resource Discovery
Event Service Composer
Composition Plan
Subscription Manager
Query Transformer
Query Engine
Query
Results
Adaptation
Manager
Constraint
Validation
Constraint
Violation
Data Store
IoT Data
Stream
Social Data
Stream

Complex Event Service Ontology – Overview (1/2)
The Complex Event Service Ontology (CES ontology) is an extension of
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OWL-S ontology.
CES ontology is used together with SSN (Semantic Sensor Network)
ontology, SSN is used to describe the sensor aspects
An Event Service is described with a Grounding and an Event Profile.
1. Groundings specify how to access and interact with event services.
2. Event Profiles describe the events provided by the services with Patterns and
Non-Functional Properties (NFP).
An Event Request is specified as an incomplete Event Service
description, without concrete service bindings.

Complex Event Service Ontology – Overview (2/2)
owls:ServiceProfile
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EventService
EventProfile
owls:Grounding
PrimitiveEvent
Service
rdf:_x (contains)
Pattern
owls:Service
owls:supports
ComplexEven
tService
EventRequest
owls:presents
hasPattern
rdf:_x (contains)
hasConstraint
Namespaces:
default: <http://www.insight-centre.org/ces#>
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
owls: <http://www.daml.org/services/owl-s/1.2/Service.owl#>
owls-sp: <http://www.daml.org/services/owl-s/1.2/ServiceParameter.owl#>
Legend:
Class
Object property
subClassOf
owls:presents
owls-sp:ServiceParameter
NFP
Constraint
Preference
hasPreference
QosWeight
Preference
hasWeight
xsd:double
rdf:_x (contains)
owls-sp:serviceParameter
Data property
hasNFP

Complex Event Service Ontology – Event Pattern
EventService
rdf:_x (contains)
rdf:_x (contains)
rdf:Seq rdf:Bag
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ComplexEvent
Service
owls:presents
EventProfile
hasPattern
Pattern
Namespaces:
default: <http://www.insight-centre.org/ces#>
rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
owls: <http://www.daml.org/services/owl-s/1.2/Service.owl#>
Legend:
Class (unimplemented)
Object property
subClassOf
Sequence
Or
Repetition And
hasFilter
Aggregation Filter SlidingWindow
Class
ValueAssignment
hasWindow
onEvent onPayload hasExpression
EventService EventPayload Expression

Stream Discovery – Sensor Stream Annotation
A sensor service description is annotated as:
PrimitiveEventService in CES ontology, as well as a Sensor device in SSN on-tology.
The CES ontology is mainly used to describe the non-functional aspects
Pd → FoId
of sensor service requests/descriptions, including sensor event types, quality pa-rameters
and sensor service groundings. SSN ontology is used to describe the
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sdesc = (td, g, qd, Pd, FoId, fd)
type grounding QoS Observed
Properties
Feature Of
Iterest
functional aspects, including ObservedProperties and FeatureOfInterest.
Listing 1. Trac sensor service description
:sampleTrafficSensor a ssn:Sensor ,ces:PrimitiveEventService;
owls:presents :sampleProfile ;
owls:supports :sampleGrounding;
ssn:observes [ a ces:AverageSpeed;
ssn:isPropertyFor :Seg_1],
[ a ces:VehicleCount;
[ a ces:EstimatedTime;
ssn:isPropertyFor :Seg_1 ].
:sampleProfile a ces:EventProfile ;
owls:serviceCategory [ a ces: TrafficReportService ;
owls: serviceCategoryName traffic_report ^^ xsd:string ].
Listing 2. Trac sensor service request
:sampleRequest a ssn:Sensor ,ces:EventRequest;
owls:presents :requestProfile ;

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Semantic Annotation
Application
Interface
ACEIS Core
Resource
Management
Knowledge Base
QoI/QoS
Stream
Description
Data Mgmt,
Indexing,
Caching
User Input
Event Request
Data
Federation
Resource Discovery
Composition Plan
Query Transformer
Query Engine
Query
Results
Adaptation
Manager
Constraint
Validation
Constraint
Violation
Data Store
IoT Data
Stream
Social Data
Stream

Stream Discovery – Event Request Annotation
Similarly, a sensor service request is annotated:
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sr = (tr, Pr, FoIr, fr, pref, C)
type
Requested
Properties
Feature of
Interest
Pd → FoId
ssn:observes [ a ces:EstimatedTime;
ssn:isPropertyFor :Seg_1 ];
ces:hasConstraint [ rdf:type ces:NFPConstraint;
ces:onProperty ces:Availability;
ces:hasExpression
[ emvo:greaterThan 0.9^^xsd:double]],
[ rdf:type ces:NFPConstraint;
ces:onProperty ces:Accuracy;
ces:hasExpression
[ emvo:greaterThan 0.9^^xsd:double]].
:requestProfile a ces:EventProfile ;
A sensor service description is denoted as sdesc = (td, g, qd, Pd, FoId, fd),
where t is the sensor event type, g is the service grounding, qd is a QoS vector
describing the QoS values, Pd is the set of ObservedProperties, FoId is the set

Stream Discovery – Event Request Annotation
Similarly, a sensor service request is annotated:
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sr = (tr, Pr, FoIr, fr, pref, C)
type
Requested
Properties
Feature of
Interest
Pd → FoId
no
grounding
NFP
ConstraintPrefer
ences
ssn:observes [ a ces:EstimatedTime;
ssn:isPropertyFor :Seg_1 ];
ces:hasConstraint [ rdf:type ces:NFPConstraint;
ces:onProperty ces:Availability;
ces:hasExpression
[ emvo:greaterThan 0.9^^xsd:double]],
[ rdf:type ces:NFPConstraint;
ces:onProperty ces:Accuracy;
ces:hasExpression
[ emvo:greaterThan 0.9^^xsd:double]].
:requestProfile a ces:EventProfile ;
A sensor service description is denoted as sdesc = (td, g, qd, Pd, FoId, fd),
where t is the sensor event type, g is the service grounding, qd is a QoS vector
describing the QoS values, Pd is the set of ObservedProperties, FoId is the set

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Semantic Annotation
Application
Interface
ACEIS Core
Resource
Management
Knowledge Base
QoI/QoS
Stream
Description
Data Mgmt,
Indexing,
Caching
User Input
Event Request
Data
Federation
Resource Discovery
Composition Plan
Query Transformer
Query Engine
Query
Results
Adaptation
Manager
Constraint
Validation
Constraint
Violation
Data Store
IoT Data
Stream
Social Data
Stream

Stream Discovery – Matching Condition
A sensor service description Sd matches a service
request Sr iff the following three conditions are true:
1. tr subsumes td:
3. ∀p1 ∈ Pr,∃p2 ∈ Pd ⇒ T(p1) subsumes T(p2) ∧ fr(p1) = fd(p2):
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2. qd satifies C:
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Stream Discovery – Matching Condition
A sensor service description Sd matches a service
request Sr iff the following three conditions are true:
1. tr subsumes td:
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Requested service type is same or a super-type of provided service type.
2. qd satifies C:
Quality-of-service properties of the provided sensor service satisfy the
constraints specified in the service request.
3. ∀p1 ∈ Pr,∃p2 ∈ Pd ⇒ T(p1) subsumes T(p2) ∧ fr(p1) = fd(p2):
Provided sensor service observes all requested physical properties from the
requested feature-of-interest (a geographical location or physical entity from
which the observations are made).

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Semantic Annotation
Application
Interface
ACEIS Core
Resource
Management
Knowledge Base
QoI/QoS
Stream
Description
Data Mgmt,
Indexing,
Caching
User Input
Event Request
Data
Federation
Resource Discovery
Composition Plan
Query Transformer
Query Engine
Query
Results
Adaptation
Manager
Constraint
Validation
Constraint
Violation
Data Store
IoT Data
Stream
Social Data
Stream

Stream Integration – Complex Event Service
• A Complex Event Service (CES) integrates different sensor
streams to detect complex events based on event patterns.
• An Event Pattern describes the correlations of integrated
streams.
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matched by the trac sensor service. When the discovery component finds all
service candidates suitable for the request, a Simple-Additive-Weighting algo-rithm
[5] is used to rank the service candidates based on qd, qr and pref.
4.3 Sensors Streams Integration
Sensor stream discovery deals only with primitive event service discovery. To
discover and integrate (composite) sensor streams for complex event service re-quests,
the event patterns specified in the complex event service requests/de-scriptions
need to be considered.
Listing 3. Complex event service request
:SampleEventRequest a ces:EventRequest;
owls:presents :SampleEventProfile.
:SampleEventProfile rdf:type owls:EventProfile;
ces:hasPattern [ rdf:type ces:And , rdf:Bag;
rdf:_1 : locationRequest ;
rdf:_2 : seg1CongestionRequest ;
ces:hasWindow 5^^ xsd:integer ].
In the context of integrated sensor stream discovery and composition, the
0d
definition of sensor stream description is extended to denote composite sensor
stream descriptions Sd = (epd,Qd,G),where epd consists of a set of sensor stream
descriptions sd and/or a set of composite sensor stream descriptions S, and a set
of event operators including Sequence, Repetition, And, Or, Selection, Filter and

Stream Integration – Matching condition
Discovery and composition of complex event services
are based on matching event patterns (and
aggregated NFP values).
Procedure:
1. Derive canonical forms of event patterns of CESs.
2. Apply tree isomorphism algorithms over the canonical event patterns and the
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requested event pattern to identify reusable or equivalent event patterns.
3. Generate all possible composition plans.
4. Aggregate NFPs based on event patterns and compare aggregated NFP values
against requested constraints to filter out unsatisfied composition plans.
5. Rank the remaining composition plans based on preferences (soft constraints).

Canonical Event Pattern (1/2)
Create complete event patterns
ES1
ES2 ES3
e1 e2
Or
And Seq
e3 e4
ES2
Or
And Seq
e1 e2 e3 e4
getCompletePattern()
ES3
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Canonical Event Pattern (2/2)
Create reduced event patterns
e1
SEQ
SEQ SEQ
e2 e2 e3
e1
SEQ
e2 e2 e3 e1
SEQ
REP
x2 e3
e2
AND
e1 AND
e1 e2
e1
AND
e1 e2 e1
AND
e2
REP
x2
REP
x3
e1
REP
x6
e1
REP
x2
SEQ
e1 e2
e1
REP
x2
e1 e1 e2
REP
x2
e2 REP
x2
e1
Sequential Lift Sequential Merge
Parallel Lift Parallel Merge
Repetition Lift
(1)
Repetition Lift
(2)
Repetition Merge
Figure 4: Examples of syntax tree reduction operations
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Event Composition via Reusability
Create event reusability hierachy
Reusable relation: R(ep1,ep2) holds if Rd(ep1,ep2) or Ri(ep1,ep2) holds.
a set of event
⌥% ↵T(v) ⇤ We denote p1 is
reusable to ep2, denoted
reusable to ep2, but
sequence of op-erations
ep1, as a result, it
have four types:
the roots of syntax
multiplies the cardinality
−⌃ T adds a se-quence
prefixes or su⇥ces;
the parallel roots.
set of syntax trees,
in-directly reusable
a set of event
e1
SEQ
e2
OR
e4
e3
e1
directly reusable in-directly reusable
SEQ
e2 e3
SEQ
e2 e3
in-directly reusable
Figure 7: Example of event pattern reusability
R(p1, p3) ✏R(p3, p2), where p1, p2 P are event patterns of
t1, t2. According to this definition, if we build an ERH for
the three event patterns in Figure 7, the edge at the top-right
is ignored. The nodes do not reuse any other nodes
are called roots in the ERH, the nodes cannot be reused by
other nodes are leaves.
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Stream Integration – Composition Plan
Example of a composition plan:
e1
SEQ
OR
e2
e3
Query
Event Service 1 Event Service 2
e1 e2
Event Service 3 Event Service 4
e1
SEQ
type= e4
loc=loc4
e2
e3
type= e3
loc=loc3
type= e2
loc=loc2
type= e1
loc=loc1
Composition Plan
OR
e3
e4
loc=loc4 loc=loc3
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Semantic Annotation
Application
Interface
ACEIS Core
Resource
Management
Knowledge Base
QoI/QoS
Stream
Description
Data Mgmt,
Indexing,
Caching
User Input
Event Request
Data
Federation
Resource Discovery
Composition Plan
Query Transformer
Query Engine
Query
Results
Adaptation
Manager
Constraint
Validation
Constraint
Violation
Data Store
IoT Data
Stream
Social Data
Stream

Repetition is a generalization of sequence, it indicates a sequence pattern should
be repeated Query several Transformation times, therefore it is – also Semantic not supported. Alignment
An And operator
indicates all its sub-events should occur, it can be mapped to the Join opera-tor.
An Or operator indicates at least one of its sub-events should occur, it can
Goal: transform the composition plan into a stream query which can be
evaluated by a stream reasoning engine over RDF data streams
be mapped to LeftOuterJoin operator in CQELS (OPTIONAL keyword) with
bound filters. Selection is mapped to Projection in CQELS to select the message
payloads for complex events. Filter and Window operators in event patterns
can be mapped to Filter and Window operators in CQELS, respectively. Ta-ble
Requirements:
• Alignments of event pattern operators to stream query operators
• Transformation Algorithm
1 summarizes the semantics alignment between event operators and CQELS
operators.
Table 1. Semantics Alignment
Alignments for CQELS query language:
Event Pattern sd Sequence Repetition And Or Selection Filter Window
CQELS Operator SGP - - Join Optional Projection Filter Window
• Sequence and Repetition not supported by CQELS.
• Sensor requests mapped to Stream Graph Pattern.
• AND operator mapped to stream join.
• OR operator mapped to OPTIONAL keyword (left-outer-join).
5.2 Transformation Algorithm
Previously (see Section 4.1), we briefly described how event patterns are speci-fied
in CES ontology. An event pattern can be recursively defined with sub event
patterns and event service descriptions, thus formulating an event pattern tree.
In this section we elaborate algorithms for parsing event pattern trees and cre-ating
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Query Transformation –Transformation Example
Example of composition plan
AND Event textual description:
seg2
traffic
Monitor the user's current
location as well as the traffic
conditions (estimated travel time)
of all the 3 street segments on
seg1
traffic
seg3
traffic
user
loc
CQELS Query Transformation Result:
the chosen route
Listing 5. CQELS query example
Select ... Where {
Graph http :// purl.oclc.org/NET/ssnx/ssn#
{?ob rdfs:subClassOf ssn: Observation}
Stream locationStreamURL [range 5s]
{? locId rdf:type ?ob. ?locId ssn:observedBy ?es4.
?locId ssn:observationResult ?result1.
?result1 ssn:hasValue ?value1.
?value1 ct:hasLongtitude ?lon. ?value1 ct:hasLatitude ?lat.
?loc ct:hasLongtitude ?lon. }
Stream trafficStreamURL1 [range 5s]
{? seg1Id rdf:type ?ob. ?seg1Id ssn:observedBy ?es1.
?seg1Id ssn:observationResult ?result2.
?result2 ssn:hasValue ?value2.
?value2 ssn:hasQuantityValue ?eta1.}
Stream trafficStreamURL2 [range 5s] {...}
Stream trafficStreamURL3 [range 5s] {...} }
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6 RelatedWork

• Automated Complext Event Implementation System
• Information model for dynamic discovery and selection of sensor
data streams i.e. Complex Event Ontology, Event Pattern
Ontology and Event Reuest/Profile
• Algorithm to create optimal composition plan using event
reusability heirachy
• Transfoormation of the composition plan into stream queries
(CQELS)
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Conclusion
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Semantic Discovery and Integration of Urban Data Streams

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