Post on 28-Apr-2018
transcript
TERESA TUNG, PH.D.
DEVELOPMENT
WITH AN
IoT PLATFORM
INNOVATION
THROUGH AGILITY
Copyright © 2016 Accenture All rights reserved.
THE PLATFORM CORE
THE ROLE OF THE
IoT PLATFORM
IoT is an iterative process that starts with an initial use case that is then rapidly adapted. Faster deployment and iteration result in better applications, more accurate analytics, better outcomes.
PLATFORM ACCELERATES DEVELOPMENT
Agility to rapidly develop and evolve use case. Scale number of devices, volumes of data, analytical models.
PLATFORM GROWS NEW USE CASES
Agility to harvest, discover, and reuse IoT components. Scale horizontal components to scale across verticals.
10s
Models
3,500
msg/sec
1 Env
AWS
1000s
Models
6,000
msg/sec
3 Env
AWS
THE IoT APPLICATION
PLATFORM
HARVESTS
MODELS
DEVICES
CLOUD
DATA
APPLICATIONS
PLATFORM ACCELERATES
2
PL
AT
FO
RM
G
RO
WS
ENABLING AGILITY AND SCALE OF APPLICATIONS
Copyright © 2016 Accenture All rights reserved. 3
A HOW TO
DRIVE AGILITY WITH AN IoT PLATFORM
Infrastructure Management
Application Management
Data Architecture Management
• Infrastructure Deployment
• System Configuration
• System Monitoring
• Asset Model and Relationships
• Visualization
• Hypermedia API
• Domain model and schema
• Data Mapping and Discovery
• Real-time Analytical model
• Batch Analytics
Install, Deploy, Configure, Test,
Link and Monitor
INTEROPERABLE
REFERENCE
ARCHITECTURE
CAPTURE &
CONFIGURE
SPECIFIC INSTANCES
. . .
Copyright © 2016 Accenture All rights reserved. 4
DEVELOPMENT:
THE FIRST APP
BUILD IT ALL: BASE COMPONENTS
AND CLIENT SPECIFIC INTERFACES
ASSET
HEALTH
LOW-
LOW
DETECT
CURVE
FITTING
TIME
SERIES
CLIENT
INSTANCE
CLIENT
INSTANCE
DEVICE
INSTANCE
AWS
EAST
DEVICE
INSTANCE
MODELS
DEVICES
CLOUD
DATA
APPLICATIONS
SMART WATER
12 weeksInitial real-time streamingBase set of components
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DEVELOPMENT:
THE NEW APP
CONFIGURE AND REUSE;
FOCUS ON DOMAIN-SPECIFICS
ASSET
HEALTH
SURVIVAL
ANALYSIS
CURVE
FITTING
TIME
SERIES
CLIENT
INSURANCE
AGE,
MANUFACTURE,
LOAD
CLIENT
INSURANCE
AWS
EAST
RISK
ANALYSIS
AGE
DEVICE
INSTANCE
DEVICE
INSTANCE
MODELS
DEVICES
CLOUD
DATA
APPLICATIONS
SMART GRID
8 weeksLeverage existing baseContribute new components
Copyright © 2016 Accenture All rights reserved. 6
DEVELOPMENT:
THE NEW CAPABILITY
REUSE MOST OF THE STACK; ADD NEW
CAPABILITY AND HARVEST
MODELS
DEVICES
CLOUD
DATA
APPLICATIONS
ASSET
HEALTH
DETECT
IMAGE
IMAGES
CLIENT
INSTANCE
CLIENT
INSTANCE
AWS
EAST
DEVICE
INSTANCE
Caffe
OIL & GAS
2 weeksMain focus on domain specificHeavy reuse of components
NEW
NEW
NEW
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CONFIGURE TO DEVELOP
CAPTURE FOR REUSE
PUTTING THE “INTERNET” IN IoT
2. CAPTURE
FOR REUSE
3. CONFIGURE TO
RAPIDLY DEPLOY
1. DEVELOP FOR
INTEROPERABILITY
Platform relevance requires an ecosystem
effect. Ease extension and harvest of new
capabilities via a self-service model.
Automation simplifies deployment. Parallelism
allows brute force comparison of components
in champion-challenger fashion. Self-learning
recommendations assist discovery and identify
new relevant components.
Design assuming heterogeneity across the
stack – devices, analytics compute engines,
datastores, applications. Establish interoperable
and asynchronous patterns.
Domain Knowledge GraphSmart Water
System Metadata
Copyright © 2016 Accenture All rights reserved.
LEGEND
8
DEVELOP FOR INTEROPERABILITY
CONFIGURABLE PIPELINE
Platform Build
Platform Configure
3rd party Integration
Client-specific Build
RAPID VISUALIZATION
DEVELOPMENT
FRAMEWORK augments BI tools to create analytics applications
DATA VIRTUALIZATION
presents a single logical access interface to all data stores
ANALYTICS LIFECYCLE
MANAGEMENT
manages configuring, launching, and monitoring analytics models
DEVICE MANAGEMENT
is vendor agnostic
CLOUD MANAGEMENT
is deployable across cloud
providers
PLATFORM MANAGER
INDUSTRY ENABLERS
ACCESS LAYERS
API MANAGER
EGRESS GATEWAY ADAPTERS
Platform Admin UI
Configuration / Monitoring
Developer Portal /Catalog
Web Apps Mobile Apps Business Intelligence Adhoc Analysis
Mediation
Service AssuranceCustomer
Subscription Relationship
Manager Software Manager
Business Rules Manager
Device Inventory
Device Manager
INGRESS GATEWAY ADAPTERS
INGRESS GATEWAY ADAPTERS
STREAMING LEG
BATCH LEG
DATA MANAGER
ANALYTICS
FEDERATED SECURITY
Device Comm. Manager
RTU
Intel Gateway
Device Agent
Batch Data IngestStaging
(SFTP, HDFS)
Storage Manager
Post-Process Event Router
Virtualized Data Access (Hive, PrestoDB)
Data Service Layer (API)
HA NoSQL Warehouse
RDBMS Data Lake
ETL Orchestration
Spark
KinesisAnalytics Layer
(SAS, R)
Stream Processing
Pre Processor
Batch Processing
Post Processing
IAM
Active Directory / Customer ID
External Gateway
Legacy Data Gateway
Notification HubDevice Manager
Device Comm. Manager
HTTPS
TCP/IP
External to Platform
Copyright © 2016 Accenture All rights reserved.
H&PS
Muscular/skeletal diagrams, cell systems
RESOURCES
Asset and workforce tracking, dispatch management, resource allocation
FINANCIAL SERVICES
Candlestick, OHCL indicators, geographical risk, lender-to-lender, and loan tranches
PRODUCTS
factory schematics
TELCO
3D globe of call data, network diagrams, security threat maps
9
APPLICATION
INTEROPERABILITY
THE VIZ STACK THE VIZ COMPONENTS
WEB CLIENT
APPLICATION
HTML, CSS, JavascriptSPAs, React.js, Angular.js, Backbone.js, etc
SERVER
APPLICATION
Java, Javascript, C#, PythonREST API, SOAP, sockets
DATA STORAGE
MySQL, Redshift, Teradata, Hana, OracleRDBMS, NoSQL, Document Stores, etc
TH
E S
TA
CK
A
STANDARDS
For how to build reusable industry specific visualizations
B
APP TEMPLATES
That drive decision science based user experience
C
APIS
Data type specific APIs that map to data input format standards
Dimensions
Data Input Format
Styling
Lifecycle
Code Structure
Layouts
MVC Patterns
Interactions
Guided Decisions
Geospatial
Tabular
Network
Hierarchical
Time Series
Aardvark Sample
D
DOMAIN SPECIFIC
LIBRARY
Needed for a variety of problems in different industries.
Example are:
Pronounced “Aardvark”—the Rapid Visualization Development Framework makes it easier to manage custom applications to supplement BI tools
Copyright © 2016 Accenture All rights reserved. 10
MODEL
INTEROPERABILITY
1. Data Science Playground
2. Deployment Wizard
Quality
Control
Center
UNIQUE FEATURES
3. MonitoringDiagnostics
4. Maintenance 5. Wiki
Server
REFERENCE ARCHITECTURE MODEL LIFECYCLE COMPONENTS
AUTOMATED ANALYTICS
USERS
Data Scientists Business Analysts
MODEL MANAGEMENT
Runtime VerifierDeployment &
SchedulerMonitoring
Service
Model StoreTrained Model
StoreMetadata Store
APIModel Interface
Templates
RUNTIME ENVIRONMENTS
Distributed Computing Scientific Computing
SCRIPT-BASED OBJECT-BASED
Analytics modeling
Statistical analysis
Machine learning algorithms
Clustering Association Classification Regression
• Uni-/multi-variate analysis
• Bayesian
• Principle component analysis (PCA)
• K-means
• Hierarchical
• Expectation-Maximization (EM)
• DBSCAN
• A priori
• FP-Growth
• Eclat
• Decision tree
• K-nearest neighbor
• Support Vector Machine (SVM)
• Neural Networks
• ARIMA
• Logistic
• Regression tree
Copyright © 2016 Accenture All rights reserved. 11
DATA INTEROPERABILITY
MANAGE DATA CONSUMPTION ACROSS
DIFFERENT STORES AND DIFFERENT FORMATS
MobileApp
BI/ReportingSingle View of
Enterprise DataSupply Chain Management
Analytics
DATA VIRTUALIZATION
Data Federation Query Plan Enterprise SecurityETL
Streams
Network Sensor Readings
Usage Sensor Readings
Maintenance Logs
CustomerContacts
SiteInventory
ServiceOrders
Customer Service Records
Site Manager Contacts
REFERENCE ARCHITECTURE DATA COMPONENTS
INGESTION PIPELINES
Type Drive Workflow
Rule Driven ETLQuality and
ReconciliationSource-Based
Enrichment
Web ServicesRDBMS Warehouse Data Lake In-memory Flat Files NoSQL
RDBMS EDW Document Store Time Series DB
METADATA PROVIDES CONTEXT
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2. CAPTURE FOR REUSE
AN ILLUSTRATIVE EXAMPLE
Has Instance
Has Subclass
“Core relationship”
LEGEND
Analytics Type
Sensor Data KindProperty Optimal Store
Infra: Datastore
has_sensor_data_kind
has_reading_type
ConfigurationOptimal Store: RDBMS
Time SeriesOptimal Store: Columnar
Forecasted
Raw
Sensor Data
Sensor Readings
Sensor
has_sensor
_readings
has_sensor
_data
has_datastore
has_analytics
_type
has_sensor_readings
Pressure Instance A DataPressure Instance Ahas_sensor
_data
has_analytics_type
has_sensor_data_kind
PressurePressure Sensor
has_sensor
_readingshas_reading_type
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3. CONFIGURE
TO RAPIDLY DEPLOY
SCALE THE IoT PLATFORM
WITH NEW APPLICATIONS
SCALE NEW APPLICATIONS
WITH AN IoT PLATFORM
THE PLATFORM CORE
CAPABILITIES SMART WATER
12 weeksInitial real-time streamingBase set of components
SMART GRID
8 weeksLeverage existing baseContribute new components
OIL & GAS
2 weeksMain focus on domain specificHeavy reuse of components
Applications
Cloud
Data
Models
Devices
Reuse
Harvest
Harvest
Harvest
Reuse
Reuse
Copyright © 2016 Accenture All rights reserved. 14
IoT IS A PLATFORM OF PLATFORMS
NOT ABOUT “YOUR PLATFORM,” BUT
ABOUT AN ECOSYSTEM OF PLATFORMS
Social
Health
Enterprise Cloud
Telco
Industrial
Copyright © 2016 Accenture All rights reserved. 15
THE PROMISE OF IoT
DRIVING TO OUTCOMES
AND AN AUTONOMOUS
PULL ECONOMY
http://www3.weforum.org/docs/WEFUSA_IndustrialInternet_Report2015.pdf
FROM WORLD ECONOMIC FORUM REPORT*
2015: INDUSTRIAL INTERNET OF THINGS…
*WEF Report authored by Accenture
FROM
PRODUCTS TO SERVICES OUTCOMES AUTONOMY
• Improved asset utilization and tracking
• Operational cost reduction
• Equipment productivity enhancement
• Improved worker productivity, safety and working conditions
• Remote equipment operation
• New ‘as-a-service’ business -models (e.g. pay-per-use)
• Product / Service hybrids
• Software based services
• Data monetization
• Open APIs / Developer Networks
• Continuous demand-sensing
• Perceptive response
• Integrated human-machine / robot workforce
• Digital labor
• End to end automation
• Resource optimizationand waste reduction
• Pay-per-outcome models
• Shared risk
• New connected ecosystems
• Platform-enabled data marketplaces
• Products-as-a-platform
• Industry boundaries blur
OPERATIONAL
EFFICIENCY
NEW PRODUCTS,
SERVICES AND
BUSINESS MODELS
AUTONOMOUS
PULL ECONOMY
OUTCOME-BASED
ECONOMY