Big Data & Data Science: A Practitioner’s PerspectiveBig Data & Data Science: A Practitioner’s...

Big Data & Data Science:A Practitioner’s Perspective

Arcot [email protected]

The University of North Carolina at Chapel Hill

Outline• Challenges in Big Data & Data Science

– Scientific Data Explosion & Role of Data Science• Gearing to Meet the Challenges

– Some Projects at UNC, Chapel Hill• Looking Towards the Future

– Integration of Data, Computing & Networks

Big Data EveryWhere! • Lot of data collected and analyzed

– Sensors and Instruments– Large Scientific projects– Web data, e-commerce– Commercial/Financial

transactions– Social Network data– Medical & Health Information– Smart Cities

Lets Start with an Analogy

Data - Today

Data - Tomorrow

Characteristics of Big DataFive Vs -– Volume – Exponential Increase in Size & Count– Velocity – Speed at which Data is Created,

Processed or Used

– Variety – Multi-dimensionality, arrangement, format,… – Veracity – Integrity & Fidelity– Value – Worth

– Findability– Availability

Four Kinds of Big Data (1)• Archetypal Big Data

– Science Projects – LHC, LSST, SCEC, OOI, …– Business/Industry – Genomics, Finance, Pharma,…– Government – NASA, NOAA, NCDC, …

• Volume –High – large datasets• Velocity – High but predictable

– Few Sources, Multiple Destinations• Variety – Low – Standardized Formats

– Few Varieties • Veracity – High Fidelity and Credible

– High Quality Metadata, Corrected data• Value – High – focused, funded

– Managed by Professionals • Findability – High – Known distribution sites

– Set Policies for Sharing and Administration• Availability – High – Published API

– Curated for Long-term PreservationLight & Visible Data

8

infographics.socialnama.comwired.com

https://www.google.com/search?q=flood+of+big+data&biw=1482&bih=680&tbm=isch&tbs=simg:CAQSpgEJqCPX1WVQvLsakQELELCMpwgahwEKOggCEhTMCbAJqxCqEO4SrRCiD7sIlwn6GRogqhY3HvfR2oQf1XPofmqKwvql810y9enxac7QfULbqYkKSQgDEhPEA8kD7Qi-A5sXwQOaF8sDYswDGjD9gr378Igt-BJtdjHbxpQzN2bTBT6t-WtEleqrZ5TxKqrUwJK2K9SlJ0D1kPXLbzUMIeRivrXwF8Ynhttps://www.google.com/search?q=flood+of+big+data&biw=1482&bih=680&tbm=isch&tbs=simg:CAQSpgEJqCPX1WVQvLsakQELELCMpwgahwEKOggCEhTMCbAJqxCqEO4SrRCiD7sIlwn6GRogqhY3HvfR2oQf1XPofmqKwvql810y9enxac7QfULbqYkKSQgDEhPEA8kD7Qi-A5sXwQOaF8sDYswDGjD9gr378Igt-BJtdjHbxpQzN2bTBT6t-WtEleqrZ5TxKqrUwJK2K9SlJ0D1kPXLbzUMIeRivrXwF8Yn

Four Kinds of Big Data (2)• Crowd-Sourced Big Data

– Social Media – Facebook, Twitter, Instagram, …– Recommenders – Yelp, Angie’s List, Groupon– Web Commerce – Amazon, Ebay, Orbitz, eNews

• Volume –High – small data• Velocity – High and non predictable

– Multiple Sources/Destinations, Few Concentrations• Variety – High – But well managed

– Site Specific• Veracity – Mixed - Low to High

– Crowd Sourced – what do you expect!!• Value – Ephemeral

– Can be None to High• Findability – High – Advertised & Known

– Web pages and Apps• Availability – Immediate Interest

– Long-term Availability is iffy

Nova-like Data 9

www.plannedparenthood.org-

dreamstime.com


Four Kinds of Big Data (3)• Long-tail Big Data

– Science Projects – small teams and organizations– Personal – Hobbies, Amateur/Citizen Science/Arts– Government – Internal and unpublished

• Volume – High – small data sets– Highly Distributed , Isolated and Hidden

• Velocity – Low– Internal usage

• Variety – High – Too many, No Common Format – Project specific, Idiosyncratic

• Veracity – Non Credible until proven– No Metadata or Non-standard Metadata– Fidelity and Integrity unknown

• Value – unknown– Earned by reputation

• Findability – None – Hidden and not advertised– No Sharing , No Services– No Administration or Management

• Availability – None – In local, disks and tapes– No Notion of Long-term Preservation

Dark Data 10

teradata.com

Images.frompo.com


Four Kinds of Big Data (4)• Sensor Streams

– Environmental & Geoscience Sensors – Smart Cities & Internet of Things – Ubiquity– Personal – Wearables, Health and Home (Fitbit, iPhones)

• Volume – High – small packets to HD video– Highly Distributed , Few concentrators

• Velocity – High and time-critical– Internal usage

• Variety – High – Too many Types – Sensor/Vendor specific

• Veracity – Credible (until proven)– Non-standard Metadata

• Value – unknown (as yet)– New Applications

• Findability – None – Hidden and not advertised– No Sharing , Closed Services– No Data Management

• Availability – None After Immediate – Very Low Long-term Preservation

Dark Data 11

Four Kinds of Big DataArchetypal

Science ProjectsLHC, LSST, SCEC

Business/IndustryGenomics, Finance

GovernmentNASA, NOAA, DOE

Crowd-SourcedSocial Media

Facebook, Twitter

RecommendersYelp, Angie, Groupon

Web CommerceAmazon, Ebay

Long-tailScience Projects

Small organizations

Personal Hobbies, Citizen Science/Arts

GovernmentInternal and unpublished

Sensor StreamsInternet of Things

Appliances, Homes

Smart CitiesEnergy grids, Transportation

HealthBiosensors, ER,OR

Characterization Archetypal Crowd-Sourced Long-tail Sensor Streams

Volume High High High High

Velocity High Bursty Low High

Variety Low High High High

Veracity High Mixed Low Mixed

Value High Ephemeral Unknown Huge

Findability High High None None

Availability High Short-term None Low

Big Data Paradigm ShiftWe need to know more about Data Science because we are in the midst of a paradigm shift: not only we have big data,

the way we do Science, Research and Business is changing

• Compute Intensive to Data Intensive

• Large Actions on Small Amounts of Data toSmall Actions on Large Numbers of Data

• Move Data to Processing Site (Supercomputer Model, Warehouse Model)Move Process to Data Site (Map-Reduce Model, Federation Model)

• Function Chaining (Programs) to Service Chaining (Workflows and DataFlows)

Leading to a Large Paradigm Shift:• Model-based Science /Business (Observe-Hypothesize-Test)

Data-based Science /Business (Data Mining, Knowledge Discovery)

Data Science is needed to bridge the future

Data Science

Computer Science

Information Science

SocialScienceMath, Logic,Statistics, OR, …

Domain SciencesEngineering EconomicsMedicalLegal

…

Building a Big Data Platformfor The First & Third Kind

Big Data Problems• Where is the processing hosted?

– Distributed server/cloud (eg. Amazon EC2, Microsoft Azure)• Where data is stored?

– Distributed Storage (eg: Amazon S3)• Where is the programming model?

– Distributed processing (eg. Google’s Map Reduce - Hadoop)• How data is stored and indexed? (eg. Apache Cassandra)

– High performance schema free database• What operations are performed on the data?

– Analytic/Semantic Processing (eg. SAS, KNIME, ..)

• How to make data available?• How to manage the data?• How to find it?

Building Towards Big Data• Storage Resource Broker (SRB) (1996-2006)

– Massive Data Analysis System (DARPA)• Super computing

– Distributed Object Computation Testbed (DARPA, USPTO, NARA)• Distributed Data Flow

– National Partnership for Advanced Computing Infrastructure (NSF)• Science Data and Metadata Management

– Transcontinental Persistent Archives Prototype (NARA) • Infrastructure Independence

• Integrated Rule Oriented Data Systems (iRODS) (2005->– Life Time Library (SILS)

• Personal Digital Library– Carolina Digital Repository (UNC)

• Institutional Repository– DataNet Federation Consortium (NSF)

• National-scale Cross Disciplinary Collaboration• Data Bridge CI (2013->

– Data Bridge (NSF)• Long-tail of Science “Data Communities”

– Data Bridge for Neuroscience (NSF)– …

Big Data of the First Kind:iRODS Distributed Data Management

UserWith Client Views & Manages Data

My DataDisk, Tape, Database, Streams, Filesystem,

etc.

The iRODS Data System can install in a “layer” over existing or new data, letting you view, manage, and share part or all of diverse data in a unified Collection.

iRODS Shows Unified “Virtual Collection”

Data in the Cloud

Disk, Tape, Database, Filesystem, etc.

User Sees Single “Virtual Collection”with metadata & services

Partner’s DataRemote Disk, Tape,

Files, DBs, etc.

Ingredients of iRODS• Virtualization

– Independence from Technology - Namespaces– Third Party Authorization, Authentication, Auditing, Accounting, Mediation

• Automation thru Policies (Rules & Microservices)– Event-based and periodic actions– Orchestration of Workflows and Provenance Capture – Repeatable Data Science

• Metadata– Integration of Context– Mediation across Multiple Metadata Systems

• Federation – Three types of Federation– Link systems/resources together

CollectionPurpose

Completeness

Correctness

Consensus

Defines

Consistency

Attribute

HasFeature

HasFeature

HasFeature

Has

Defines

Policy

Has

Property Defines ProcedureControls Updates

Client Action

Periodic Assessment

Criteria Policy

Policy Enforcement

Point

Workflow

Invokes

HasSubType Isa

Micro-service

Chains

Operation

Isa

PersistentState

Information

Isa

Digital Object

Updates

Has

Has

Replication

Checksum

Quota

Data TypeIsa

IsaIntegrity

Isa

AuthenticityIsa

Access control

Isa

msiGetUserACL

msiSetDataType

msiSetQuota

msiDataObjRepl

msiSysChksumDataObj

Isa

Isa

Isa

Isa

Isa

DATA_ID DATA_REPL_NUM DATA_CHECKSUM

Isa Isa Isa

Policy-based Collection Management

Isa

Isa

HasFeature

Invokes

Datanet Federation Consortiumhttp://datafed.org/

VISION:Enable Collaboration across Scientific DomainsSupport reproducible data-driven researchBuild a National Scale Data Cyberinfrastructure

IDEAS:Build a Federated Environment

Data SharingMetadata –based DiscoveryWorkflow OrchestrationPolicy as basis for collaboration

Research Environment - Portals, Applications, Workflows

DFC Collaboration Environment –Data Grid

Community Resource – Repository, Catalog

DataNet Federation Consortium Vision• Enable collaborative research

– Sharing of data, information, and knowledge• Build national-scale cyber-infrastructure

– Federation of existing data management systems

• Support reproducible data-driven research– Encapsulate knowledge in shared workflows

• Enable student participation in research– Policy-controlled access to “live” data

Data Driven Science and Engineering• Collaboration Environments

– Oceanography – Ocean Observatory Initiative• Archiving of climatic data records from real-time

sensor data streams, replay of sensor data– Engineering – CIBER-U

• Engineering Digital Library: curation of civil engineering data, student training materials

– Hydrology - CUAHSI, …• Automation of hydrology research workflows

(reproduce, reuse and repurpose)– Plant Biology – iPlant Collaoratory

• Project data I sharing and integration, virtualized metadata services

– Social Science – Odum Institute• Survey data and Statistical data processing

– Cognitive Science – Temporal Dynamics Learning• Inter-team collaboration policies, human data

Engineering Representation

Federation is Central to DFC• DFC exposes three models of Federation

– Strong Federation• Full and complete protocol-level federation across grids• Seamlessly Move from one grid to another • Used in DFC to federate Science & Engineering grids

– Weak Federation = peering• One-way DFC to External Micro-services and Workflows• DFC needs to ‘know’ the external protocol - plug-ins & wrappers• Used in DFC

– To access THREDDS (netCDF), Sensor system, federal data resources– SEAD, DataONE, and CUAHSI-HIS , AWS

– Asynchronous Federation = loose coupling• Message used to interact with external system• Easy to connect (similar to Data Bridge)• Used in DFC to expose indexing and format conversion services

Three Federations in DFC

Eng

Mar

Main

Hydr

SoL

Soc

Bio

THREDDSSEAD

TerraPop

NASA/ NCCS

NCDC

EC2S3

ARTS/ORB

DataBook

Vivo Indexer

HIVE Ontology 2

HIVE Ontology 1

Elastic Search

Format Indexer

SOLR

JENA

Data Verse

DataONE

Policies Govern the DFC

Policies for automatingdata management

StandardsGroups

InternationalProjects

AdvisoryCommittee

Science &Engineering

Domains

SustainabilityAnd

Institutions

FacilitiesAnd

Operations

TechnologyAnd

Research

EducationAnd

OutreachPolicies

AndStandards

Policies forpublication& federation

Policies for IPR & citations

Policies forprovenance & sustainability

Policies forcollaboration and reuse

Policies fortechnology migration

Policies formetadata extraction

Policies foranalysis and workflow

Policies for change management

Domain-centricpolicies

Policies forauthentication & authorization

Policies forarchiving, staging & caching

Policies forreplication & synchronization

Policies forretention & disposition

Policies forDeletion & redaction

Policies fortrust

Polices forcuration & preservation

How iPlant CI Enables DiscoveryOverview of resources

End

Use

rsCo

mpu

tatio

nal U

sers

XSEDE

Storage Computation Hosting Web Services Scalability

Building a platformthat can support diverse and constantly evolving needs.

Fig 3. iPlant Discovery Environment, showing the Data, App, and Analyses windows.

Merchant N, Lyons E, Goff S, Vaughn M, Ware D, et al. (2016) The iPlant Collaborative: Cyberinfrastructure for Enabling Data to Discovery for the Life Sciences. PLOS Biology 14(1): e1002342. https://doi.org/10.1371/journal.pbio.1002342http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002342

http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002342

How iPlant CI Enables DiscoveryWhat iPlant data solutions mean for a bovine breeder

“It's kind of like being in that COPD commercial where the weight is lifted off your chest, only in our case, we have access to more computational power, so we can get to projects much faster and we can do big projects that our machines may not have allowed us to do previously!

The ability to transport 2TB of data overnight using the iRODS system was particularly helpful because previously, we had been mailing hard drives which is not an optimal solution to sharing big data.”

James Koltes ,Iowa State

DataBridgehttp://databridge.web.unc.edu/

VISION:Data as a Citizen – Empower Data

IDEAS:Build Social Networks for

Long-tail Science Data Detect Communities through

Multi-dimensional Socio-metric Analyses

Big Data of the Third Kind:

Power law distribution

Data in the Large

Data in the Small

• Dark Data - Hidden, Unknown• But has Presence and Value/worth• Voluminous, Varigated, Unknown Veracity

Long Tail of Data

Problems: Long-tail of Science DataFirst Mile Problem• How to make it available?• Where do I upload?• Who is in charge?• How do I get credit?• Can I control access?• How do I pool with other

like-minded researchers? Community services?

• How much is long-term?• Who pays for it?

Last Mile Problem• How to make it findable?• What is needed to make it

more visible? Metadata?• Are there other methods to

make my data findable?• My data has specific ways &

characteristics how do I expose them as finding aids?

• How can I find similar • data?

Data Bridge 35

Solving the long-tail problem will also help other two Big Data problems

blog.enrichconsulting.com

Data Bridge: DesignConstruct multi-dimensional social networks for data. Three challenges:• Evaluate multiple types of

“metrics” on data– Domain-specific, genre-specific,

project-specific– Use Socio-metric Network Algorithms– Similar to – but for data

• Find relevance– Slices of similarity– Explore Relationships between

Data, Users, Resources, Methods, Workflows, …– Use Relevance Algorithms

• Create communities– Use Clustering Algorithms

• Provide an extensible & big data framework– Democratize the process Data Bridge 36

ScreenShot: Finding similarities

Data Bridge 37

Filter Connectivity by similarity value

Select Network Data

ScreenShot: Weight of similarity

38

Similarity measure: 0.5

ScreenShot: Highlights of similarities

39

Link to the data

Data Bridge Architecture

40

Two New(inter-related)

Challenging Problem:

Smart CitiesInternet of Things

Big Data of the Fourth Kind

What is a smart city?

1. SMART: Urban platforms, Internet, (Big) data manipulation, sensoring and metering

2. SUSTAINABLE: Healthy, Pollution free, Balanced

3. INCLUSIVE: Every one has equal rights and possibilities, no poverty, Social and economic sustainable as well

4. INNOVATIVE: Technologies, creativity, buildings, transport, etc.

5. WELL PLANNED: Urban planning 2.0

SMART CITY

Smart City =Smart Information + Smart Infrastructure + Smart Communication

Internet of Things• The Internet of Things “Internet of Objects” “Machine-to-

Machine Era” “Internet of Everything” • The Internet of Things, also called The Internet of Objects, refers

to a wireless network between objects, usually the network will be wireless and selfconfiguring, such as household appliances. ------Wikipedia

• Internet of Things refers to the concept that the Internet is no longer just a global network for people to communicate with one another using computers, but it is also a platform for devices to communicate electronically with the world around them.” --Center for Data and Innovation

Our Project: Smart State NC• Promote Sustainable Social and Economic

Developments across the State of North Carolina• Build bridges across Rural and Urban

Communities• Apply Innovative Technologies

– Big Data & Deep Learning– Data Sharing Urban Platforms– Social Media Networks– Internet of Things & Sensor Networks– Cloud Computing & Edge Computing

• North Carolina as an Extreme Living Lab

– Pilot Deployments across Communities

NSF SCC Proposal• Building a North Carolina Smart & Connected

Communities Hub – Tackle this growing gap between urban and rural communities.– Adapt and adopt S&CC innovations in data analytics and

technology from urban settings and effectively apply them in rural settings.

– State of North Carolina as a living laboratory and present a series of well-defined research experiments targeted at specific NC communities.

• The goal of each living lab is to apply innovative strategies to solve rural problems and provide a sustainable resource for use by rural communities well beyond the endpoint of the proposed research.

– Through the use of smart technological and social innovations, we hope to (re)connect small rural communities with each other and with urban areas across NC.

• Multiple government agencies and five universities• https://smartcities.web.unc.edu/ 47

SmartAnalytics

HardwarePlatform

SoftwareTools

DataNetworks

SmartInfrastructure

SmartAir & Water

SmartEnergy

SmartEnvironment

RuralUrban

National

SmartCommunities

SmartLiving

SmartCitizens

SmartHealth

S&CC Model of Connectedness: Multiple Dimensionalities (triangles), Each with Shared Disciplinary Components (circles)

Data & Information

ScienceComputer

Science

SocialScience

ICT: Information & Communication Technologies

IoT: Internet of Things (sensors)Advanced Data &

Information AnalyticsSocial Media & Networks

SmartMobility

SmartEconomy

SmartGovernance

SmartPolicies

SmartLiteracy

S&CC Model

Living Labs

(Health)

Living Labs

(Food)

NCAoT: North Carolina Array of Things Sensor Network

Living Labs

(Air &Water)

Living Labs

(Disaster Recovery)

Living Labs

(Energy)

Living Labs(Pollution)

Mitigate Literacy Gap(Accessibility & Literacy POPs)

Community Engagement(Workshops & Meetings)

NC Smart & Connected Communities Hub: A Socio-Technical System Approach

PSMS: Place-based Social Media Services

SCCConnect: Open Data Platform for Collaboration, Data & Apps

Living Labs

(Mobility)

Cross-cutting ThemesEnhanced Accessibility, Rural Revitalization & Sustainable Solutions

Players in NSF SCCI (proposed project)Community Engagement

(Workshops & Town Hall Meetings) Academic Engagement*

Wak

eFo

rest

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Sylv

a[?

]

Tran

sylv

ania

Coun

ty[?

]

Kins

ton

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Jord

anLa

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ake

How

ell

Pitt

Coun

ty[?

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Spin

dale

[?]

Salis

bury

[?]

Regi

onA

(SW

C)[?

]

Regi

on G

(PTR

C) [?

]

Regi

onC

(IPDC

)[?]

Living Lab Areas for Rural Revitalization & Sustainable Solutions

Smar

t En

viro

nmen

ts

Disaster Recovery X ECU, UNC-CH

Energy X X WCU, UNC-C

Food Production X NCSU

Water Management X X UNC-C, UNC-CH

Pollution Monitoring X X X UNC-C, UNC-CH

Common NCSCCHub Infrastructure

Smar

t Po

licie

s

Open Data Platforms(SCCConnect)

X X X X UNC-CH

Sensor Networks(NCAoT)

X X X X UNC-C, UNC-CH

Smar

t Ci

tizen

s

Accessibility/ Literacy PoPs

X X UNC-CH

Place-based Social Media Systems

X X X X UNC-C

*ECU = East Carolina University; NCSU = North Carolina State University; UNC-C = UNC – Charlotte; UNC-CH = UNC – Chapel Hill; WCU = Western Carolina University; SWC = South Western Commission; IPDC = Isothermal Planning and Development Commission; PTRC = Piedmont Triad Regional Council

Region SpokesScience Spokes Infrastructure Spokes

Food

Air & Water

Energy

DisasterRecovery

Health

Apalachia

Piedmont

Coastal Plains

Coast

WesternFoothills

SensorApps & DataAnalytics

Open DataPlatform

SocialMediaSystems

CommunityPoPs

Governments

Citizens

Academics Industries&Businesses

NGOs

Community Spokes

CentralHub

Fig 2. NC SCC Hub and Spoke System. The system is multi-tiered with four key hub-and-spokes dimensions. The central hub stitches together all the dimensions and acts as a coordination center for the whole initiative.

S&CC Platform Framework

A Second Challenge:Data-Centric Collaborative Research

Confluence of forces reshaping how data is leveraged• Data volumes, network limitations, requirements around security, privacy, regulations, new analysis methods that

rely on unique hardware/software systems, cloud computing models, dizzying array of new tools

Forces are misdirecting researchers from science• Provisioning storage, compute servers, and networking• Setting up and configuring data sharing and transfer tools• Provisioning and customizing complex tools on complex IT and High Performance Computing (HPC) systems• Determining how to secure data and IT systems• Figuring out how to publish data and tools for others to discover and use

Data centric collaborations are requiring too many technologists• data scientists, informaticians, library scientists, computer scientists, HPC experts, IT vendors, and

security/privacy experts

Same challenges exist across scientific domains

S

Deep Indexing(DataBridge)

Cloud/HPC Computing(CyVerse Atmosphere)

Elastic Collaboration(RADII)

Federated Data(iRODS, DFC)

Secure Data Spaces

Visualization Interfaces

Approach

Provides an on-demand distributed, private workspace for sharing tools and data

Conclusion• Challenges in Big Data & Data Science




Data Science & Big Data in the Future• Organization• Classification• Ontologies• Metadata• Retrieval• Management• Collection building• Analysis• Information seeking• Knowledge Representation• Human Computer Interaction• Social Skills• Data Citizenship• Information Behavior

• Ethics• Privacy• Security• Information Technology• Transformation, Interpretation• Dissemination• Application• Reference Collections• Information Processing• Data Mining• Information Visualization• Information Network• Policy

The List is the Same for Tomorrow – No Different than Today – But Changes to meet the 5Vs New Methodology, New Way of Thinking, New Processing Paradigms, New Interactions

Slide Number 1OutlineBig Data EveryWhere! Lets Start with an AnalogyData - TodayData - TomorrowCharacteristics of Big DataFour Kinds of Big Data (1)Four Kinds of Big Data (2)Four Kinds of Big Data (3)Four Kinds of Big Data (4)Four Kinds of Big DataBig Data Paradigm Shift Data ScienceOutlineBuilding a Big Data Platform�for The First & Third Kind��Big Data ProblemsBuilding Towards Big DataBig Data of the First Kind:�iRODS Distributed Data ManagementSlide Number 20Ingredients of iRODSSlide Number 22Datanet Federation Consortium�http://datafed.org/DataNet Federation Consortium VisionData Driven Science and EngineeringFederation is Central to DFCThree Federations in DFCPolicies Govern the DFCSlide Number 29Slide Number 30Slide Number 31DataBridge�http://databridge.web.unc.edu/Power law distributionProblems: Long-tail of Science DataData Bridge: DesignScreenShot: Finding similaritiesScreenShot: Weight of similarityScreenShot: Highlights of similaritiesData Bridge ArchitectureOutlineTwo New�(inter-related)�Challenging Problem:� �Smart Cities�Internet of Things��Big Data of the Fourth Kind�Slide Number 44Internet of ThingsOur Project: Smart State NCNSF SCC Proposal S&CC Model of Connectedness: Multiple Dimensionalities (triangles), Each with Shared Disciplinary Components (circles)Slide Number 49Players in NSF SCCI (proposed project)Slide Number 51A Second Challenge:� Data-Centric Collaborative ResearchApproachConclusionData Science & Big Data in the Future

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