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Semantic integration at large scale:
benefits and challenges
Maria-Cristina Marinescu
Barcelona Supercomputing Center
What is a Smart City?
Smart Cities
SUSTAINABLE
GREEN
SECURE
LIVEABLE
SELF-SUFFICIENT
RESILIENT
EQUITABLE
Smart Cities
SUSTAINABLE
GREEN
SECURE
LIVEABLE
SELF-SUFFICIENT
RESILIENT
EQUITABLE
Right?
Smart Cities?
BETTER, FASTER MAIN AVENUE
… may split neighbourhoods
NEW HOUSING IN PREVIOUSLY RAN DOWN NEIGHBOURHOOD
… housing may become unaffordable for previous neighbours
… neighbourhoods loose identity, friends/family move apart
… existing local issues spread globally (crime, STDs – Baltimore
housing projects, ...)
CITY FOCUSES ON LOCAL PRODUCE /
RURAL REGION SPECIALIZES IN FEW HIGHLY DEMANDED PRODUCTS
… draught or plague may hit a crop → there`s no backup plan for rural region,
and delayed response in city
TRAM TO AVOID TRAFFIC JAMS
AND RUN DIRECTLY
… may collapse car /public transport.
traffic that runs in different direction
WALKABLE CITIES
… commercial traffic worsens,
no place to stop
… less parking space for neighbours
… possibly in places where people
don`t usually walk! (e.g. steep hills)
CHANGING / DAMMING WATER
COURSE FOR CITIES
… ecosystem changes
… displaced people
Smart(er) Cities
BETTER, FASTER MAIN AVENUE
… may split neighbourhoods
NEW HOUSING IN PREVIOUSLY RAN DOWN NEIGHBOURHOOD
… housing may become unaffordable for previous neighbours
… neighbourhoods loose identity, friends/family move apart
… existing local issues spread globally (crime, STDs, ...)
CITY FOCUSES ON LOCAL PRODUCE /
RURAL REGION SPECIALIZES IN FEW HIGHLY DEMANDED PRODUCTS
… draught or plague may hit a crop → there`s no backup plan for rural region,
and delayed response in city
TRAM TO AVOID TRAFFIC JAMS
AND RUN DIRECTLY
… may collapse car /public transport.
traffic that runs in different direction
WALKABLE CITIES
… commercial traffic worsens,
no place to stop
… less parking space for neighbours
… possibly in places where people
don`t usually walk! (e.g. steep hills)
CHANGING / DAMMING WATER
COURSE FOR CITIES
… ecosystem changes
… displaced people
Define goals
Define model that allows
- integration of data
- measuring the current status
Capture interconnections between domains (and goals)
Massive city data integration
 Not only in volume, but more importantly, in number of
heterogeneous data sources!
 Structured, semi-structured, raw data… in different formats
 Traditional approach:
1. Manually identify relevant data and map it to a schema
 Problem: schema may not support actual data, needs
redesign!
2. Consult, analyze, display data
 Problem: if schema changes,
apps don´t work any longer!
Green&Sport
Area
Number of Trees
Coordinates
(format1)
Status
Area
Id
Adjacent to
Coordinates
(format2)
Surface
Green Area
Id
Number of Trees
Sport Area
Id
Sport Material
Alternatives?
 ETL (Extract Transform Load) has some
problems:
 Don´t scale as well as technologies that access
data without moving it around
 Too rigid for ad-hoc data exploration, sparse data,
implicit information
 Semantic technologies
 Explicit relationships
 Richer, more general model (domain rather than application
specific)
Association
Is a
Semantic integration
Area
Coordinates
Surface
Green AreaSport Area
Is a Is a
Has Coordinates
Has Surface
Adjacent to
Numeric
Has Sport Material
Numeric
Has Trees
Semantic integration
Examples:
 ‘Adjacent to’ not only a field
name in table ‘Area’, but a
concept in itself, with labels,
relationships, synonyms,
superconcepts, subconcepts, etc.
 ‘Adjacent to’ transitive
 Newly inferred relationships:
Sport Area has surface
Reflexive
Transitive
Road
Adjacent to
Traffic
volume
Contamination
Has
Measured by
Area
Coordinates
Surface
Green AreaSport Area
Is a Is a
Has Coordinates
Has Surface
Adjacent to
Numeric
Has Sport Material
Numeric
Has Trees
Association
Is a
 Explicit relationships
 Richer, more general model (domain rather than application
specific)
 Easier to understand, browse, and search
 Easier to integrate cross-domain information
Semantic Approach. Part III
Area
Coordinates
Surface
Green Area
Semantic integration
Green & Sport Area inherits from both
sport area and green area:
 No data duplication
 If no data for some attribute (e.g. sport material),
no need to leave empty slot (i.e. no assumptions
are done about non-existing data)
 Allows different formats at the same time
(e.g. coordinates)
Area
Coordinates
Surface
Green AreaSport Area
Numeric Numeric
Green & Sport
Area
Is aIs a
 Semi-automatic mapping is easier because at least one of the
models is richer; can be done (collaboratively) via Web
 Extend rather than change the model
 Supports Open World – incomplete and evolving data
Semantic Approach. Part V
 Consult, analyze, display
 Reduce app re-engineering (e.g. Green &
Sport Areas are implicity included as types
of Areas)
 Semantic access without modifying the apps
– mapping to different data via the same model
Display areas on map, show
attributes and related concepts,
query data and model
Semantic integration
Barcelona schema Quito schema
MAP MAP
Is aIs a
Area
Green AreaSport Area
Is a
(inference)
Software
Schema
Data
Is a Is a
Green & Sport
Area
Semantic Approach. Part VI
 Data accessible unambiguously (each concept = URI) and directly
via Web
 Open Linked Data: publish, share, reuse, import (same format!)
 Link with concepts from other semantic models!
Area
Semantic integration
Geo-
spatial
Measures
Indicators
Time
Area
Coordinates
Surface
Semantic Models Issues & Solutions
 Bad design of the semantic model could affect its extensibility
 A good ontology is domain specific, but depends on HOW it will
be used (application goals), based on standards when possible
 Complex models to browse and search
 The more domain specific, the easier to use (but less reusable)
 Integration of open data, evolving ontologies, population at
large scale
 Entities and relationships may evolve over time or between
cities
 Data of new types – help from mapping tool and exploration /
navigation tools
 Data not precise, inconsistent, or uncertain
 Data curation and consistency
 Probabilistic data
Challenges
Semantic Models Issues & Solutions
 Lack of support for geo-spatial information
 Needs huge storage capacity
 Integration with maps
 Querying easier than in general purpose GIS or general
purpose semantic platforms
 Specific platforms/algorithms to speed up the queries
 Data not available at requested spatial granularity – domain
experts apply different approximation methods depending
on data type
 Query scalability
 Trade-off between expressiveness and computational costs
 Reasons: reasoning at runtime, no generally applicable
efficient indexing schemas, graph-based querying, etc
Challenges (cont´d)
Questions?
 Analyzing integrated data may uncover hidden correlations
 Usually requires extensive monitoring
Discover, predict, plan, react
 Neighbourhoods with insufficient
green areas, etc...
 Conflictive neighbourhoods
 Areas where assaults mostly happen
Discover
Discover, predict, plan, react
 Neighbourhoods with insufficient
green areas, etc...
 Conflictive neighbourhoods
 Areas where assaults mostly
happen
Planning
green areas
 Analyzing integrated data may uncover hidden correlations
 Usually requires extensive monitoring
Discover
... less racially mixed
… have low density of
population in the streets
… at night when
(1) single person waiting
(2) bus stop hidden from
camera view
(3) in tourist neighborhood
...etc
… different behavior may
be suspicious
Discover, predict, plan, react
 Analyzing integrated data may uncover hidden correlations
 Usually requires extensive monitoring
 Neighbourhoods with insufficient
green areas, etc...
 Conflictive neighbourhoods
 Areas where assaults mostly
happen
Planning
green areas
Predict
Discover
... less racially mixed
… have low density of
population in the streets
Predict
… at night when
(1) single person waiting
(2) bus stop hidden from
camera view
(3) in tourist neighborhood
...etc
… different behavior may
be suspicious
Long-term
demographic mix,
preventive security
Discover, predict, plan, react
 Neighbourhoods with insufficient
green areas, etc...
 Conflictive neighbourhoods
 Areas where assaults mostly
happen
Planning
green areas
 Analyzing integrated data may uncover hidden correlations
 Usually requires extensive monitoring
Discover
... less racially mixed
… have low density of
population in the streets
Predict
… at night when
(1) single person waiting
(2) bus stop hidden from
camera view
(3) in tourist neighborhood
...etc
… different behavior may
be suspicious
Long-term
demographic mix,
preventive security
Discover, predict, plan, react
 Neighbourhoods with insufficient
green areas, etc...
 Conflictive neighbourhoods
 Areas where assaults mostly
happen
Ensure safety,
more business,
better social
gather places
Planning
green areas
 Analyzing integrated data may uncover hidden correlations
 Usually requires extensive monitoring
Discover
... less racially mixed
… have low density of
population in the streets
Predict
… at night when
(1) single person waiting
(2) bus stop hidden from
camera view
(3) in tourist neighborhood
...etc
… different behavior may
be suspicious
Potential
safety alarm
Long-term
demographic mix,
preventive security
Discover, predict, plan, react
 Analyzing integrated data may uncover hidden correlations
 Usually requires extensive monitoring
 Neighbourhoods with insufficient
green areas, etc...
 Conflictive neighbourhoods
 Areas where assaults mostly
happen
Planning
green areas
Ensure safety,
more business,
better social
gather places
Discover
... less racially mixed
… have low density of
population in the streets
Predict
… at night when
(1) single person waiting
(2) bus stop hidden from
camera view
(3) in tourist neighborhood
...etc
… different behavior may
be suspicious
Potential
safety alarm
Long-term
demographic mix,
preventive security
Potential
safety alarm
Discover, predict, plan, react
 Analyzing integrated data may uncover hidden correlations
 Usually requires extensive monitoring
 Neighbourhoods with insufficient
green areas, etc...
 Conflictive neighbourhoods
 Areas where assaults mostly
happen
Ensure safety,
more business,
better social
gather places
Planning
green areas
Discover
 How should smart infrastructure be integrated
to make a Smart City truly smart?
 What does this mean for the organization of our
city governments?
 How do we find guidelines to understand the
architecture of integrated infrastructures?

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Semantic Integration at large scale

  • 1. Semantic integration at large scale: benefits and challenges Maria-Cristina Marinescu Barcelona Supercomputing Center
  • 2. What is a Smart City?
  • 5. Smart Cities? BETTER, FASTER MAIN AVENUE … may split neighbourhoods NEW HOUSING IN PREVIOUSLY RAN DOWN NEIGHBOURHOOD … housing may become unaffordable for previous neighbours … neighbourhoods loose identity, friends/family move apart … existing local issues spread globally (crime, STDs – Baltimore housing projects, ...) CITY FOCUSES ON LOCAL PRODUCE / RURAL REGION SPECIALIZES IN FEW HIGHLY DEMANDED PRODUCTS … draught or plague may hit a crop → there`s no backup plan for rural region, and delayed response in city TRAM TO AVOID TRAFFIC JAMS AND RUN DIRECTLY … may collapse car /public transport. traffic that runs in different direction WALKABLE CITIES … commercial traffic worsens, no place to stop … less parking space for neighbours … possibly in places where people don`t usually walk! (e.g. steep hills) CHANGING / DAMMING WATER COURSE FOR CITIES … ecosystem changes … displaced people
  • 6. Smart(er) Cities BETTER, FASTER MAIN AVENUE … may split neighbourhoods NEW HOUSING IN PREVIOUSLY RAN DOWN NEIGHBOURHOOD … housing may become unaffordable for previous neighbours … neighbourhoods loose identity, friends/family move apart … existing local issues spread globally (crime, STDs, ...) CITY FOCUSES ON LOCAL PRODUCE / RURAL REGION SPECIALIZES IN FEW HIGHLY DEMANDED PRODUCTS … draught or plague may hit a crop → there`s no backup plan for rural region, and delayed response in city TRAM TO AVOID TRAFFIC JAMS AND RUN DIRECTLY … may collapse car /public transport. traffic that runs in different direction WALKABLE CITIES … commercial traffic worsens, no place to stop … less parking space for neighbours … possibly in places where people don`t usually walk! (e.g. steep hills) CHANGING / DAMMING WATER COURSE FOR CITIES … ecosystem changes … displaced people Define goals Define model that allows - integration of data - measuring the current status Capture interconnections between domains (and goals)
  • 7. Massive city data integration  Not only in volume, but more importantly, in number of heterogeneous data sources!  Structured, semi-structured, raw data… in different formats  Traditional approach: 1. Manually identify relevant data and map it to a schema  Problem: schema may not support actual data, needs redesign! 2. Consult, analyze, display data  Problem: if schema changes, apps don´t work any longer! Green&Sport Area Number of Trees Coordinates (format1) Status Area Id Adjacent to Coordinates (format2) Surface Green Area Id Number of Trees Sport Area Id Sport Material
  • 8. Alternatives?  ETL (Extract Transform Load) has some problems:  Don´t scale as well as technologies that access data without moving it around  Too rigid for ad-hoc data exploration, sparse data, implicit information  Semantic technologies
  • 9.  Explicit relationships  Richer, more general model (domain rather than application specific) Association Is a Semantic integration Area Coordinates Surface Green AreaSport Area Is a Is a Has Coordinates Has Surface Adjacent to Numeric Has Sport Material Numeric Has Trees
  • 10. Semantic integration Examples:  ‘Adjacent to’ not only a field name in table ‘Area’, but a concept in itself, with labels, relationships, synonyms, superconcepts, subconcepts, etc.  ‘Adjacent to’ transitive  Newly inferred relationships: Sport Area has surface Reflexive Transitive Road Adjacent to Traffic volume Contamination Has Measured by Area Coordinates Surface Green AreaSport Area Is a Is a Has Coordinates Has Surface Adjacent to Numeric Has Sport Material Numeric Has Trees Association Is a  Explicit relationships  Richer, more general model (domain rather than application specific)  Easier to understand, browse, and search  Easier to integrate cross-domain information
  • 11. Semantic Approach. Part III Area Coordinates Surface Green Area Semantic integration Green & Sport Area inherits from both sport area and green area:  No data duplication  If no data for some attribute (e.g. sport material), no need to leave empty slot (i.e. no assumptions are done about non-existing data)  Allows different formats at the same time (e.g. coordinates) Area Coordinates Surface Green AreaSport Area Numeric Numeric Green & Sport Area Is aIs a  Semi-automatic mapping is easier because at least one of the models is richer; can be done (collaboratively) via Web  Extend rather than change the model  Supports Open World – incomplete and evolving data
  • 12. Semantic Approach. Part V  Consult, analyze, display  Reduce app re-engineering (e.g. Green & Sport Areas are implicity included as types of Areas)  Semantic access without modifying the apps – mapping to different data via the same model Display areas on map, show attributes and related concepts, query data and model Semantic integration Barcelona schema Quito schema MAP MAP Is aIs a Area Green AreaSport Area Is a (inference) Software Schema Data Is a Is a Green & Sport Area
  • 13. Semantic Approach. Part VI  Data accessible unambiguously (each concept = URI) and directly via Web  Open Linked Data: publish, share, reuse, import (same format!)  Link with concepts from other semantic models! Area Semantic integration Geo- spatial Measures Indicators Time Area Coordinates Surface
  • 14. Semantic Models Issues & Solutions  Bad design of the semantic model could affect its extensibility  A good ontology is domain specific, but depends on HOW it will be used (application goals), based on standards when possible  Complex models to browse and search  The more domain specific, the easier to use (but less reusable)  Integration of open data, evolving ontologies, population at large scale  Entities and relationships may evolve over time or between cities  Data of new types – help from mapping tool and exploration / navigation tools  Data not precise, inconsistent, or uncertain  Data curation and consistency  Probabilistic data Challenges
  • 15. Semantic Models Issues & Solutions  Lack of support for geo-spatial information  Needs huge storage capacity  Integration with maps  Querying easier than in general purpose GIS or general purpose semantic platforms  Specific platforms/algorithms to speed up the queries  Data not available at requested spatial granularity – domain experts apply different approximation methods depending on data type  Query scalability  Trade-off between expressiveness and computational costs  Reasons: reasoning at runtime, no generally applicable efficient indexing schemas, graph-based querying, etc Challenges (cont´d)
  • 17.  Analyzing integrated data may uncover hidden correlations  Usually requires extensive monitoring Discover, predict, plan, react  Neighbourhoods with insufficient green areas, etc...  Conflictive neighbourhoods  Areas where assaults mostly happen Discover
  • 18. Discover, predict, plan, react  Neighbourhoods with insufficient green areas, etc...  Conflictive neighbourhoods  Areas where assaults mostly happen Planning green areas  Analyzing integrated data may uncover hidden correlations  Usually requires extensive monitoring Discover
  • 19. ... less racially mixed … have low density of population in the streets … at night when (1) single person waiting (2) bus stop hidden from camera view (3) in tourist neighborhood ...etc … different behavior may be suspicious Discover, predict, plan, react  Analyzing integrated data may uncover hidden correlations  Usually requires extensive monitoring  Neighbourhoods with insufficient green areas, etc...  Conflictive neighbourhoods  Areas where assaults mostly happen Planning green areas Predict Discover
  • 20. ... less racially mixed … have low density of population in the streets Predict … at night when (1) single person waiting (2) bus stop hidden from camera view (3) in tourist neighborhood ...etc … different behavior may be suspicious Long-term demographic mix, preventive security Discover, predict, plan, react  Neighbourhoods with insufficient green areas, etc...  Conflictive neighbourhoods  Areas where assaults mostly happen Planning green areas  Analyzing integrated data may uncover hidden correlations  Usually requires extensive monitoring Discover
  • 21. ... less racially mixed … have low density of population in the streets Predict … at night when (1) single person waiting (2) bus stop hidden from camera view (3) in tourist neighborhood ...etc … different behavior may be suspicious Long-term demographic mix, preventive security Discover, predict, plan, react  Neighbourhoods with insufficient green areas, etc...  Conflictive neighbourhoods  Areas where assaults mostly happen Ensure safety, more business, better social gather places Planning green areas  Analyzing integrated data may uncover hidden correlations  Usually requires extensive monitoring Discover
  • 22. ... less racially mixed … have low density of population in the streets Predict … at night when (1) single person waiting (2) bus stop hidden from camera view (3) in tourist neighborhood ...etc … different behavior may be suspicious Potential safety alarm Long-term demographic mix, preventive security Discover, predict, plan, react  Analyzing integrated data may uncover hidden correlations  Usually requires extensive monitoring  Neighbourhoods with insufficient green areas, etc...  Conflictive neighbourhoods  Areas where assaults mostly happen Planning green areas Ensure safety, more business, better social gather places Discover
  • 23. ... less racially mixed … have low density of population in the streets Predict … at night when (1) single person waiting (2) bus stop hidden from camera view (3) in tourist neighborhood ...etc … different behavior may be suspicious Potential safety alarm Long-term demographic mix, preventive security Potential safety alarm Discover, predict, plan, react  Analyzing integrated data may uncover hidden correlations  Usually requires extensive monitoring  Neighbourhoods with insufficient green areas, etc...  Conflictive neighbourhoods  Areas where assaults mostly happen Ensure safety, more business, better social gather places Planning green areas Discover
  • 24.  How should smart infrastructure be integrated to make a Smart City truly smart?  What does this mean for the organization of our city governments?  How do we find guidelines to understand the architecture of integrated infrastructures?