SUPPORTING A MODELING CONTINUUM IN SCALATION

1. John A. Miller Michael E. Cotterell Stephen J. Buckley University of Georgia IBM Thomas J. Watson Research Center SUPPORTING A MODELING CONTINUUM IN SCALATION

2. Introduction Big Data Analytics Relationship to Simulation Modeling Modeling Continuum Application to Supply Chain Management Conclusions and Future Work Outline

3. 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

4. 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)

5. 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)

6. 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

7. 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

8. Simulation in ScalaTion • Event-Scheduling (ES) • Process-Interaction (PI) • Activity Models (AM) • State-Transition Models (ST) • System Dynamics (SD)

9. 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?

10. Modeling Continuum: Structural Richness Hierarchical Models Gen Linear Mod Prob Graph Models kNN NB REG NN BN ARMA low high ES ST SD AM PI Simulation Models

11. Analytics and Simulation Low fidelity approx Complex System or Process Analytics Techniques Data extraction Statistics Optimizers Induction Calibration High fidelity approx Output Knowledge Ontologies Simulation Models Model building

12. 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

13. IBM Europe PC Study • Item

14. IBM Asset Management Tool • Item

15. IBM Pandemic Business Impact Modeler • Item

16. 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

17. 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.

18. Questions