dissertation progress report - university of notre...

Dissertation Progress Report

Tim Schoenharl

Notre Dame CSE

February 27, 2007

Tim Schoenharl (Notre Dame CSE) Dissertation Progress Report February 27, 2007 1 / 24

Overview

1 Research Goals

2 Completed Work

3 Status of Completed and Proposed Goals

4 Publications


Original Research Goals

� Network Understanding / Anomaly Detection System

� Creation of Simulations from Streaming Data

� Updating Simulations from Streaming Data

� Online Validation Against Streaming Data

� Sensor Steering


Original Research Goals

� Network Understanding / Anomaly Detection System Removed

� Creation of Simulations from Streaming Data

� Updating Simulations from Streaming Data

� Online Validation Against Streaming Data

� Sensor Steering Removed


Revised Research Goals

Research goals have been revised to reflect the priority of the WIPERproject

� GIS for Data Analysis, Visualization and Simulation

� Data Curation

� WIPER: Simulation Prediction System

� Design and Implementation of WIPER Simulation

� Movement and Activity Models


Revised Research Goals

Research goals have been revised to reflect the priority of the WIPERproject

� GIS for Data Analysis, Visualization and Simulation

� Data Curation

� WIPER: Simulation Prediction System System description, publishedin [1, 2, 3]

� Design and Implementation of WIPER Simulation

� Movement and Activity Models


Original Research Goal Status

� Creation of Simulations from Streaming Data - Mostly Complete

� Updating of Simulations from Streaming Data - In Progress

� Online Validation Against Streaming Data - Offline Implementation.Working Towards Online Implementation


Revised Research Goal Status

� GIS for Visualization and Simulation - Complete

� Data Curation - Complete

� Design and Implementation of WIPER Simulation Prediction System -Mostly complete

� Design and Implementation of WIPER Simulation - Mostly Complete

� Movement and Activity Models - Taxonomy and Implementation ofseveral movement models complete.


Creation of Simulations from Streaming Data

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Tower Activity 1 7 2 5 3 22 4 10

Movement Model

Activity Model

Figure: Graphical Explanation of Process for Generating Simulations fromStreaming Data


Creation of Simulations from Streaming Data

Procedure

� Offline: Develop Movement and Activity Models

� Offline: Build GIS files describing area

� Online: Receive “snapshot” of activity in tower cell and region fromDAS

� Online: Apply Movement and Activity Models to generate distributionof agents over cell


Validation of Simulations

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Call Activity Sampled at 10 Minute Intervals

Time Interval

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Empirical DataSimulated Data

Figure: Plot of actual andsimulated activity data.

Call Activity Data Validation

� Activity Model uses EmpiricalData to Generate Activity

� Passes Kolmogorov-Smirnovtest, D = 0.0903, p=0.6003,two-sided test at α = 0.05



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Empirical DataData with Gaussian Noise

Figure: Empirical Data Plottedagainst data normally distributedaround the points.

Call Activity Data Validation

� Data generated by normallydistributing points aroundempirical data

� Generated with mean =empirical data, sd = 1

� Fails Kolmogorov-Smirnov test,D = 0.1389, p = 0.1243,two-sided test at α = 0.05



Several issues remain when validating simulations online vs streaming data

� Valid against empirical data? Distribution? Model? Currently usingKS test against empirical data, with poor results

� KS test unable to distinguish normally distributed data generatedfrom empirical distribution

� Idea: Utilize the prior probability of anomaly from MMPP model forvalidation.

� Implement in a per cell fashion, need to be cautious with timeintervals


GIS for Visualization and Simulation

Figure: GIS Image from aWIPER Simulation

GIS Uses:

� Visualizing tower locations,relationship to urban areas, etc

� Simulations: agent and towerlocations can be initialized fromdata, agents can interact withreal world geography


GIS

Figure: Cell phone activity overlaidon a satellite image.

Figure: 3D View of Tower Activity


Data Curation

Production Machine(Azrael)

Archive/Test Machine

Internet

Incoming Data

Raw Data

Hashed Data (Flat files and DB Tables)

ND Research Machines

Data Reception Process

-Check Validity of data-Send receipt to data originator-Transfer data to archive

Data Hashing & Encryption

-Files are decompressed, hashed-More thorough integrity checking-Hashed files transferred to Production machine-Raw files are encrypted and stored

Data Upload Server (Sim5)

Figure: The Data Curation Workflow

Overview of the DataCuration Workflow

� Created workflow to curateprivacy-sensitive data

� Manage tradeoff betweenaccess to data andprotection of cell customerprivacy

� Implemented cryptographichashing scheme to protectcustomer privacy


The WIPER Scenario

Real World

Call Activity

Dynamic Social Network

Anomaly?

Simulation Prediction

System

Yes

Figure: The WIPER Scenario

WIPER Scenario

� Detect Anomalies fromstreaming data

� Run simulations to understandcrisis events

� Output results to web console


Overview of the WIPER System

Decision Support System (DSS)



Decision Support System (DSS)Simulation and

Prediction System (SPS)

Simulation and Prediction System

(SPS)


(SPS)


(SPS)


(SPS)


(SPS)

Mobile Agent for Data

Preprocessing


Preprocessing


Preprocessing

Data Source Cellular Service

Provider

Data Stream Detection and Alert System

(DAS)

Potential Anomaly?

Raw Data Stream

Dyna

mic

Data

Req

uest Dynamically

Injected Data

Web Console Clients(Internet)

Data Source and Measurement

Layer

Detection, Simulation and

Prediction Layer

Decision Support

Layer

Figure: The WIPER system

The WIPER System Components

� Real Time Data Source

� Detection and Alert System

� Simulation Prediction System

� Decision Support System


Design and Implementation of WIPER Simulation

WiperSimModelWiperAgentsVoronoAgentsDataLoggerSpatialLookupsetup()buildModel()buildDisplay()buildSchedule()

WiperAgentDataLoggerVoronoiAgentMovementModelActivityModelDataLoggerGeometrystep()moveToLatLong()Voronoi Agent

GeometrycontainedAgentscontainsAgent()getRandomIntPoint()registerAgent()

Activity Model

checkCall()modelType()

Movement Model

move()modelType()

Null Movement

Model

Random Movement

Model

Flee Visualization

Movement Model

crisisLocation

Bounded Flee Movement

ModelcrisisLocationmovementRatefleeRadius

Flee Movement

ModelcrisisLocationmovementRate

Move And Return

MovementModel

movementRatewithinThreshold()

Null Activity Model

Always Call Model

Distribution Based ModelDistributionFilegetCallsNow()

ColorMap Gradient Helper

createColorMapMinMax()

Data LoggerLog FilelogCall()logLocation()

Spatial Lookup Structure

VoronoiCellsSpatialIndexgetVoronoiCellForAgent()initialize()

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*1

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11

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*

Figure: The WIPER Simulation


Crisis Behavior Taxonomy

Crisis Behavior

Flee Flock Jam

Flee Point Flee Area Flee Line

Chemical Spill

Tsunami

Protest Traffic Jam

Fire Tsunami

Figure: A Taxonomy of Crisis Scenarios.


Movement and Activity Models

� As shown in the previous figure, movement models for crisis scenarioscan be arranged in a taxonomy. This taxonomy allows rapiddevelopment of models due to shared components.

� The class hierarchy mirrors the crisis taxonomy and uses code re-useto reduce development time and increase model validity.


Movement Models

83.3

m/min

Direction away

from Disturbance

Agent's

Movement Agent

Disturbance

Figure: Basic Flee Action

Movement Model Explanation

� Agent calculates new locationbased on direction todisturbance

�


Activity Models

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050

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Call Activity by Hour

Hour

Cal

l Act

ivity

Figure: Empirical Distributionof Call Activity

Activity Model Explanation

� Simulation Reads in EmpiricalDistribution of Call Activity forthe Day of Week

� Simulation schedules anappropriate number of calls forthe time period based on theempirical distribution


Publications

[1] T. Schoenharl, G. Madey, G. Szabo, and A.-L. Barabasi, “WIPER: Amulti-agent system for emergency response,” in Proceedings of the ThirdInternational ISCRAM Conference, May 2006.

[2] T. Schoenharl, R. Bravo, and G. Madey, “WIPER: Leveraging the cell phonenetwork for emergency response,” International Journal of Intelligent Controland Systems, vol. TBA, 2007.

[3] G. R. Madey, A.-L. Barabasi, N. V. Chawla, M. Gonzalez, D. Hachen,B. Lantz, A. Pawling, T. Schoenharl, G. Szabo, P. Wang, and P. Yan,“Enhanced situational awareness: Application of DDDAS concepts toemergency and disaster management,” in Proceedings of the InternationalConference on Computational Science (P. Sloot and J. Dongarra, eds.), May2007.


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