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SUPPORTING A MODELING CONTINUUM IN SCALATION
John A. MillerMichael E. CotterellStephen J. Buckley
University of GeorgiaIBM Thomas J. Watson Research Center
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Outline
● Introduction● Big Data Analytics● Relationship to Simulation Modeling● Modeling Continuum● Application to Supply Chain Management● Conclusions and Future Work
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Introduction
● Related Disciplines– Analytics– Data Mining– Machine Learning– Simulation Modeling
● So What's New– Massive Amounts of Data– Web Accessible Data– Meta-data and Semantics– Availability of Multi-core Clusters– High-level Programming Environments
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Era of Big Data
● Sources of Big Data– Scientific Experiments: Large Hadron Collider– Business Transactions: IBM Analytics– Wireless Sensor Networks: Environment– Social Networks: twitter-2010– Public: www.google.com/publicdata,
www.bigdata-startups.com/public-data, www.kdnuggets.com/datasets
● 3Vs of Big Data– Volume (TB+), Variety, Velocity (Streams)
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Era of Big Data
● Distributed Data– Distributed Databases (e.g., HP Vertica)– Distributed File Systems (e.g., HDFS)– Large Matrices, Sparse Matrices and Graphs
● Computational Models for Clusters– Map-Reduce (e.g., Hadoop)– Bulk Synchronous Parallel (BSP)– Asynchronous Parallel– Message Passing (e.g., MPI, Akka)
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Big Data Analytics in ScalaTion
● Scala– Object-Oriented Functional Language– Java-based, but 3x more concise– Support for
• Parallel Computing (ParArray, .par)• Distributed Computing (Akka)
● ScalaTion– Multi-paradigm Modeling using Scala
• Simulation, Analytics, Optimization– High-Level and concise like MATLAB and R
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Big Data Analytics in ScalaTion
● Prediction: y = f(x, t; b)– Regression (REG),– Nonlinear Regression (NRG),– Neural Nets (NN), ARMA Models
● Classification: c = f(x, b)– Logistic Regression (LRG)+,– k-Nearest Neighbors (kNN), – Naive Bayes (NB), Bayesian Networks (BN),– Support Vector Machines (SVM),– Decision Trees (DT)
+ also used for prediction
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Simulation in ScalaTion
● Event-Scheduling (ES)● Process-Interaction (PI)● Activity Models (AM)● State-Transition Models (ST)● System Dynamics (SD)
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Big Data and Simulation
● Relationships– Simulation models make data, data make
better simulation models– Analytics: more data rich– Simulation: more knowledge rich
● Building Simulation Models– Determination of Components – Analysis of Components
• “Small Data Analytics”– How will “Big Data” impact this process?
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Modeling Continuum: Structural Richness
Hierarchical Models
ES ST SD AM PI
Simulation Models
highlow
Gen Linear Mod
NB REG NN BN
Prob Graph Models
ARMAkNN
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Analytics and Simulation
Complex System orProcessAnalytics
Techniques
SimulationModels
KnowledgeOntologies
OptimizersHigh fidelityapprox
Low fidelity approx
Data extraction
Induction
Model building
Output
Calibration
Statistics
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Application to Supply Management
● Forecasting– Time-dependent predictive analytics techniques
– Forecasts feed supply change process
– Satisfy demand on a continuing basis
● Simulation– Simulate various scenarios (changes in
Supply/Demand, etc.) to determine effects
– Use both forecasting and simulation to make decisions
● Three Case Studies– To illustrate the point
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IBM Europe PC Study
● Item
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IBM Asset Management Tool
● Item
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IBM Pandemic Business Impact Modeler● Item
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Conclusions
● Impact of Big Data– Must effectively handle and utilize massive data
● Role of Simulation in Big Data– Organizing data
– Generating/evaluating scenarios
– Supporting better decision making
● Role of Big Data in Simulation– Increasing model richness/fidelity
– Better model calibration
– Hybrid systems
● Emerging Discipline of Data Science
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Future Work
● Featured Minitrack at WSC 2014– Big Data Analytics and Decision
Making– Leverage the 3Vs to make better
decisions– Applications areas:
• Atomic physics, weather, power grids,
traffic networks, urban populations, etc.
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Questions