SUPPORTING A MODELING CONTINUUM IN SCALATION

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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
• 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
Gen Linear Mod
Prob Graph Models
kNN
NB
REG
NN
BN
ARMA
low
high
ES ST SD AM PI
Simulation Models
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Analytics and Simulation
Low fidelity approx
Complex System or Process
Analytics Techniques
Data extraction
Statistics
Optimizers
High fidelity approx
Induction
Calibration
Output
Simulation Models
Knowledge Ontologies
Model building
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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