The document discusses computational paradigms for large scale open environments. It describes how environments have shifted from small controlled ones to large open ones with thousands of data sources and schemas. This requires processing information as it flows in real-time from multiple distributed sources. The talk introduces the concept of Information Flow Processing, which processes information as it streams in without intermediate storage. Examples of domains where this paradigm can be applied are given like financial analytics, inventory management and environmental monitoring.

1. 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

2. 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

3. 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

5. 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.

6. 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

7. 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.

8. @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

9. LARGE
SCALE
OPEN
ENVIRONMENTS
PART
I
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

10. Emerging Environments…
Smart
City
Energy
Smart
Building
Water
Management

11. From
Internet
of
Things
to
Internet
of
Everything

12. 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

13. 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

14. Fundamental
DecentralizaKon
14
• MulKple
perspecKves
(conceptualizaKons)
of
the
reality.
• Ambiguity,
vagueness,
inconsistency.

15. 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

16. COMPUTATIONAL
PARADIGMS
PART
II
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

17. 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

18. 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.

19. 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

20. 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)

21. • Event
processing
agents,
network,
and
rules.
Event
Processing
Architecture
Producer
Producer
E2
E3
E1
Rule
21
of
31
Event
Processing
Engine
Consumer

22. 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.

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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

32. RDF
EVENT
PROCESSING
PART
III
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

33. 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.

34. 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

35. 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

36. SemanKc
Sensor
Networks
Ontology
[JoWS 2012]

37. 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

38. 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]

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

40. LSM:
Live
train
info

41. 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/

42. THEORY
OF
EVENT
EXCHANGE
PART
IV
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

43. 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

44. 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

45. 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

46. 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

47. 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

48. 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

49. 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

50. 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

51. 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

52. 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.

53. 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

54. 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.

55. SemanKc
Coupling
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014
Event
Processing
Space
Time
SynchronizaKon
Event
source
Event
consumer
SemanKc
Coupling
type,
aTributes,
values

56. APPROACHES
TO
SEMANTIC
COUPLING
Part
V
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

57. 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

58. Current
Approaches
Semantic Decoupling
Effectiveness & Efficiency
Content-based
Concept-based
Bottom-up
Semantics

59. 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

60. 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

61. 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

62. 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

63. 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

64. 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

65. 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

66. 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

67. • 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

68. 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.
...

69. 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)

70. 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)

71. 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)

72. 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
~

73. 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
~

74. 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)

75. 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

76. 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.

77. 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

78. SubscripKon
RepresentaKon
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014
Subscrip@on
power,
computers
type=
increased
energy
usage
event~,
device~=
laptop~,
office=
room
112

79. ProbabilisKc
Approximate
Matcher
• Top-­‐1
and
Top-­‐k
mappings
between
an
event
and
a
subscripKon
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

80. 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

81. 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

82. 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

83. 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

84. 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

85. EXAMPLE
APPLICATION:
LINKED
ENERGY
INTELLIGENCE
PART
VI
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

86. New
Smart
Building
86
Cost
-­‐
€
40,000,000

87. 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

88. 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

89. Energy
Management
System

90. Sensors
90
of
26

91. Energy
Management
So]ware

92. HolisKc
Energy
ConsumpKon
Holis@c
Energy
Management
FaciliKes
Business
Travel
Data
Centre
Daily
Commute
Office
IT

93. Business
Context
of
Energy
ConsumpKon
Resource
Allocation
Energy
Finance
Asset Mgmt
Human
Resources

94. 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

95. 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. 96
Building
Data Center
Office IT
Logistics
Corporate
Organisation-level
Business Process Personal-level
Linked
dataspace
for
Energy
Intelligence
Linked
Energy
Intelligence

97. 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.

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

99. Building
Energy
Explorer
99 of 26
1. Data
from
Enterprise
Linked
Data
Cloud
2. Sensor
Data
3. Building
Energy
SituaKon
Awareness

100. Energy
Analysis
by
Group

101. iEnergy
–
Personal

102. @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

103. @WATERNOMICS_EU www.waternomics.eu103
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

104. @WATERNOMICS_EU www.waternomics.eu104
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)

105. 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

106. 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

107. References
• CUGOLA,
G.
AND
MARGARA,
A.,
2011.
Processing
flows
of
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.
• Carlile,
Paul
R.
"Transferring,
translaKng,
and
transforming:
An
integraKve
framework
for
managing
knowledge
across
boundaries."
Organiza:on
science15.5
(2004):
555-­‐568.
• 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
• HASAN,
S.,
O’RIAIN,
S.
AND
CURRY,
E.,
2013.
TOWARDS
UNIFIED
AND
NATIVE
ENRICHMENT
IN
EVENT
PROCESSING
SYSTEMS.
IN
THE
7TH
ACM
INTERNATIONAL
CONFERENCE
ON
DISTRIBUTED
EVENT-­‐BASED
SYSTEMS
(DEBS
2013).
ARLINGTON,
TEXAS,
USA:
ACM.
• HASAN,
S.,
O’RIAIN,
S.
AND
CURRY,
E.,
2012.
Approximate
SemanKc
Matching
of
Heterogeneous
Events.
In
6th
ACM
Interna:onal
Conference
on
Distributed
Event-­‐Based
Systems
(DEBS
2012).
Berlin,
Germany:
ACM,
pp.
252–263.
• HASAN,
S.
AND
CURRY,
E.,
2014.
ThemaKc
Event
Processing.
Middleware
2014.
Under
review.
• HASAN,
S.,
CURRY,
E.,
BANDUK,
M.,
AND
O’RIAIN,
S.
TOWARD
SITUATION
AWARENESS
FOR
THE
SEMANTIC
SENSOR
WEB:
COMPLEX
EVENT
PROCESSING
WITH
DYNAMIC
LINKED
DATA
ENRICHMENT.
THE
4TH
INTERNATIONAL
WORKSHOP
ON
SEMANTIC
SENSOR
NETWORKS
2011
(SSN11),
(2011),
60–72.
• E.
Curry,
“Message-­‐Oriented
Middleware,”
in
Middleware
for
CommunicaKons,
Q.
H.
Mahmoud,
Ed.
Chichester,
England:
John
Wiley
and
Sons,
2004,
pp.
1–28.
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

108. More
References
• P.
McFedries,
The
coming
data
deluge,
IEEE
Spectrum,
2011.
• CUGOLA,
G.
AND
MARGARA,
A.,
2011.
Processing
flows
of
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.
• LUCKHAM,
D.,
2002.
The
Power
of
Events:
An
Introduc:on
to
Complex
Event
Processing
in
Distributed
Enterprise
Systems,
Addison-­‐Wesley
Professional.
• 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.
• 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.
• MUHL
,
G.,
FIEGE,
L.,
AND
PIETZUCH,
P.
2006.
Distributed
Event-­‐Based
Systems.
Springer
• 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..
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

109. More
References
• 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
• 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).
• C.
Shannon
and
W.
Weaver.
The
mathemaKcal
theory
of
communicaKon.
University
of
Illinois
Press,
1949.
• 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,
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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Yahoo!
2013.
Yahoo!
Directory:
AutomoKve
-­‐
Makes
and
Models.
(2013).
hep://dir.yahoo.com/recreaKon/
automoKve/makes
and
models/
• 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.
• Richard
Cyganiak.
2013.
Rooms
in
the
DERI
building.
(2013).
hep://lab.linkeddata.deri.ie/2010/deri-­‐rooms
7-­‐11
July
2014,
Rhodes,
Greece
EarthBiAs2014

110. 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