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SUPPORTING A MODELING CONTINUUM IN SCALATION John A. Miller Michael E. Cotterell Stephen J. Buckley University of Georgia IBM 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 www.google.com/publicdatawww.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 STSD AMPI Simulation Models high low Gen Linear Mod NBREGNNBN Prob Graph Models ARMA kNN
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Analytics and Simulation Complex System or Process Analytics Techniques Simulation Models Knowledge Ontologies Optimizers High fidelity approx 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
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