Dealing with Semantic Heterogeneity in Real-Time Information

EarthBiAs2014

Global
NEST

University
of
the
Aegean

Dealing
with
Seman@c
Heterogeneity
in
Real-‐Time

Informa@on

Dr.
Edward
Curry

Insight
Centre
for
Data
Analy@cs,

Na@onal
University
of
Ireland
Galway

Tuesday
8th
July
2014

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014
1

Talk
Overview

•  Part
I:
Large
Scale
Open
Environments

•  Part
Ii:
ComputaKonal
Paradigms

•  Part
III:
RDF
Event
Processing

•  Part
IV:
Theory
of
Event
Exchange

•  Part
V:
Approaches
to
SemanKc
Decoupling

•  Part
VI:
Example
ApplicaKon:
Linked
Energy

Intelligence

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

About
Me

•  PhD
in
Computer
Science
(NUI

Galway)

•  Green
and
Sustainable
IT

Research
Group
Leader
in
DERI/
Insight
NUI
Galway

•  Researcher
in
both
Computer

Science
and
InformaKon

Systems

Overall Objective
WATERNOMICS will provide personalised and actionable
information about water consumption and water availability
to individual households, companies and cities in an intuitive
and effective manner at a time-scale relevant for decision
making.

Project-‐Sense

Non-Technical Users
•  Targets Occupants of the
Building
•  Non-Technical Office
Workers
•  No experience in Energy
Management
•  Low cost installation
Self-Configuration
•  Collaborative system
configuration
•  Crowdsourced contextual
data from building
occupants
•  Imports relevant
enterprise data via Excel
•  Semantic event matching
reduces configuration
costs
Decision Support
•  Sensor and Data Fusion
•  Multi-level decision
support model
•  Identifies Energy Saving
Opportunities
•  Leverages Open Data and
Predictive Analytics
User Experience
•  From Awareness to
Engagement
•  Transtheoretical Model
•  Gamification
•  User Personalisation
•  Simple non-technical user
interfaces
Self-‐conﬁguring
smart

energy
management

systems
for
small

commercial
buildings

7European Data Forum 2014 BIG 318062
BIG
Big Data Public Private Forum
7 BIG 318062
The BIG Project
BIG aims to promote a well-developed EU industrial
landscape in Big Data:
▶  Providing a clear picture of existing technology trends and
their maturity
▶  Acquiring a sharp understanding of how Big Data can be
applied to concrete environments / use cases
▶  Pushing European Big Data research and innovation to
contribute in increasing European competitiveness
▶  Building a self-sustainable, industry-led initiative
Overall Objective
Work at technical, business and policy levels, shaping
the future through the positioning of IIM and Big Data
specifically in Horizon 2020.
Bringing the necessary stakeholders into a self-
sustainable industry-led initiative, which will greatly
contribute to enhance the EU competitiveness taking
full advantage of Big Data technologies.

@BYTE_EU www.byte-project.eu
Big data roadmap and cross-‐
disciplinarY community for
addressing socieTal Externali9es
• 
The
effects
of
a
decision
by
stakeholders
(e.g.,
governments,
industry,

scienKsts,
policy-‐makers)
that
have
an
impact
on
a
third
party

(especially
members
of
the
public).

• 
May
be
posiKve
or
negaKve

Economic

• Boost
to
the

economy

• InnovaKon

• Increase

efficiency

• Smaller
actors

le]
behind

• Shrink
economies

Legal

• Privacy

• Data
protecKon

• Data
ownership

• Copyright

• Risks
associated

with
inclusion
&

exclusion

Social
&
Ethical

• Transparency

• DiscriminaKon

• Methodological

difficulKes

• Spurious

relaKonships

• Consumer

manipulaKon

PoliKcal

• Reliance
on
US

services

• Services
have

become
uKliKes

• Legal
issues

become
trade

issues

LARGE
SCALE
OPEN
ENVIRONMENTS

PART
I

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Emerging Environments…
Smart
City
Energy

Smart
Building
Water
Management

From
Internet
of
Things
to

Internet
of
Everything

Lots
of
Data
“90%
of
the
data
in
the
world
today
has
been

created
in
the
last
two
years

alone”

–
IBM

“The
bringing
together
of
a
vast
amount
of
data

from
public
and
private
sources
[…]
is
what

Big
Data
is
all
about”
–
IDC

Over
the
next
few
years
we’ll
see
the
adop@on

of
scalable
frameworks
and
pla^orms
for

handling
streaming,
or
near
real-‐@me,

analysis
and
processing.”
–
O’Reilly

Big Data represents a number of
developments in technology that have
been brewing for years and are
coming to a boil. They include an
explosion of data and new kinds of
data, like from the Web and sensor
streams; [...].” – IDC

From
Rigid
Schemas
to
Schema-‐less

13

•  Heterogeneous,
complex
and
large-‐scale
data

•  Very-‐large
and
dynamic
“schemas”

•  Open
Environments:
distributed,
decoupled
data
sources,
anonymous

users,
mulK-‐domain,
lack
of
global
order
of
informaKon
ﬂow

10s-‐100s
aeributes

1,000s-‐1,000,000s
aeributes

circa
2000

circa
2014

Fundamental
DecentralizaKon

14

•  MulKple
perspecKves
(conceptualizaKons)
of
the
reality.

•  Ambiguity,
vagueness,
inconsistency.

Current
Trends

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Small
scale,
controlled

environments

Large
scale,
open
environments

Informa@on
sources
10s
to
100s
1000s
to
millions

Data
heterogeneity
Small
number
of
schemas
High
number
of
schemas

Users
Small
number

Know
the
environment

Large
number

Not
quite
know
the
environment

Users
organiza@on
Users
know
each
others

Top-‐down
hierarchies

(e.g.
enterprises)

Decoupled
and
distributed

Dynamism
Low
High

(sources
and
users
join
and
leave
o]en)

Domain
Domain
speciﬁc
Users
interest
range
from
domain

speciﬁc
to
domain
agnosKc

COMPUTATIONAL
PARADIGMS

PART
II

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

InformaKon
Flow
Processing
(IFP)

•  Users
need
to
collect
informaKon

– Produced
by
mulKple
distributed
sources

– For
Kmely
way
processing

– To
extract
knowledge
asap

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Financial Continuous
Analytics
RFID Inventory
Management
Environmental
Monitoring

InformaKon
Flow
Processing
(IFP)

•  Processing
informaKon
as
it
ﬂows

– No
intermediate
storage

– New
informaKon
produced

– Raw
informaKon
can
be
discarded

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

InformaKon
Flow

Processing
Engine

Producers
Consumers

Rule
managers

CUGOLA,
G.
AND
MARGARA,
A.,
2011.
Processing
ﬂows
of
informaKon:
From
data
stream
to

complex
event
processing.
ACM
Compu:ng
Surveys
Journal.

InformaKon
Flow
Processing
(IFP)

•  Requirements

– Real-‐Kme
or
near
real-‐Kme
processing

– Expressive
language
for
rules

– Scalability
to
large
number
of
producers
and

consumers

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

ComputaKonal
Paradigm

•  Event
Processing

–  Event:
object
represenKng
a
happening.

–  Deals
with
events
and
relaKons
of
events
(e.g.
inter-‐events

sequencing,
causality,
etc.)

•  Stream
Processing

–  Stream:
homogeneous
and
totally
ordered
set
of
data
items.

–  Deals
with
streams
and
operaKons
on
streams
(e.g.
joins).

•  Event
“cloud”
may
contain
steams
of
events
as
well
as

parKally
ordered
set
of
events.

–  (Cugola
&
Margara,
2012)

•  Event
processing
agents,
network,
and
rules.

Event
Processing
Architecture

Producer

Producer

E2

E3

E1

Rule

21
of
31

Event
Processing

Engine

Consumer

Events
Processing
is
Decoupled

for
Scalability

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Event
Processing

Space

Time

SynchronizaKon
Event

source

Event

consumer

Patrick
Th.
Eugster,
Pascal
A.
Felber,
Rachid
Guerraoui,
and
Anne-‐Marie
Kermarrec.
2003.
The
many
faces
of
publish/
subscribe.
ACM
Comput.
Surv.
35,
2
(June
2003),
114-‐131.

AcKve
Databases

•  TradiKonal
database
systems

–  Passive

–  Store
data
and
wait
for
user’s
interacKon

–  ReacKve
behaviour
in
the
applicaKon
layer

–  DAYAL,
U.,
BLAUSTEIN,
B.,
BUCHMANN,
A.,
CHAKRAVARTHY,
U.,
HSU,
M.,
LEDIN,
R.,
MCCARTHY,
D.,
ROSENTHAL,
A.,

SARIN,
S.,
CAREY,
M.
J.,
LIVNY,
M.,
AND
JAUHARI,
R.
1988.
The
hipac
project:
Combining
acKve
databases
and
Kming

constraints.
SIGMOD
Rec.
17,
1,
51–70.

–  LIEUWEN,
D.
F.,
GEHANI,
N.
H.,
AND
ARLEIN,
R.
M.
1996.
The
ode
acKve
database:
Trigger
semanKcs
and

implementaKon.
In
Proceedings
of
the
12th
InternaKonal
Conference
on
Data
Engineering
(ICDE’96).
IEEE
Computer

Society,
Los
Alamitos,
CA,
412–420.

–  GATZIU,
S.
AND
DITTRICH,
K.
1993.
Events
in
an
acKve
object-‐oriented
database
system.
In
Proceedings
of
the

InternaKonal
Workshop
on
Rules
in
Database
Systems
(RIDS),
N.
Paton
and
H.
Williams,
Eds.
Workshops
in

CompuKng,
Springer-‐Verlag,
Edinburgh,
U.K.

–  CHAKRAVARTHY,
S.
AND
ADAIKKALAVAN,
R.
2008.
Events
and
streams:
Harnessing
and
unleashing
their
synergy!
In

Proceedings
of
the
2nd
InternaKonal
Conference
on
Distributed
Event-‐Based
Systems
(DEBS’08).
ACM,
New
York,
NY,

1–12.

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

AcKve
Databases

•  ReacKve
behaviour
to
database
layer

•  Event-‐CondiKon-‐AcKon
(ECA)
rules

– Event:
source.
E.g.
tuple
inserted

– CondiKon:
post
event.
E.g.
inserted.value
>
5

– AcKon:
what
to
do.
E.g.
modify
the
DB

•  Cons

– Persistent
storage
model

– Suitable
when
updates
not
frequent
and
few
rules

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Data
Stream
Management

Systems

•  Streams
unbounded
(not
like
tables)

•  No
arrival
order
assumpKons

•  Typically
no
storage

•  Use
conKnuous,
or
standing,
queries

•  ReacKve
in
nature

•  CHANDRASEKARAN,
S.,
COOPER,
O.,
DESHPANDE,
A.,
FRANKLIN,
M.
J.,
HELLERSTEIN,
J.
M.,
HONG,
W.,
KRISHNAMURTHY,
S.,

MADDEN,
S.
R.,
REISS,
F.,
AND
SHAH,
M.
A.
2003.
Telegraphcq:
ConKnuous
dataﬂow
processing.
In
Proceedings
of
the
ACM

SIGMOD
InternaKonal
Conference
on
Management
of
Data
(SIGMOD’03).
ACM,
New
York,
NY,
668–668.

•  CHEN,
J.,
DEWITT,
D.
J.,
TIAN,
F.,
AND
WANG,
Y.
2000.
Niagaracq:
A
scalable
conKnuous
query
system
for
Internet

databases.
SIGMOD
Rec.
29,
2,
379–390.

•  LIU,
L.,
PU,
C.,
AND
TANG,
W.
1999.
ConKnual
queries
for
internet
scale
event-‐driven
informaKon
delivery.
IEEE
Trans.

Knowl.
Data
Eng.
11,
4,
610–628.

•  ARASU,
A.,
BABU,
S.,
AND
WIDOM,
J.
2006.
The
CQL
conKnuous
query
language:
SemanKc
foundaKons
and
query
execuKon.

VLDB
J.
15,
2,
121–142.

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Data
Stream
Management

Systems

•  ConKnuous
queries
semanKcs

– Answer:
append
only
stream
or
update
store

– Exact
or
approximate
answer

•  Cons

– Atomic
item
is
the
stream

– Not
possible
to
detect
sequencing
or
causal

paeerns

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Publish/Subscribe

Systems

•  InformaKon
items
are
no:ﬁca:on

•  Indirect
addressing-‐based
communicaKon

scheme

•  Ancestors

–  Message
Passing

–  Remote
Procedure
Call
(RPC)

–  Shared
spaces

–  Message
Queueing

EUGSTER,
P.T.,
FELBER,
P.A.,
GUERRAOUI,
R.
AND
KERMARREC,
A.M.,
2003.
The
many
faces
of
publish/subscribe.
ACM
Compu:ng

Surveys
(CSUR),
35(2),
pp.114–131.

MUHL
,
G.,
FIEGE,
L.,
AND
PIETZUCH,
P.
2006.
Distributed
Event-‐Based
Systems.
Springer

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Publish/Subscribe
Systems

•  One-‐to-‐many
and
many-‐to-‐many
distribuKon
mechanism

–  allows
single
producer
to
send
a
message
to
one
user
or

potenKally
hundreds
of
thousands
of
consumers

E.
Curry,
“Message-‐Oriented
Middleware,”
in
Middleware
for
CommunicaKons,
Q.
H.
Mahmoud,

Ed.
Chichester,
England:
John
Wiley
and
Sons,
2004,
pp.
1–28.

IntroducKon
to
Message-‐Oriented

Middleware

28

Publish/Subscribe

Systems

•  Topic-‐based
pub/sub

–  Topics
are
groups
or
channels

–  Events
of
a
topic
are
sent
to
the
topic’s
subscribers

ALTHERR,
M.,
ERZBERGER,
M.,
AND
MAFFEIS,
S.
1999.
iBus—a
so]ware
bus
middleware
for
the
Java
plavorm.
In
Proceedings
of
the
InternaKonal

Workshop
on
Reliable
Middleware
Systems.
43–53.

•  Content-‐based
pub/sub

–  Matching
by
message
filters

–  Publishers
and
subscribers
channels
are
defined
by
the

content
and
the
subscripKons

David
S.
Rosenblum
and
Alexander
L.
Wolf.
1997.
A
design
framework
for
Internet-‐scale
event
observaKon
and
noKficaKon.
SIGSOFT
SoGw.
Eng.

Notes
22,
6
(November
1997),
344-‐360.
DOI=10.1145/267896.267920
hep://doi.acm.org/10.1145/267896.267920

•  Type-‐based
pub/sub

–  Matching
on
type
hierarchy

EUGSTER,
P.
AND
GUERRAOUI,
R.
2001.
Content
based
publish/subscribe
with
structural
reflecKon.
In
Proceedings
of
the
6th
Usenix
Conference
on

Object-‐Oriented
Technologies
andSystems
(COOTS’01).

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Complex
Event
Processing

Systems

•  DetecKon
of
complex
paeerns

– Sequencing

– Causal

– Ordering
in
general

– Of
mulKple
events

– And
generate
complex,

or
derived,
events

LUCKHAM,
D.,
2002.
The
Power
of
Events:
An
Introduc:on
to
Complex
Event
Processing
in
Distributed
Enterprise
Systems,

Addison-‐Wesley
Professional.

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Complex
Event
Processing

Systems

Adapted
from
CUGOLA,
G.
AND
MARGARA,
A.,
2011.
Processing
ﬂows
of
informaKon:
From
data
stream
to
complex
event

processing.
ACM
Compu:ng
Surveys
Journal.

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

RDF
EVENT
PROCESSING

PART
III

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Why
Linked
Data
for
the
IoT?

•  Many
communiKes
struggle
with
closed
approaches

–  E.g.,
pervasive
compuKng,
embedded
systems,
IoT,
...

•  Cyber-‐Physical
Systems
are
inherently
“open
world”

–  Prof.
David
Karger
(MIT)
in
his
ESWC
2013
keynote:

“Semantic Web technologies support and open world assumption where
millions of unforeseeable schemas may have to be integrated.”
•  Simple
integraKon
with
exisKng
LOD
data
sets

–  Geo-‐spaKal,
governmental,
media,
...

•  Manageable
integraKon
eﬀort
with
other
graph
data,
e.g.,
Google

Knowledge
Graph,
Facebook
Graph,
etc.

EU ICT OpenIoT Project
Knowledge-Based Future Internet
Step 2:
Sensor/Cloud
Formulation
Step 1:
Sensing-as-a-Service
Request
Step 3:
Service Provisioning
(Utility Metrics)
Infrastructure’s provider(s) (e.g., Smart City)
OpenIoT User (Citizen, Corporate)
Domain #1 Domain #N
34
Middleware Core features:
Open Source
Linked Data
Cloud Computing
Internet of Things
IoT
Management
Data Privacy
and
Security
Mobility
and
Quality of
Service
www.openiot.eu
EU ICT-2011.1.3 Contract No.: 287305
An Open Source Cloud Solution for the Internet of Things!
Open Source blueprint for large scale self-organizing
cloud environments for IoT applications

Sensor Networks
•  OpenIoT leverages the
SoA on Internet of Things
(IoT) RFID/WSN
middleware frameworks.
•  OpenIoT provides
baseline service
functionalities associated
with registering and
looking up internet-
connected objects (ICOs)
named things.
IoT Management
•  OpenIoT provides
baseline visualization
services.
•  OpenIoT supports
dynamic interoperable
self-organizing
management on cloud
environments for IoT.
•  OpenIoT enables the
autonomy of a variety of
IoT entities and resources.
Cloud Computing
•  OpenIoT allows creation
of PaaS models over
internet-connected
objects.
•  OpenIoT supports
applications that leverage
information from multiple
sensors, actuators and
other devices to the cloud.
•  OpenIoT enables cloud
solutions to support IoT.
Open Source
•  OpenIoT is an open
source solution.
•  OpenIoT is first a kind of
extension of existing open
cloud computing
infrastructures towards the
IoT support.
•  OpenIoT is a customizable
toolkit for the IoT.
OpenIoT Innovation for the Smart Industry www.openiot.eu
Agrifood PhenonetSmart CityManufacturing
Smart Campus Gain Briddes Plant
Key Performance
Indicators Air Quality Silver Angel
Broke
r
Broke
r
Broke
r
Mobile
Broker
P
S
S
35

SemanKc
Sensor
Networks
Ontology

[JoWS 2012]

SSN
ApplicaKon:
SPITFIRE

• DUL: DOLCE+DnS Ultralite
• EventF: Event-Model F
• SSN: SSN-XG
• CC: Contextualised-Cognitive
Concepts on sensor network topology and
devices
Concepts on sensor role, events, sensor project
Event
Datasets
Sensor Datasets
LOD Cloud

CQELS

n  ConKnuous
Query
EvaluaKon
over
Linked

Streams

n  Scalable
processing
model
for
uniﬁed

Linked
Stream
Data
and
Linked
Open
Data

n  Combines
data
pre-‐processing
and
an

adapKve
cost-‐based
query
opKmizaKon

algorithm

[SSN
2009,
SSN
2010,
ISWC
2011]

Linked
Stream
Middleware

[WWW 2009, JoWS 2012, CLOSER 2013]
http://lsm.deri.ie/

LSM:
Live
train
info

Projects
using
Linked
Data
for
IoT

Open Source IoT Architectural Blueprint
http://www.openiot.eu/
https://github.com/OpenIotOrg/openiot
Real-Time IoT Stream Processing and
Large-scale Data Analytics for Smart Cities
http://www.ict-citypulse.eu/
Smart, secure and cost-effective
integrated IoT deployments in smart cities
http://vital-project.eu/
Behaviour-driven Autonomous Services for
smart transportation in smart cities
http://gambas-ict.eu/

THEORY
OF
EVENT
EXCHANGE

PART
IV

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Problem

•  Event
producers
and
consumers
are
semanKcally
coupled

–  Consumers
need
prior
knowledge
of
event
types,
aeributes
and

values.

–  Limits
scalability
in
heterogeneous
and
dynamic
environments

due
to
explicit
dependencies

–  Diﬃcult
development
of
event
processing
subscripKons/rules
in

heterogeneous
and
dynamic
environments.

Space
Time
Synch
Producer Consumer
Semantic

Type
Energy
Consumption

Place
Room 202e
Amount
40 kWh
Type
Electricity
Consumption

Loca@on
Room 202e
Amount
70 kWh
Type
Electricity
Utilized

Venue
Room 202e
Amount
600 kWh
e1
Event
Producers
e.g. Sensors
Type =“Energy Consumption”
Place =“Room 202e”
Type =“Electricity Consumption”
Location =“Room 202e”
Type =“Electricity Utilized”
Venue =“Room 202e”
TradiKonal

Event

Processing

e1
Consumer
e1e2
e1e3
Exact
Matching
Model

Type
Energy
Consumption

Place
Room 202e
Amount
40 kWh
Type
Electricity
Consumption

Loca@on
Room 202e
Amount
70 kWh
Type
Electricity
Utilized

Venue
Room 202e
Amount
600 kWh
e1
Event
Producers
e.g. Sensors
e1
e1e2
e1e3
SemanKc

Event

Processing

Type =“Energy Consumption”~
Location =“Room 202e”
Consumer
SemanKc
Matching

How
Good
are
Our
Paradigms?

•  Scale

– Big
volume

– Big
Velocity

– Big
Variety

•  Distributed
sources
and
consumers

•  The
big
challenge
is
now
in
the
exchange
of

knowledge
at
a
very
large-‐scale

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Shannon-‐Weaver
Model

C.
Shannon
and
W.
Weaver.
The
mathemaKcal
theory
of
communicaKon.
University
of
Illinois
Press,
1949.

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Cross-‐Boundaries
Exchange

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

SyntacKc

SemanKc

PragmaKc

Producer
Consumer

P.
R.
Carlile.
Transferring,
translaKng,
and
transforming:
An
integraKve
framework
for
managing
knowledge
across
boundaries.

OrganizaKon
science,
15(5):555{568,
2004.

Boundaries

Open

environment

Known

environment

SyntacKc
Boundary

•  Transfer
is
the
most
common
type
of

informaKon
movement
across
this
boundary

•  A
common
lexicon
exists

– Move
and
process
syntax
(0’s
and
1’s)

– Dominant
form
of
Shannon
Weaver’s
theory

•  E.g.
Diﬀerent
data
models
of
events

•  E.g.
Transfer
RDF
events
over
HTTP

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

SemanKc
Boundary

•  Common
lexicon
doesn’t
exist

•  Lexicon
evolve

•  AmbiguiKes
exist

•  TranslaKon
is
the
process
to
cross
this

boundary

•  E.g.
Diﬀerent
ontologies
for
sensors

•  E.g.
Ontology
alignment
for
RDF
events

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

PragmaKc
Boundary

•  Actors
on
the
sides
of
the
boundary
have:

–  Different
contexts

–  Different
perspecKves

–  Different
interests

•  TransformaKon
is
the
process
to
cross
this

boundary

•  E.g.
Temp
sensor
reading
of
35
celsius
is

acceptable
from
outdoor
sensors
but
not
from

indoor

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Cross-‐Boundaries
Exchange

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

SyntacKc

SemanKc

PragmaKc

Producer
Consumer

Boundaries

Open

environment

Known

environment

P.
R.
Carlile.
Transferring,
translaKng,
and
transforming:
An
integraKve
framework
for
managing
knowledge
across
boundaries.

OrganizaKon
science,
15(5):555{568,
2004.

Transfer-‐Translate-‐Transform

•  Current
approaches
in
event
processing

•  Transfer

–  Common
event/language
models

•  E.g.
RDF
over
HTTP

•  Translate

–  Agreements
on
schemas/thesauri/ontologies

•  E.g.
DERI
Energy
ontology
for
building
energy
events

•  Curry,
Edward,
et
al.
"Linking
building
data
in
the
cloud:
IntegraKng
cross-‐domain
building
data
using
linked

data."
Advanced
Engineering
Informa:cs
27.2
(2013):
206-‐219.

•  Transform

–  Dedicated
enrichers,
joins
in
event
languages

•  CQELS
language
for
Linked
Stream
Data
mashups

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Decoupling
for
Scalability

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Event
Processing

Space

Time

SynchronizaKon
Event

source

Event

consumer

Patrick
Th.
Eugster,
Pascal
A.
Felber,
Rachid
Guerraoui,
and
Anne-‐Marie
Kermarrec.
2003.
The
many
faces
of
publish/
subscribe.
ACM
Comput.
Surv.
35,
2
(June
2003),
114-‐131.

SemanKc
Coupling

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Event
Processing

Space

Time

SynchronizaKon

Event

source

Event

consumer
SemanKc
Coupling

type,
aTributes,
values

APPROACHES
TO
SEMANTIC
COUPLING

Part
V

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Loosening
the
SemanKc
Coupling

•  Approach
1:
Content-‐Based
with
SemanKc
Decoupling

–  A.
Carzaniga,
D.
S.
Rosenblum,
and
A.
L.
Wolf.
Achieving
scalability
and
expressiveness
in
an
internet-‐scale
event
noK_caKon
service.
In
Proceedings
of
the

nineteenth
annual
ACM
symposium
on
Principles
of
distributed
compuKng,
pages
219-‐227.
ACM,
2000.

•  Approach
2:
Content-‐Based
with
Implicit
Shared

Agreements

•  David
S.
Rosenblum
and
Alexander
L.
Wolf.
1997.
A
design
framework
for
Internet-‐scale
event
observaKon
and
noKﬁcaKon.
SIGSOFT
SoGw.
Eng.
Notes
22,
6

(November
1997),
344-‐360.
DOI=10.1145/267896.267920
hep://doi.acm.org/10.1145/267896.267920

•  Approach
3:
Concept-‐Based

–  M.
Petrovic,
I.
Burcea,
and
H.-‐A.
Jacobsen.
S-‐topss:
semanKc
toronto
publish/subscribe
system.
In
Proceedings
of
the
29th
internaKonal

conference
on
Very
large
data
bases
-‐
Volume
29,
VLDB
'03,
pages
1101-‐1104.
VLDB
Endowment,
2003.

•  Approach
4:
Loose
SemanKc
Coupling
+
ApproximaKon

–  Hasan,
S.
and
Curry,
E.,
2014.
Approximate
SemanKc
Matching
of
Events
for
The
Internet
of
Things.
ACM
Transac:ons

on
Internet
Technology
(TOIT).
In
Press

•  Approach
5:
Theme-‐Based

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Current
Approaches

Semantic Decoupling
Effectiveness & Efficiency
Content-based
Concept-based
Bottom-up
Semantics

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Approach
1:
Content-‐Based
with

SemanKc
Decoupling

•  Very
low
detecKon
rate

– High
false
posiKves/negaKves

– Low
precision/recall

Producer
Consumer

event

Seman@c
De-‐Coupling

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
B

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Approach
1:
Content-‐Based
with

SemanKc
Decoupling

•  Use
many
rules
to
improve
detecKon

– Time
and
eﬀort

– Aﬀects
scalability
to
heterogeneous
environments

Producer
Consumer

event

Seman@c
De-‐Coupling

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
A

Interested
in
B

Interested
in
C

Approach
2:
Content-‐Based
with

Implicit
Shared
Agreements

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Producer
Consumer

event

Seman@c
Coupling
via

Implicit
Agreements

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
A

Face-‐to-‐face,
or
via

documentaKon

Use
symbol
A
to
describe

Approach
2:
Content-‐Based
with

Implicit
Shared
Agreements

•  Implicit
semanKcs

– Top-‐down
approach
to
semanKcs

– Granular
on
the
level
of
concepts

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Producer
Consumer

event

Seman@c
Coupling
via

Implicit
Agreements

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
A

Approach
2:
Content-‐Based
with

Implicit
Shared
Agreements

•  Need
for
shared
agreements

– Time
and
eﬀort

– Aﬀects
scalability
to
heterogeneous
environments

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Producer
Consumer

event

Seman@c
Coupling
via

Implicit
Agreements

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
A

Approach
3:
Concept-‐Based

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Producer
Consumer

event

Seman@c
Coupling
via

Ontologies

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
B

C

D

B

E

A

F
subClassOf

Approach
3:
Concept-‐Based

•  Explicit
semanKcs

– Top-‐down
approach
to
semanKcs

– Granular
on
the
level
of
concepts

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Producer
Consumer

event

Seman@c
Coupling
via

Ontologies

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
B

Approach
3:
Concept-‐Based

•  Need
for
shared
agreements

– Time
and
eﬀort

– Aﬀects
scalability
to
heterogeneous
environments

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Producer
Consumer

event

Seman@c
Coupling
via

Ontologies

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
B

•  Most
semanKc
models
have
dealt
with
parKcular
types
of
construcKons,

and
have
been
carried
out
under
very
simplifying
assumpKons,
in
true
lab

condiKons.

•  If
these
idealizaKons
are
removed
it
is
not
clear
at
all
that
modern

semanKcs
can
give
a
full
account
of
all
but
the
simplest
models/
statements.

Sahlgren,
2013

Formal
World

Real
World

SemanKcs
for
a
Complex
World

67

Baroni
et
al.
2013

Distributional Semantic
Model
•  Distributional hypothesis: the context surrounding a given
word in a text provides relevant information about its
meaning.
•  Simplified semantic model.
–  Associational and quantitative.
•  Explicit Semantic Analysis (ESA) is the primary distributional
model used in this work.
68
A
wife
is
a
female
partner
in
a
marriage.
The
term
"wife"
seems
to
be
a

close
term
to
bride,
the
laeer
is
a
female
parKcipant
in
a
wedding

ceremony,
while
a
wife
is
a
married
woman
during
her
marriage.

...

DistribuKonal
SemanKc
Model

c1
child
husband
spouse
cn
c2
function (number of times that the words occur in c1)
0.7
0.5
Commonsense is here
69

(Freitas,
2012)

SemanKc
Relatedness

70

θ
c1
child
husband
spouse
cn
c2
Works as a semantic ranking function
E.g.
esa(room,
building)=
0.099

E.g.
esa(room,
car)=
0.009

(Freitas,
2012)

Approach
4:
Loose
SemanKc

Coupling
+
ApproximaKon

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Producer
Consumer

event

Loose
Seman@c
Coupling

via
Large
Text
Corpora

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
B

A
d1
d2
d3
d4
d5
d6
d7
d8
….

B
d1
d3
d4
d17
d25
d26
d77
d78
….

~

(Hasan
et
al.,
2004)

Approach
4:
Loose
SemanKc

Coupling
+
ApproximaKon

•  Boeom-‐up
model
of
semanKcs

•  Global
semanKcs:
distribuKon
vs.
granular

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Producer
Consumer

event

Loose
Seman@c
Coupling

via
Large
Text
Corpora

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
B

~

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Approach
4:
Loose
SemanKc

Coupling
+
ApproximaKon

•  Low
cost
to
Scale
to
heterogeneous

environments

•  Slightly
lower
detecKon
rate

Producer
Consumer

event

Loose
Seman@c
Coupling

via
Large
Text
Corpora

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
B

~

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Approach
5:
Theme-‐Based

•  Can
we
exchange
beeer
approximaKons
of

meanings
rather
than
mere
symbols
to

improving
detecKon
rate?

Producer
Consumer

event

Loose
Seman@c
Coupling

via
Large
Text
Corpora

Happened

Publish:

A
Happened

Interested
in

Subscribe:

Interested
in
B

~

(Hasan
and
Curry,
2014)

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Approach
5:
Theme-‐Based

Producer
Consumer

event

Loose
Seman@c
Coupling

via
Large
Text
Corpora

Happened

Publish:

(A+T1)

Happened

Interested
in

Subscribe:

Interested
in
(B
+T2)

A
d1
d2
d3
d4
d5
d6
d7
d8
….

B
d1
d3
d4
d17
d25
d26
d77
d78
….

~

Theme
T2

The
ThemaKc
Approach

•  Exchange
approximaKons
of
meanings

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Event

Publisher

Alice

Consumer

Bob

Theme
the

Payload

Subscrip@on

Theme
ths

Expression

Approximate

matcher

ParameterizaKon

Loose
coupling
mode:
lightweight
agreement
on
themes

No
coupling
mode:
free
use
of
well
representaKve
themes

Hasan,
S.
and
Curry,
E.,
2014.
ThemaKc
Event
Processing.
Middleware
2014.
Under
review.

Event
RepresentaKon

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Event

energy,
appliances,
building

type:
increased
energy
consumpKon
event,

measurement
unit:
kilowae
per
hour,

device:
computer,

oﬃce:
room
112

SubscripKon
RepresentaKon

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Subscrip@on

power,
computers

type=
increased
energy
usage
event~,

device~=
laptop~,

oﬃce=
room
112

ProbabilisKc
Approximate

Matcher

•  Top-‐1
and
Top-‐k
mappings
between
an
event

and
a
subscripKon

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Building
IoT
So]ware

7-‐11
July
2014,
Rhodes,
Greece

Indexing

Collector

SemanKc

relatedness

web
service

Textual

corpus

Vector

space

index

Consumer
Bob

(user)

Publisher
Alice

Publish
+
thema:c
tags

ThemaKc
event
processing
engine(s)

Approximate
single
event
matching

Subscribe
+

thema:c

tags

IoT
sensors

Terms
+

themes
pairs

Relatedness

score

Collector
Publisher
Carol

Publish
+
thema:c
tags

Collector
Publisher
Dave

Publish
+
thema:c
tags

Consumer
Dan

(applicaKon
developer)

Consumer
Erin

(applicaKon
developer)

Heterogeneous
IoT
Events

Relevant

events

normalized

for
Bob

Subscribe
+

thema:c

tags

Relevant

events

normalized

for
Dan

Subscribe
+

thema:c

tags

Relevant

events

normalized

for
Erin

Summary

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Simple

Content-‐
based

Content-‐
based
+

Many
Rules

Concept-‐
based

Simple

Distribu@onal
+

Approxima@on

Thema@c

Matching
exact
string

matching

exact
string

matching

Boolean
semanKc

matching

approximate
semanKc

matching

approximate

semanKc

matching

SemanKc

Coupling

term-‐level
full

agreement

term-‐level
full

agreement

concept-‐level
shared

agreement

loose
agreement
loose

agreement

SemanKcs
not
explicit
not
explicit
top-‐down
ontology-‐
based

staKsKcal
model
based

on
distribuKonal

semanKcs

staKsKcal
model

based
on

distribuKonal

semanKcs
+

themes

EffecKveness

very
low
100%
depends
on
the

domains
and

number
of
concept

models

depends
on
the
corpus
depends
on
the

corpus
+
theme

representaKves

Cost
defining
a
small

number
of
rules

defining
a
large

number
of
rules

establishing
shared

agreement

on
ontologies

minimal
agreement
on
a

large

textual
corpus

minimal

agreement
on
a

large

textual
corpus
+

good
theme

representaKves

Efficiency
high
high
medium
to
high
medium
to
high
Medium
to
high

EvaluaKon
Dataset

•  Seed
events
synthesized
from
IoT
sensors

•  SmartSantander
smart
city
project

–  Luis
Sanchez,
Jos´e
Antonio
Galache,
Veronica
GuKerrez,
JM
Hernandez,
J
Bernat,
Alex
Gluhak,
and
Tom´as
Garcia.

2011.
SmartSantander:
The
meeKng
point
between
Future
Internet
research
and
experimentaKon
and
the
smart

ciKes.
In
Future
Network
&
Mobile
Summit
(FutureNetw),
2011.
IEEE,
1–8.

• 
Sensor
CapabiliKes

–  solar
radiaKon,
parKcles,
speed,
wind
direcKon,
wind

speed,
temperature,
water
ow,
atmospheric
pressure,

noise,
ozone,
rainfall,
parking,
radiaKon
par,
co,

ground
temperature,
light,
no2,
soil
moisture
tension,

relaKve
humidity,
energy
consumpKon,
cpu
usage,

memory
usage

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Hasan,
S.
and
Curry,
E.,
2014.
Approximate
SemanKc
Matching
of
Events
for
The
Internet
of
Things.
ACM

Transac:ons
on
Internet
Technology
(TOIT).
In
Press

EvaluaKon
Dataset

•  Seed
events
synthesized
from
IoT
sensors

•  Linked
Energy
Intelligence
plavorm

–  Edward
Curry,
Souleiman
Hasan,
and
Sean
O’Riain.
2012.
Enterprise
energy
management
using
a
linked
dataspace
for

Energy
Intelligence.
In
Sustainable
Internet
and
ICT
for
Sustainability
(SustainIT),
2012.
IEEE,
1–6.

•  Car
brands
from
the
yahoo
directory

–  Yahoo!
2013.
Yahoo!
Directory:
AutomoKve
-‐
Makes
and
Models.
(2013).
hep://dir.yahoo.com/recreaKon/

automoKve/makes
and
models/

•  Home
based
appliances
from
BLUED
dataset

–  Kyle
Anderson,
Adrian
Ocneanu,
Diego
Benitez,
Derrick
Carlson,
Anthony
Rowe,
and
Mario
Berges.
2012.
BLUED:
A

Fully
Labeled
Public
Dataset
for
Event-‐Based
Non-‐Intrusive
Load
Monitoring
Research.
In
Proc.
SustKDD.

•  Rooms
from
DERI
Building

–  Richard
Cyganiak.
2013.
Rooms
in
the
DERI
building.
(2013).
hep://lab.linkeddata.deri.ie/2010/deri-‐rooms

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Hasan,
S.
and
Curry,
E.,
2014.
Approximate
SemanKc
Matching
of
Events
for
The
Internet
of
Things.
ACM

Transac:ons
on
Internet
Technology
(TOIT).
In
Press

EvaluaKon

•  FScore
up
to
95%
and
1000s
events/sec

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Hasan,
S.
and
Curry,
E.,

2014.
Approximate

SemanKc
Matching
of

Events
for
The
Internet

of
Things.
ACM

Transac:ons
on

Internet
Technology

(TOIT).
In
Press

EXAMPLE
APPLICATION:

LINKED
ENERGY
INTELLIGENCE

PART
VI

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

New
Smart
Building

86

Cost
-‐
€
40,000,000

A
Real-‐World
Example

87

Time Monday Tuesday Wednesday Thursday Friday
08:00-‐09:00
09:00-‐10:00 237 237 200 237
10:00-‐11:00 237 237 237 200
11:00-‐12:00 237 180 180 145 237
12:00-‐13:00 237 200 237 200 149
13:00-‐14:00 145
14:00-‐15:00 221 237 145 140
15:00-‐16:00 221 120 160 140
16:00-‐17:00 149 250 160
17:00-‐18:00 200 160
CO2
levels

ASHRAE

62.1-‐2010

Occupancy
Paeern

AirCon
8:30-‐11:00
&
15:00-‐16:00
Mon
to
Fri

Cost
-‐
€
40,000,000

Legacy
Building

•  DERI
Building

•  No
BMS
or
BEMS

•  160
person
Oﬃce
space

•  Café

•  Data
centre

•  3
Kitchens

•  80
person
Conference

room

•  4
MeeKng
rooms

•  CompuKng
museum

•  Sensor
Lab

88

Energy
Management
System

Energy
Management
So]ware

HolisKc
Energy
ConsumpKon

Holis@c

Energy

Management

FaciliKes

Business
Travel
Data
Centre

Daily
Commute
Oﬃce
IT

Business
Context
of
Energy

ConsumpKon

Resource
Allocation
Energy
Finance
Asset Mgmt
Human
Resources

MulK-‐Level
Energy
Analysis

Example KPI:
Energy used by
global IT department
CIO
Example KPI:
PUE of the
Data Center in Dublin
Helpdesk
Example KPI:
kWhs used by
server 172.16.0.8
Maintenance Personnel
Building
Data Center
CEO
CSO
Operational Analysis
•  Technician needs
equipment power usage
•  Low-level monitoring
Sensors, events
Strategic Analysis
•  CIO needs high-level
business function power
usage
•  CSO real-time carbon
emissions
Tactical Analysis
•  Manager needs energy
usage of business
processes, business line or
group
94 of

Key
Challenges

•  Technology
and
Data
Interoperability

•  Data
scaeered
among
different

systems

•  MulKple
incompaKble
technologies
make
it
difficult
to
use

•  InterpreKng
Dynamic
and
StaKc
Data

•  Sensors,
ERP,
BMS,
assets
databases,
…

•  Need
to
proacKvely
idenKfy
efficiency
opportuniKes

•  Empowering
AcKons
and
Including
Users
in
the

Loop

•  Understanding
of
direct
and
indirect
impacts
of
acKviKes

•  Embedding
impacts
within
business
processes

•  Engaging
Users

95

96

Building
Data Center
Office IT
Logistics
Corporate
Organisation-level
Business Process Personal-level
Linked
dataspace
for

Energy
Intelligence

Linked
Energy
Intelligence

Linked
Energy
Intelligence
Applications
Energy Analysis
Model
Complex Events
Situation Awareness
Apps
Energy and
Sustainability Dashboards
Decision Support
Systems
LinkedData
Support
Services
Entity
Management
Service
Data
Catalog
Complex Event
Processing
Engine
Provenance Search &
Query
Sources
Adapter Adapter Adapter Adapter Adapter
n  Cloud of Energy Data
n  Linked Sensor Middleware
n  Resource Description
Framework (RDF)
n  Semantic Sensor Networks
n  Constrained Application
Protocol (CoAP)
n  Semantic Event Processing
n  Collaborative Data Mgmt.
n  Energy Saving Applications
n  Energy Awareness
Curry E. et al, Enterprise Energy Management using a Linked dataspace for Energy
Intelligence. In: The Second IFIP Conference on Sustainable Internet and ICT for
Sustainability (SustainIT) 2012.

Energy
Saving
ApplicaKons

Enterprise Energy
Observatory
Smart Buildings Green Cloud
Computing
Office IT Energy Mgmt. Personal Energy Mgmt.

Building
Energy
Explorer

99 of 26
1.  Data
from

Enterprise

Linked
Data

Cloud

2.  Sensor
Data

3.  Building

Energy

SituaKon

Awareness

Energy
Analysis
by
Group

@WATERNOMICS_EU www.waternomics.eu102
Concrete Objectives
•  To introduce demand response and accountability principles
(water footprint) in the water sector
•  To engage consumers in new interactive and personalized ways
that bring water efficiency to the forefront and leads to changes in
water behaviours
•  To empower corporate decision makers and municipal area
managers with a water information platform together with
relevant tools and methodologies to enact ICT-enabled water
management programs
•  To promote ICT enabled water awareness using airports and
water utilities as pilot examples
•  To make possible new water pricing options and policy actions by
combining water availability and consumption data
WATERNOMICS will provide personalised and actionable
information on water consumption and water availability to
individual households, companies and cities in an intuitive &
effective manner at relevant time-scales for decision making

WATERNOMICS PLATFORM ARCHITECTURE
Support
Services
SourcesApplications
Water Analysis
Model
Complex Events
Usage Model Water Dashboards
Entity
Management
Service
Decision Support
Systems
LinkedWater
Data
Data
Catalog
Complex Event
Processing
Engine
Prediction Search &
Query
Adapter Adapter Adapter Adapter Adapter
▶ Water Management Apps
▶ Water Data Analysis and
Prediction
▶ Semantic Sensor
Networks and Complex
Event Processing to aid
Decision Making
▶ Linking of data from
different Water
Management Sustems
using Linked Data / RDF

PILOT OVERVIEW
# Focus Location Intent Partner
1
Water utility for
domestic users
(Thermi)
To demonstrate, validate, and assess the
WATERNOMICS Platform for domestic
water users
2
Water
Management
Cycle in an
airport
(Milan Linate)
To demonstrate, validate, and assess the
WATERNOMICS methodology and
hardware innovations, and software/
analysis results via the deployment of
WATERNOMICS ICT
3
Water
distribution in a
Municipality
(Sochaczew)
To validate and showcase the
WATERNOMICS Platform at a municipal
level (i.e. mixed use consumers supplied
by a water utility)

Conclusions

•  Coupling
necessary
for
crossing
boundaries

•  Decoupling
necessary
for
scalable
so]ware

•  Event-‐based
systems
do
not
address
the

coupling/decoupling
tradeoﬀ
for
semanKcs

•  Approximate
and
themaKc
event
processing

exchange
approximaKons
of
meaning
with

loose
semanKc
coupling

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Dataset
and
So]ware

•  Dataset

– Souleiman
Hasan,
Edward
Curry,
ThemaKc
event

processing
dataset,
DOI:
10.13140/2.1.3342.9123

•  hep://www.researchgate.net/publicaKon/263673956_ThemaKc_event_processing_dataset

•  Collider

–  Souleiman
Hasan,
Kalpa
Gunaratna,
Yongrui
Qin,
and
Edward
Curry.
2013.
Demo:
approximate
semanKc
matching
in

the
collider
event
processing
engine.
In
Proceedings
of
the
7th
ACM
interna:onal
conference
on
Distributed
event-‐
based
systems
(DEBS
'13).
ACM,
New
York,
NY,
USA,
337-‐338.
DOI=10.1145/2488222.2489277

hep://doi.acm.org/10.1145/2488222.2489277

•  Easy
ESA

–  EasyESA
is
an
implementaKon
of
Explicit
SemanKc
Analysis
(ESA)

–  hep://treo.deri.ie/easyesa/

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

References

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G.
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2011.
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2003.
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2014.
Approximate
SemanKc
Matching
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The
Internet
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ACM
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Technology
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In
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Ed.
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7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

More
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G.
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MARGARA,
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2011.
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informaKon:
From
data
stream
to
complex
event
processing.
ACM
Compu:ng

Surveys
Journal.

•  EUGSTER,
P.T.,
FELBER,
P.A.,
GUERRAOUI,
R.
AND
KERMARREC,
A.M.,
2003.
The
many
faces
of
publish/subscribe.
ACM
Compu:ng
Surveys

(CSUR),
35(2),
pp.114–131.

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D.,
2002.
The
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•  DAYAL,
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BLAUSTEIN,
B.,
BUCHMANN,
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M.
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LIVNY,
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F.,
GEHANI,
N.
H.,
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ode
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•  GATZIU,
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AND
DITTRICH,
K.
1993.
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on
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Paton
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•  CHAKRAVARTHY,
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AND
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2008.
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ACM,
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•  CHANDRASEKARAN,
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O.,
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•  CHEN,
J.,
DEWITT,
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L.,
PU,
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TANG,
W.
1999.
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2006.
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FIEGE,
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ERZBERGER,
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AND
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7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

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•  David
S.
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Wolf.
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1997),
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hep://doi.acm.org/10.1145/267896.267920

•  EUGSTER,
P.
AND
GUERRAOUI,
R.
2001.
Content
based
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with
structural
reflecKon.
In
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of
the
6th
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Conference
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andSystems
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•  P.
R.
Carlile.
Transferring,
translaKng,
and
transforming:
An
integraKve
framework
for
managing
knowledge
across
boundaries.

OrganizaKon
science,
15(5):555{568,
2004.

•  Curry,
Edward,
Souleiman
Hasan,
and
Seán
O'Riain.
"Enterprise
energy
management
using
a
linked
dataspace
for
energy

intelligence."
Sustainable
Internet
and
ICT
for
Sustainability
(SustainIT),
2012.
IEEE,
2012.

•  Curry,
Edward,
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"Linking
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•  Patrick
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and
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2003.
The
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of
publish/subscribe.
ACM

Comput.
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35,
2
(June
2003),
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•  A.
Carzaniga,
D.
S.
Rosenblum,
and
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L.
Wolf.
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•  Edward
Curry,
Souleiman
Hasan,
and
Sean
O’Riain.
2012.
Enterprise
energy
management
using
a
linked
dataspace
for
Energy

Intelligence.
In
Sustainable
Internet
and
ICT
for
Sustainability
(SustainIT),
2012.
IEEE,
1–6.

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2013.
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Directory:
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-‐
Makes
and
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(2013).
hep://dir.yahoo.com/recreaKon/
automoKve/makes
and

models/

•  Kyle
Anderson,
Adrian
Ocneanu,
Diego
Benitez,
Derrick
Carlson,
Anthony
Rowe,
and
Mario
Berges.
2012.
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A
Fully
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Public

Dataset
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Rooms
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building.
(2013).
hep://lab.linkeddata.deri.ie/2010/deri-‐rooms

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

Credits

Green
and
Sustainable
IT
Group
at
Insight
Galway

for
all
their
hard
work.

Special
thanks
to
Souleiman
Hasan
for
his

assistance
with
the
Tutorial

Andre
Freitas
–
Slides
on
DistribuKonal
SemanKcs

Prof.
Manfred
Hauswirth
and
USM
at
Insight

Galway
(LSM,
OpenIoT,
etc..)

7-‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

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