bi g data_urban modeling_applications_23092013
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Urban Modeling Using Big Data
Vahid Moosavi
Researcher at Future Cities Laboratory
PhD Student at Chair for Computer Aided Architectural Design (CAAD), ETH Zurich, Ludger Hovestadt
23 September 2013
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Outline
• Our understanding of Big Data and its applications
• My research interests related to data driven modeling
• Possible Collaborations
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Big Data Landscape and My Research Interests (How to go beyond simple data analytics toward a new data literacy)
Data-Infrastructures
Data Management and Data Processing
Complex Event Processing
Data-Driven Modeling Technologies
Clustering and Grouping
Signal Processing /Time Series Forecasting Prediction and Classification
Information Visualization
Data Management
• My research focus areas with red color
Practical Application Domains
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Self Organizing Map (SOM) : A generic setup, playing with symbolic indexes
• SOM acts as a multidimensional sorting system
Informational Space
Physical Space (Imaginary)
Teuvo Kohonen
Somehow similar to the idea of Cartesian Dualism
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SOM as a social Machine
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Specific Modeling with a specific curve Pre-specific modeling with any potential curve
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SOM: Formal products
Then, we can do any kind of prediction/classification, pattern recognition, time series analysis
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Some projects…
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Case 1: Pre-Specific City Modeling
Footprint of buildings in Orchard area, Singapore
Similar buildings are in the same area of SOM
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Matthias Standfest, Vahid Moosavi , Design Modelling Symposium Berlin 2013
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Case 2: Data Driven Modeling of a Manufacturing Process
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Designed Experiments
(Data Farming)
A practical look up table for the operator
How to optimize the process variables for different product specs?
Saraee, Mohamad, Vahid Moosavi, and Shabnam Rezapour. "Application of Self Organizing Map (SOM) to model a machining process."Journal of Manufacturing Technology Management 22.6 (2011): 818-830.
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Case 3: Data Driven Modeling of Urban Air Quality (Rochor Area, Singapore)
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Building Structural Features
Land Use Features
Urban Network Features
Different Pollutants
241 measurement points in the city More than 3000 points without measurements
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Case 3: Data Driven Modeling of Urban Air Quality (Rochor Area, Singapore)
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Finding the main Clusters of similar points
Center of the clusters as the potential measurement stations
Predicting the pollution level without any direct measurement
241 measurement points More than 3000 points without measurements
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Some of the Potential Applications for collaboration
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1- A Generic Setup for Text Modeling in Association With Other Data Streams (How to gain insight from lots of low-economic value data?)
Search APIs
Text Streams
Text Modeling
Quantitative Data
movement traces Stock Market
Smart Grid
Real estate
SOM
Text Processing
Markov Chain
Sentiment Analysis
Pattern recognition
Prediction and classification
Decision Making
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Enterprise Knowledge Modeling Using Text Archives (As a potential software solution)
1. Using Historical reports through digital archives
2. Finding the main (similar) knowledge pools in the organizations
3. Modeling the strategic trends and strategic focus of the company
4. Finding the informal (hidden) networks of potential collaborations between human resources
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Urban Energy Modeling (Singapore Case)
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Energy Consumptions
Building Information
Design
Land Use
History
Smart Meter records
Gas and Electricity
Urban Information
Land use
Weather
Demand Forecasting
Customer Segmentations
Analytics
Just as a sample
Future Steps (Smart Grid Applications)
• Load Forecasting • Theft Detection • Dynamic Pricing • Distribution
Optimization • And Capacity Planning
Household Demographics
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Conclusion
• Some applications of data driven modeling
• Potential collaborations
– A generic text modeling framework : Enterprise Knowledge Modeling
– Urban Energy Modeling
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Thanks!