SUPPORTING A MODELING CONTINUUM IN SCALATION

John A. MillerMichael E. CotterellStephen J. Buckley

University of GeorgiaIBM Thomas J. Watson Research Center

Outline

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

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

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)

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)

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

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

Simulation in ScalaTion

● Event-Scheduling (ES)● Process-Interaction (PI)● Activity Models (AM)● State-Transition Models (ST)● System Dynamics (SD)

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?

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

Analytics and Simulation

Complex System orProcessAnalytics

Techniques

SimulationModels

KnowledgeOntologies

OptimizersHigh fidelityapprox

Low fidelity approx

Data extraction

Induction

Model building

Output

Calibration

Statistics

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

IBM Europe PC Study

● Item

IBM Asset Management Tool

● Item

IBM Pandemic Business Impact Modeler● Item

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

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.

Questions