0
EarthBiAs2014	
  
Global	
  NEST	
  
	
  
University	
  of	
  the	
  Aegean	
  
Dealing	
  with	
  Seman@c	
  Heterogeneit...
Talk	
  Overview	
  
•  Part	
  I:	
  Large	
  Scale	
  Open	
  Environments	
  
•  Part	
  Ii:	
  ComputaKonal	
  Paradig...
About	
  Me	
  
•  PhD	
  in	
  Computer	
  Science	
  (NUI	
  
Galway)	
  
•  Green	
  and	
  Sustainable	
  IT	
  
Resea...
Overall Objective
WATERNOMICS will provide personalised and actionable
information about water consumption and water avail...
Project-­‐Sense	
  
Non-Technical Users
•  Targets Occupants of the
Building
•  Non-Technical Office
Workers
•  No experie...
7European Data Forum 2014 BIG 318062
BIG
Big Data Public Private Forum
7 BIG 318062
The BIG Project
BIG aims to promote a ...
@BYTE_EU www.byte-project.eu
Big  data  roadmap  and  cross-­‐
disciplinarY  community  for  
addressing  socieTal  Extern...
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	
 ...
From	
  Rigid	
  Schemas	
  to	
  Schema-­‐less	
  
13	
  
•  Heterogeneous,	
  complex	
  and	
  large-­‐scale	
  data	
 ...
Fundamental	
  DecentralizaKon	
  
14	
  
•  MulKple	
  perspecKves	
  (conceptualizaKons)	
  of	
  the	
  reality.	
  
• ...
Current	
  Trends	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Small	
  scale,	
  controlled	
  ...
COMPUTATIONAL	
  PARADIGMS	
  
PART	
  II	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
InformaKon	
  Flow	
  Processing	
  (IFP)	
  
•  Users	
  need	
  to	
  collect	
  informaKon	
  
– Produced	
  by	
  mulK...
InformaKon	
  Flow	
  Processing	
  (IFP)	
  
•  Processing	
  informaKon	
  as	
  it	
  flows	
  
– No	
  intermediate	
  ...
InformaKon	
  Flow	
  Processing	
  (IFP)	
  
•  Requirements	
  
– Real-­‐Kme	
  or	
  near	
  real-­‐Kme	
  processing	
...
ComputaKonal	
  Paradigm	
  
•  Event	
  Processing	
  
–  Event:	
  object	
  represenKng	
  a	
  happening.	
  
–  Deals...
•  Event	
  processing	
  agents,	
  network,	
  and	
  rules.	
  
Event	
  Processing	
  Architecture	
  
Producer	
  
Pr...
Events	
  Processing	
  is	
  Decoupled	
  
for	
  Scalability	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthB...
AcKve	
  Databases	
  
•  TradiKonal	
  database	
  systems	
  
–  Passive	
  
–  Store	
  data	
  and	
  wait	
  for	
  u...
AcKve	
  Databases	
  
•  ReacKve	
  behaviour	
  to	
  database	
  layer	
  
•  Event-­‐CondiKon-­‐AcKon	
  (ECA)	
  rule...
Data	
  Stream	
  Management	
  
Systems	
  
•  Streams	
  unbounded	
  (not	
  like	
  tables)	
  
•  No	
  arrival	
  or...
Data	
  Stream	
  Management	
  
Systems	
  
•  ConKnuous	
  queries	
  semanKcs	
  
– Answer:	
  append	
  only	
  stream...
Publish/Subscribe	
  	
  Systems	
  
•  InformaKon	
  items	
  are	
  no:fica:on	
  	
  
•  Indirect	
  addressing-­‐based	...
Publish/Subscribe	
  Systems	
  
•  One-­‐to-­‐many	
  and	
  many-­‐to-­‐many	
  distribuKon	
  mechanism	
  
–  allows	
...
Publish/Subscribe	
  	
  Systems	
  
•  Topic-­‐based	
  pub/sub	
  
–  Topics	
  are	
  groups	
  or	
  channels	
  
–  E...
Complex	
  Event	
  Processing	
  
Systems	
  
•  DetecKon	
  of	
  complex	
  paeerns	
  
– Sequencing	
  
– Causal	
  
–...
Complex	
  Event	
  Processing	
  
Systems	
  
	
  
Adapted	
  from	
  CUGOLA,	
  G.	
  AND	
  MARGARA,	
  A.,	
  2011.	
 ...
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...
EU ICT OpenIoT Project
Knowledge-Based Future Internet
Step 2:
Sensor/Cloud
Formulation
Step 1:
Sensing-as-a-Service
Reque...
Sensor Networks
•  OpenIoT leverages the
SoA on Internet of Things
(IoT) RFID/WSN
middleware frameworks.
•  OpenIoT provid...
SemanKc	
  Sensor	
  Networks	
  Ontology	
  
[JoWS 2012]
SSN	
  ApplicaKon:	
  SPITFIRE	
  	
  
• DUL: DOLCE+DnS Ultralite
• EventF: Event-Model F
• SSN: SSN-XG
• CC: Contextualis...
CQELS	
  
n  ConKnuous	
  Query	
  EvaluaKon	
  over	
  Linked	
  
Streams	
  
n  Scalable	
  processing	
  model	
  for...
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...
THEORY	
  OF	
  EVENT	
  EXCHANGE	
  
	
  
PART	
  IV	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
...
Problem	
  
•  Event	
  producers	
  and	
  consumers	
  are	
  semanKcally	
  coupled	
  
–  Consumers	
  need	
  prior	
...
Type	
   Energy
Consumption	
  
Place	
   Room 202e
Amount	
   40 kWh
Type	
   Electricity
Consumption	
  
Loca@on	
   Roo...
Type	
   Energy
Consumption	
  
Place	
   Room 202e
Amount	
   40 kWh
Type	
   Electricity
Consumption	
  
Loca@on	
   Roo...
How	
  Good	
  are	
  Our	
  Paradigms?	
  
•  Scale	
  
– Big	
  volume	
  
– Big	
  Velocity	
  
– Big	
  Variety	
  
• ...
Shannon-­‐Weaver	
  Model	
  
C.	
  Shannon	
  and	
  W.	
  Weaver.	
  The	
  mathemaKcal	
  theory	
  of	
  communicaKon....
Cross-­‐Boundaries	
  Exchange	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
	
  
	
  
	
  
	
  
...
SyntacKc	
  Boundary	
  
•  Transfer	
  is	
  the	
  most	
  common	
  type	
  of	
  
informaKon	
  movement	
  across	
  ...
SemanKc	
  Boundary	
  
•  Common	
  lexicon	
  doesn’t	
  exist	
  
•  Lexicon	
  evolve	
  
•  AmbiguiKes	
  exist	
  
•...
PragmaKc	
  Boundary	
  
•  Actors	
  on	
  the	
  sides	
  of	
  the	
  boundary	
  have:	
  
–  Different	
  contexts	
  ...
Cross-­‐Boundaries	
  Exchange	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
	
  
	
  
	
  
	
  
...
Transfer-­‐Translate-­‐Transform	
  
•  Current	
  approaches	
  in	
  event	
  processing	
  
•  Transfer	
  
–  Common	
...
Decoupling	
  for	
  Scalability	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Event	
  Processin...
SemanKc	
  Coupling	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Event	
  Processing	
  
Space	
...
APPROACHES	
  TO	
  SEMANTIC	
  COUPLING	
  
	
  
Part	
  V	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs...
Loosening	
  the	
  SemanKc	
  Coupling	
  
•  Approach	
  1:	
  Content-­‐Based	
  with	
  SemanKc	
  Decoupling	
  
–  A...
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	
...
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Approach	
  1:	
  Content-­‐Based	
  with	
  
SemanKc	
...
Approach	
  2:	
  Content-­‐Based	
  with	
  
Implicit	
  Shared	
  Agreements	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  G...
Approach	
  2:	
  Content-­‐Based	
  with	
  
Implicit	
  Shared	
  Agreements	
  
•  Implicit	
  semanKcs	
  
– Top-­‐dow...
Approach	
  2:	
  Content-­‐Based	
  with	
  
Implicit	
  Shared	
  Agreements	
  
•  Need	
  for	
  shared	
  agreements	...
Approach	
  3:	
  Concept-­‐Based	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Producer	
   Cons...
Approach	
  3:	
  Concept-­‐Based	
  
•  Explicit	
  semanKcs	
  
– Top-­‐down	
  approach	
  to	
  semanKcs	
  
– Granula...
Approach	
  3:	
  Concept-­‐Based	
  
•  Need	
  for	
  shared	
  agreements	
  
– Time	
  and	
  effort	
  
– Affects	
  sc...
•  Most	
  semanKc	
  models	
  have	
  dealt	
  with	
  parKcular	
  types	
  of	
  construcKons,	
  
and	
  have	
  been...
Distributional Semantic
Model
•  Distributional hypothesis: the context surrounding a given
word in a text provides releva...
DistribuKonal	
  SemanKc	
  Model	
  
c1
child
husband
spouse
cn
c2
function (number of times that the words occur in c1)
...
SemanKc	
  Relatedness	
  
70	
  
θ
c1
child
husband
spouse
cn
c2
Works as a semantic ranking function
E.g.	
  esa(room,	
...
Approach	
  4:	
  Loose	
  SemanKc	
  
Coupling	
  +	
  ApproximaKon	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   ...
Approach	
  4:	
  Loose	
  SemanKc	
  
Coupling	
  +	
  ApproximaKon	
  
•  Boeom-­‐up	
  model	
  of	
  semanKcs	
  
•  G...
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Approach	
  4:	
  Loose	
  SemanKc	
  
Coupling	
  +	
 ...
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Approach	
  5:	
  Theme-­‐Based	
  
•  Can	
  we	
  exc...
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Approach	
  5:	
  Theme-­‐Based	
  
Producer	
   Consum...
The	
  ThemaKc	
  Approach	
  
•  Exchange	
  approximaKons	
  of	
  meanings	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Gr...
Event	
  RepresentaKon	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Event	
  
energy,	
  applian...
SubscripKon	
  RepresentaKon	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Subscrip@on	
  
power,...
ProbabilisKc	
  Approximate	
  
Matcher	
  
•  Top-­‐1	
  and	
  Top-­‐k	
  mappings	
  between	
  an	
  event	
  
and	
  ...
Building	
  IoT	
  So]ware	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
  
Indexing	
  
Collector	
  
SemanKc	
  
rel...
Summary	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	
   EarthBiAs2014	
  
Simple	
  
Content-­‐
based	
  
Content-­‐
...
EvaluaKon	
  Dataset	
  
•  Seed	
  events	
  synthesized	
  from	
  IoT	
  sensors	
  
•  SmartSantander	
  smart	
  city...
EvaluaKon	
  Dataset	
  
•  Seed	
  events	
  synthesized	
  from	
  IoT	
  sensors	
  
•  Linked	
  Energy	
  Intelligenc...
EvaluaKon	
  
•  FScore	
  up	
  to	
  95%	
  and	
  1000s	
  events/sec	
  
7-­‐11	
  July	
  2014,	
  Rhodes,	
  Greece	...
EXAMPLE	
  APPLICATION:	
  	
  
LINKED	
  ENERGY	
  INTELLIGENCE	
  
	
  PART	
  VI	
  
7-­‐11	
  July	
  2014,	
  Rhodes,...
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 ...
Legacy	
  Building	
  
•  DERI	
  Building	
  
•  No	
  BMS	
  or	
  BEMS	
  
•  160	
  person	
  Office	
  space	
  
•  Caf...
Energy	
  Management	
  System	
  
Sensors	
  
90	
  of	
  26	
  
Energy	
  Management	
  So]ware	
  
HolisKc	
  Energy	
  ConsumpKon	
  
Holis@c	
  
Energy	
  
Management	
  
	
  	
  
	
  	
  
FaciliKes	
  
Business	
  Trav...
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
D...
Key	
  Challenges	
  
•  Technology	
  and	
  Data	
  Interoperability	
  
•  Data	
  scaeered	
  among	
  different	
  	
 ...
96	
  	
  
Building
Data Center
Office IT
Logistics
Corporate
Organisation-level
Business Process Personal-level
Linked	
 ...
Linked	
  Energy	
  Intelligence	
  Applications
Energy Analysis
Model
Complex Events
Situation Awareness
Apps
Energy and
...
Energy	
  Saving	
  ApplicaKons	
  
Enterprise Energy
Observatory
Smart Buildings Green Cloud
Computing
Office IT Energy M...
Building	
  Energy	
  Explorer	
  
99 of 26
1.  Data	
  from	
  
Enterprise	
  
Linked	
  Data	
  
Cloud	
  
2.  Sensor	
 ...
Energy	
  Analysis	
  by	
  Group	
  
iEnergy	
  –	
  Personal	
  	
  
@WATERNOMICS_EU www.waternomics.eu102
Concrete Objectives
•  To introduce demand response and accountability principles
(w...
@WATERNOMICS_EU www.waternomics.eu103
WATERNOMICS PLATFORM ARCHITECTURE
Support
Services
SourcesApplications
Water Analysi...
@WATERNOMICS_EU www.waternomics.eu104
PILOT OVERVIEW
# Focus Location Intent Partner
1
Water utility for
domestic users
(T...
Conclusions	
  
•  Coupling	
  necessary	
  for	
  crossing	
  boundaries	
  
•  Decoupling	
  necessary	
  for	
  scalabl...
Dataset	
  and	
  So]ware	
  
•  Dataset	
  
– Souleiman	
  Hasan,	
  Edward	
  Curry,	
  ThemaKc	
  event	
  
processing	...
References	
  
•  CUGOLA,	
  G.	
  AND	
  MARGARA,	
  A.,	
  2011.	
  Processing	
  flows	
  of	
  informaKon:	
  From	
  d...
More	
  References	
  
•  P.	
  McFedries,	
  The	
  coming	
  data	
  deluge,	
  IEEE	
  Spectrum,	
  2011.	
  
•  CUGOLA...
More	
  References	
  
•  David	
  S.	
  Rosenblum	
  and	
  Alexander	
  L.	
  Wolf.	
  1997.	
  A	
  design	
  framework...
Credits	
  
Green	
  and	
  Sustainable	
  IT	
  Group	
  at	
  Insight	
  Galway	
  
for	
  all	
  their	
  hard	
  work....
Dealing with Semantic Heterogeneity in Real-Time Information
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Dealing with Semantic Heterogeneity in Real-Time Information

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Tutorial at the EarthBiAs 2014 Summer School on Dealing with Semantic Heterogeneity in Real-Time Information

Part I: Large Scale Open Environments
Part Ii: Computational Paradigms
Part III: RDF Event Processing
Part IV: Theory of Event Exchange
Part V: Approaches to Semantic Decoupling
Part VI: Example Application: Linked Energy Intelligence



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Transcript of "Dealing with Semantic Heterogeneity in Real-Time Information"

  1. 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. 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. 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    
  4. 4. 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.
  5. 5. 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  
  6. 6. 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.
  7. 7. @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  
  8. 8. LARGE  SCALE  OPEN  ENVIRONMENTS   PART  I   7-­‐11  July  2014,  Rhodes,  Greece   EarthBiAs2014  
  9. 9. Emerging Environments… Smart  City  Energy   Smart  Building   Water  Management  
  10. 10. From  Internet  of  Things  to   Internet  of  Everything  
  11. 11. 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
  12. 12. 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  
  13. 13. Fundamental  DecentralizaKon   14   •  MulKple  perspecKves  (conceptualizaKons)  of  the  reality.   •  Ambiguity,  vagueness,  inconsistency.    
  14. 14. 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  
  15. 15. COMPUTATIONAL  PARADIGMS   PART  II   7-­‐11  July  2014,  Rhodes,  Greece   EarthBiAs2014  
  16. 16. 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
  17. 17. 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.  
  18. 18. 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  
  19. 19. 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)  
  20. 20. •  Event  processing  agents,  network,  and  rules.   Event  Processing  Architecture   Producer   Producer   E2   E3   E1   Rule   21  of  31   Event  Processing   Engine   Consumer  
  21. 21. 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.    
  22. 22. 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  
  23. 23. 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  
  24. 24. 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  
  25. 25. 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  
  26. 26. 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  
  27. 27. 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  
  28. 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  
  29. 29. 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  
  30. 30. 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  
  31. 31. RDF  EVENT  PROCESSING   PART  III   7-­‐11  July  2014,  Rhodes,  Greece   EarthBiAs2014  
  32. 32. 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.  
  33. 33. 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
  34. 34. 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
  35. 35. SemanKc  Sensor  Networks  Ontology   [JoWS 2012]
  36. 36. 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
  37. 37. 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]  
  38. 38. Linked  Stream  Middleware   [WWW 2009, JoWS 2012, CLOSER 2013] http://lsm.deri.ie/
  39. 39. LSM:  Live  train  info  
  40. 40. 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/
  41. 41. THEORY  OF  EVENT  EXCHANGE     PART  IV   7-­‐11  July  2014,  Rhodes,  Greece   EarthBiAs2014  
  42. 42. 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
  43. 43. 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  
  44. 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 e1 e1e2 e1e3 SemanKc   Event   Processing   Type =“Energy Consumption”~ Location =“Room 202e” Consumer SemanKc  Matching  
  45. 45. 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  
  46. 46. 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  
  47. 47. 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  
  48. 48. 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  
  49. 49. 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  
  50. 50. 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  
  51. 51. 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.  
  52. 52. 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  
  53. 53. 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.    
  54. 54. SemanKc  Coupling   7-­‐11  July  2014,  Rhodes,  Greece   EarthBiAs2014   Event  Processing   Space   Time   SynchronizaKon   Event   source   Event   consumer  SemanKc  Coupling   type,  aTributes,  values  
  55. 55. APPROACHES  TO  SEMANTIC  COUPLING     Part  V   7-­‐11  July  2014,  Rhodes,  Greece   EarthBiAs2014  
  56. 56. 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  
  57. 57. Current  Approaches   Semantic Decoupling Effectiveness & Efficiency Content-based Concept-based Bottom-up Semantics
  58. 58. 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  
  59. 59. 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  
  60. 60. 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          
  61. 61. 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  
  62. 62. 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  
  63. 63. 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  
  64. 64. 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  
  65. 65. 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  
  66. 66. •  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  
  67. 67. 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.     ...  
  68. 68. 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)  
  69. 69. 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)  
  70. 70. 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)  
  71. 71. 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   ~  
  72. 72. 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   ~  
  73. 73. 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)  
  74. 74. 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  
  75. 75. 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.  
  76. 76. 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  
  77. 77. SubscripKon  RepresentaKon   7-­‐11  July  2014,  Rhodes,  Greece   EarthBiAs2014   Subscrip@on   power,  computers   type=  increased  energy  usage  event~,   device~=  laptop~,     office=  room  112  
  78. 78. ProbabilisKc  Approximate   Matcher   •  Top-­‐1  and  Top-­‐k  mappings  between  an  event   and  a  subscripKon   7-­‐11  July  2014,  Rhodes,  Greece   EarthBiAs2014  
  79. 79. 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  
  80. 80. 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  
  81. 81. 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  
  82. 82. 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  
  83. 83. 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  
  84. 84. EXAMPLE  APPLICATION:     LINKED  ENERGY  INTELLIGENCE    PART  VI   7-­‐11  July  2014,  Rhodes,  Greece   EarthBiAs2014  
  85. 85. New  Smart  Building   86   Cost  -­‐  €  40,000,000      
  86. 86. 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      
  87. 87. 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
  88. 88. Energy  Management  System  
  89. 89. Sensors   90  of  26  
  90. 90. Energy  Management  So]ware  
  91. 91. HolisKc  Energy  ConsumpKon   Holis@c   Energy   Management           FaciliKes   Business  Travel  Data  Centre   Daily  Commute  Office  IT  
  92. 92. Business  Context  of  Energy   ConsumpKon   Resource Allocation Energy Finance Asset Mgmt Human Resources
  93. 93. 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
  94. 94. 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
  95. 95. 96     Building Data Center Office IT Logistics Corporate Organisation-level Business Process Personal-level Linked  dataspace  for   Energy  Intelligence   Linked  Energy  Intelligence  
  96. 96. 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.
  97. 97. Energy  Saving  ApplicaKons   Enterprise Energy Observatory Smart Buildings Green Cloud Computing Office IT Energy Mgmt. Personal Energy Mgmt.
  98. 98. Building  Energy  Explorer   99 of 26 1.  Data  from   Enterprise   Linked  Data   Cloud   2.  Sensor  Data   3.  Building   Energy   SituaKon   Awareness  
  99. 99. Energy  Analysis  by  Group  
  100. 100. iEnergy  –  Personal    
  101. 101. @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
  102. 102. @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
  103. 103. @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)
  104. 104. 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  
  105. 105. 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  
  106. 106. 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  
  107. 107. 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  
  108. 108. 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  
  109. 109. 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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