Harnessing the power of high performance computing to assess and ...
Transcript of Harnessing the power of high performance computing to assess and ...
Harnessing the power of high performance computing for creating sustainable, resilient,
and liveable cities
Maria-Cristina MarinescuBarcelona Supercomputing Center, Spain
Jorge Garcia VidalUniversitat Politecnica de Catalunya, Spain
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R & D Areas
We develop HPC software for science and engineeringWe do research in various areas: modeling, simulation, visualization
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R & D Areas
We develop HPC software for science and engineering
We do research in various areas: modeling, simulation, visualization
Integrate and analyze data
Simulate
Visualize
Urban processes and structures
Dicover patterns Compute metrics (liveability, resilience, contamination, etc) Understand the effect of changes/decisions beforehand Predict (timed) events offline and in real time
To plan, predict, react...
Data integration
Goal
● Urban Management and Planning– Data Integration from multiple data sources (errors, uncertainty, ..)
– Query facilities
– GIS Visualization
● Big Data Solutions if needed for performance
● Ecological Urbanism Paradigm– Environmental, social, and economic sustainability and resilience of cities
– Evaluated by specific indicators (KPI)
● Standard-based Semantic Data Model for Smart Cities
Barcelona Urban Ecology Agency
● Ecological Urbanism: degree to which a city adheres to the following principles
Efficientland useEfficientland use
Quality ofPublicSpace
MobilityAnd
Services
HabitabilityIn Housing
and Buildings
Biodiversity
Social Inclusion
and Interaction
Self-Sufficiency
Organization
City Semantics
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Urban City Model• Ontology to include urban models• Instantiation engine (Open Linked Data repositories)• SPARQL queries• Integration with openGIS
RDF graph
Ontology for smartcities (OWL-DL)
SPARQLquery
Data(RDF, XML, GML, JSON, etc)
$RDB
XML
SQL query
Xpath,Xquery
IoT
internet
Example Scenario
Ratio of health related incidents per medical center within given neighbourhood?
Relate Data
Integrate with GIS
Measure
Query and
Visualize
Technical Benefits
● Open World Concept– Valid data even if there are unknown features (e.g. we cannot know the
number of residents of a new building complex but cannot assume there are no residents) – semantically precise although incomplete
● Multi-classification– E.g. a street can be pedestrian or not, depending on the hour of the day
● Automatic inference– E.g. A public school is an educational facility, a facility, a public building,
a building, a geolocalizated structure, etc.
● Concept Browsing● Allows the access and the sharing of the City Data via Web● Eases data sharing between cities using the same city model● Uses a Standard Query Language (SPARQL)● GIS Capabilities
● Modeling relevant and shared city data● Why is this important?
– It simplifies the development of applications that require integrated access to city data sources (cross-domain)
– It enables solution reuse as we move from one city to the next
– It allows extending the metadata with new categories (Sanitation, Crime) without modifying the application or the data sources
● In terms of resilience, it facilitates:
– Data pattern discovery – prediction tool● E.g. Conditions under which an event triggers, correlations between monitored parameters
– Computing quality metrics – analysis tool
A Semantic Data Model for Smart Cities
Data integration and inference
E.g: Integrate transportation networks -metro, bus, train, …
Scenario 1: An accident on the Vittorio Emmanuele – Termini tramSolution: use existent connection via train through Tuscolana
Scenario 2: An accident on the Lepanto -Flaminio tramSolution: introduce temporary bus line
Scenario 3: explosion on metro tram that affect electricity networkIF additionally we integrate electricty network we know potentially affected neighbourhoods… where there may be a hospitalSolution: prepare generators for hospital
Simulation
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Atmospheric Simulations
Atmospheric Transport - FALL3D– Forecast of volcanic ash for air control
Atmospheric physics - modelling– Cloud formation
Atmospheric Transport at Microscale (CFD)
CFD Indoors/Outdoors
CFD Indoors/Outdoors
CFD Indoors/Outdoors
Water Flooding
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LIDAR Geometry example
City Geometry for Simulation
City Geometry for Simulation
Agent Based Simulation
Agent Based Models
Useful for – Social simulations– Crowd simulations– Mobility simulations (cars+pedestrian+…)– Epidemic evolution– . . .
But ABM scale bad in parallel computers, then there are serious limits in:– Agents complexity– Number of agents
Agent-Based Modeling
● Bottom-up approach based on the creation of a group of actors that interact inside a defined environment.
● Evolutionary approach
● Agent Heterogeneity
● Complex Behaviour
● Environment
Classical approaches to markets
● Physical-based Models– The actors of the system are forces, and the system looks for an
equilibrium
● Game Theory– The actors of the system are players that try to maximize their profits.
How can we model...
Heterogeneity? Behaviour? Realistic Scenarios?
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PANDORA
PANDORA: Scalable execution of agent-based models– Able to run models with millions of agents– C++ framework for large-scale social simulation– Automated generation of parallelized code– GIS support (Grass), Statistical support (R package)– Python scripting to define ABM– Cassandra: Cassandra is an analysis tool, implemented to interpret
results and detect patterns
Potential Applications
Smart cities
Financial Markets
Policy Analysis
Traffic Simulation
Prediction in dynamic environments
● Explore mixed numerical – ABM simulations for decision making in case of emergency
Understand social activities
Visualization
Pasive (predictive) big data analysis
Big Data Visualization
Big Data Visualization
Data visualization for crisis management
• Data dashboards: Clarity and recognition speed are main goal• Intuitive displays needed to communicate maximum of relevant
information as immediately as possible (it is an operational, NOT an analytical tool)
• Heterogeneous team of data designers and interface experts• We are developing technology for innovative and collaborative
interfaces
Active data visualization and processing
• Use modern means of communication for:• Fast and cheap one way communication (govt to
citizens)• Empower citizens for massive feedback
• Example: Blackouts during hurricane Sandy• Integrate power visualization with online big data sources
Massive data collection, visualization and processing
• Measure citizen response to events (even in real time)• Example: “Happiness” can be extracted from social network
geolocated activity
Questions?
Digital
Data
Model
Geo Data
Semantic
City Data
Data Integration Different formatsHidden semantic relationships
Relate Data
Integrate with GIS
Data preprocessing
Map data to modelSpecialize model
for new city
SemanticCity Data
DataProcessi
ng
QueryTechnol
ogy
Graphical
Interfaces
Data Processing and Visualization
Quality metrics
Average distance to recycling points per habitant
Green zones per habitant and block
Measure
Query and Visualize
An ontology is …
Conceptually a graph– Precise semantics– Standard representation to allow interoperability (triples/ Linked
Open Data)• Same URI means same resource
– Models an open world, i.e. allows incomplete information• Adding edges / vertices is easier than adding information into tables
(which may require refactoring)
– Understandable and uniformly accessible from anywhere
Amenable data structure for– Defining data constraints– Discovering unknown relationships via logic inference
Integration of Open Linked Data from any source
Composition of RDF graphs is another RDF graph
SCRIBE● Open Source model developed by IBM
● Non-normative, authoritative, modular, extensible semantic model for Smarter Cities
● Consist of a Core Model that includes common classes (events and messages, stakeholders, departments, services, city landmarks and resources, KPIs, etc.)
Simple language Based on standards (OWL-QL, SPARQL) from the W3C Metadata annotations and Tagging
Authoritative Aligned with standards (CAP, NIEM, MISA/MRM, UCore) Validated with customer scenarios Validated with open city data
City Data Analysis and Planning Tool
http://...Selected ElementsProperty: year-built
BuildingRoad…
196519701920…
Textual query
Numerical Result
BuildingYear-built: 1965Year-remodeled: 1970Area: 250m2…
Road Green Buildings: match the condition
Building data
Buildings
Result: 6,3m.Avg (height of Building with (year-built > 1960) OR (year-remodeled > 1980))
Search
Conditional query: Avg Building height
year-built > 1960 ORyear-remodeled > 1980..
Monitor
Discovery
Planning & new quality metrics
• Changes in mobility network
• Best places to create business
• Neighbourhoods with insufficient green areas
• Conflictive neighbourhoods
• Violence indicators
• Pollution levels
User Benefits
A Semantic Data Model as an Ontology
● Semantic network of Concepts– Model: Class + Relations + Constraints
– Knowledge base: Model + Instances
– InferenceAirport Address
Calle 26, nº103-109Bogotá, ColombiaEl Dorado
Geo
Facility
04-43N, 074-09W
Domain:FacilityRange: Address
Domain:FacilityRange: Geo
is-a
typetype type
Inferredtype
hasGeopositionhasAddress
An Agent-Based market
● “the agent-based method can provide an unprecedented understanding of the emergent properties of interacting parts in complex circumstances”
Farmer & Foley, The economy needs agent-based modelling, Nature, 2009)
● Simple modeling of complex emergent properties:● Self-fulfilling expectations● Asymmetric information● Non-equilibrium situations
● We would be able to evaluate different scenarios...● ...what if we introduce a new competitor?● ...what if a regulatory agency is created?