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Visual Analytics on Multidimensional Big Data
Dorian GorganComputer Science Department
Technical University of Cluj-Napocahttp://users.utcluj.ro/~gorgan
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Contents
Big Data Data to Information Viasual Analytics Multidimensional Space Computer Game Strategies Hydrological SWAT model calibration Sociophysics Models Publications
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Big Data
Huge data globally available 2012, 2.8x1021 Bytes (2.8 ZB) at global level, 10 x 2007 2020, 40 ZB (~14 x 2012)
3% marked/annotated, 0.5% analyzed Big Data – volume, variety, velocity, variability, veridity Earth Observation Data (EO Data) High costs of data management Increase data value by using instead of just storing Data -> knowledge -> information High Performance Computation resources + Analytics
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Data to Information
Just storing data is a trivial, useless and ineffective solution
How to reduce the data to only those that are useful?
How to know what is useful? What algorithms are able to
highlight information? How to process and
understand raw data?
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Computation capacity of machine
+
Analyze and synthesize capacity of human
Information
Data
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Data to Information
Visual channel is the main interface to the human brain
Visual presentation rather than numerical information
Dynamically rather than statically
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Computation capacity of machine
+
Analyze and synthesize capacity of human
Information
Data
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Visual Analytics
Definition: Visual analytics is the analytical reasoning by user interactive
visualization and navigation within value spaces
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Multidimensional Space
Model M(P, F, S, C) P - Basic parameters
Coordinate system (p1, p2, …, pn)
F – Functions f1(p1, p2, …, pn), g = f1of2, …
S – set of model instances (states) C- set of conditions for transition within S
Model visualization1. Mapping of the model M onto k-dimensional space
e.g. k=4, axes: age, weight, happiness, satisfaction
2. Mapping of the k-dimensional space onto the screene.g. k=4, axes: x(age), y(weight), z(happiness), color(satisfaction)
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L*a*b Color Value Space
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Navigation in L*a*b Color Value Space
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Computer Game Strategies
Casual Games: little mental involvement high motor skills purpose: entertainment
Strategy Games: make decisions develop strategy purpose: mental challenge
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Problem and Motivation
Lack of specialized software for analysis of strategies in games Multi-criteria optimization No aid to the player in understanding the game Professional players have to rack up hours of gameplay in order to
be successful
Lack of software tools to help game designers build engaging videogames Research shows importance of game balancing and the ties
between user engagement and game difficulty
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Solution
Computers have great processing power Humans have great analytical power and high familiarity
with the subject Develop software tools that take advantage of both
How? Games can be modeled as n-dimensional systems Computers can analyze a restrained domain of the system Humans can analyze through visual representations the results and
guide the computer for further searches
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Case Study: Tower Defense Game
Tower Defense: Build turrets Stop computer
units
Strategy: Resource
management
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Formal Architecture
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Game Breakdown
Waves of enemies Only know information about
current wave Need to find out all waves before
generating strategy
Basic computation: Local Optimum – quickly find out
waves info Global Strategy – combine locals
for an overall better solution
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Strategy Analysis
Fixed:
Game_World {
Map,
Path
}
Computer_Unit {
Health,
Speed,
Damage
}
Variable:Turret {
Position,Range,Damage,Speed
}
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Strategy Evaluation
Metrics: Player health at the end of the game Resources left unspent Difference between total enemy health and total damage given
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Parameters and Strategy Values
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Visualization Technique
Represent n-dimensional system as multiple 2D projections Coloring scheme:
represent strategy analysis through colors
Projection: 2 axes with sample values 1 slider with varying parameter
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Game World
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Navigation within Value Space
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Navigation within Value Space
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Game Strategy Tool
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Strategy Validation
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EnviroGRIDS Project
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enviroGRIDS - Gridifying the Black Sea catchment to support its sustainable development (http://www.envirogrids.net)
Founded by the European Commission FP7, 2009-2013, 30 partners, 7.9 mil EUR.
Coordinator: University of Geneva, Switzerland
Objectives:
Develop a SDI (Spatial Data Infrastructure) targeting the Black Sea catchment region
E.g. hydrological models, satellite images, and maps
Perform distributed spatially-explicit simulations of environmental changes
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SWAT Model Overview
SWAT (Soil Water Assessment Tool) hydrological model operates on a daily time step used for predicting the water
resources, sediment, and chemical yields in a specific watershed
Input data: weather, soil properties, topography, vegetation, and land management practices of the watershed
SWAT estimates the impact of land management practices on water quantity and quality in complex watersheds
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SWAT Hydrological Model Calibration
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SWAT Model Calibration
SWAT model must pass through a careful calibration and uncertainty analysis
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Model Calibration – one iteration
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Calibration parameters:r__CN2.mgt -0.168640 -0.048970
v__ALPHA_BF.gw 0.326250 0.792908
v__GW_DELAY.gw 326.361267 456.747559
v__GWQMN.gw 1.372719 1.814273
v__GW_REVAP.gw 0.068777 0.130139
v__ESCO.hru 0.826673 0.898115
v__CH_N2.rte 0.109526 0.211008
v__CH_K2.rte 49.320133 120.924545
v__ALPHA_BNK.rte -0.116990 0.257143
r__SOL_AWC(1).sol -0.234486 0.026237
r__SOL_K(1).sol 0.989069 2.104857
r__SOL_BD(1).sol 0.523180 0.947732
v__SFTMP.bsn 2.944960 6.109218
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gSWAT – SWAT model calibration
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Complex SWAT models
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Black Sea basin 200 simulations Local sequential execution: 8,059 hours Distributed execution: ~40h Basin scale: 2 milioane km2 Nr. of files: ~1.300.000
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Sociophysics Models
Social model Economic system Demographic evolution Financial system Human relationships Resources oriented concurrency Business relationships . . .
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High Performance Computation
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GPU ClusterGPGPU
Particle based modeling
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Computation Architecture Overview
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Particle Model Partitioning
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GPU levela) Static 2D cyclic partitioning
b) Communication and synchronization
c) Simulation algorithm
a)
b)c)
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Particle Model Partitioning
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Cluster levela) Static 2D block-all partitioning
b) Border particles
a)
b)
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Particle Model Partitioning
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Cluster level Extended model
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Parallel and Distributed Computation
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ParTSim Architecture
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Model Execution
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Crowd Dynamics in Sociophysics
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Agent
Obstacle
Agent
Agent
Attraction force
Repultion force
Repultion force
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Crowd Dynamics in Sociophysics
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Publications
Ilies D.R., Sabou A., Gorgan D., “Real Time Visualization of Crowd Dynamics Scenarios”, RoCHI Conference (in press), (2015)
Catana M.C., Gorgan D., “Analyzing Computer Game Strategies through Visual Techniques”, RoCHI Conference (in press), (2015)
Sabou A., Gorgan D., “A parallel, distributed, high-performance architecture for simulating particle-based models”, in 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), (2014).
Sabou A., Gorgan D., “Interactive particle-based simulation of sociophysics models”, ICCP 2014 IEEE International Conference on Intelligent Computer Communication and Processing, pp. 411-416 (2014).
Gorgan D., Bacu V., Mihon D., Rodila D., Abbaspour, K., and Rouholahnejad, E.: Grid based calibration of SWAT hydrological models, Journal of Nat. Hazards Earth Syst. Sci., 12, pp. 2411-2423, (2012).
Gorgan D., Bacu V., Mihon D., Stefanut T., Rodila D., Cau P., Abbaspour K., Giuliani G., Ray N., Lehmann A., Software platform interoperability throughout enviroGRIDS portal, in International Journal of Selected Topics in Applied Earth Observations and Remote Sensing – JSTARS, Vol. 5/6, pp. 1617-1627, (2012).
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ACKNOWLEDGMENTS
The scientific consultancy and technology transfer has been supported by MEN-UEFISCDI by Contract no. 344/2014, PECSA - Experimental High Performance Computation Platform for Scientific Research and Entrepreneurial Development.
Part of this research was supported by the FP7 enviroGRIDS Project funded by the European Commission, through the Contract 226740.
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Many thanks for your attention!
Dorian GorganComputer Science Department
Technical University of Cluj-Napocahttp://users.utcluj.ro/~gorgan