Transcript Presentation - Geospatial World Forum

Convergence and Collaboration:
Transforming Business Process and Workflows
Steven Hagan, Vice President, Server Technologies
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Convergence & Collaboration:
Transform Processes, Workflow:
Why Happening
• Mandated:
– Corporate Acquisitions + Mergers
– Government Department Mergers
– Policy – “Place Matters”
• Voluntary - Leverage, Value Increase
– Enterprise Partners Collaborating Together
– Private Enterprise & Government & Academia Working Together
– Collaboration due to the Many and New Sources, Types of Data
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Convergence & Collaboration: Drivers:
• BIG Data – Terabytes, Petabytes, Exabytes, Zettabytes, Yottabytes
– Sensors, RFID, VIDEO, LIDAR, Raster, 3D, Terrain and City Models
– Internet of Things, Social Media, Tagged Data, History/Archive/Version Data
– SDIs, INSPIRE, Linked Open Data –- Persistent Relationships, Semantics, Ontologies
• BIG Hardware:
– CLOUD Platforms – Public and Private
– Cheaper, more powerful – Clusters of Commodity Servers, Virtualization: = Greener
– Massively parallel database machines – Software Enablement – e.g. Hadoop
• BIG Software –
– Real Time Analytics –Biggest value from fastest response – Streams and Events –Internet of Things; Spatially Aware – no separate GIS
– Location Enable All Applications: ERP, CRM, Business Intelligence, Public Sectors
– CyberSecurity
– Support Standards – W3C, OGC, ISO, Wide Range
– Engineered Systems – Fully installed and tested (Labor Cost is now Dominant Factor)
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Collaborations: Tools – Understand Data Quickly
Discovery & Predictive Analysis
Problem Classification
Sample Problem
Anomaly Detection
Given demographic data about a set of customers, identify
customer purchasing behavior that is significantly different from
the norm (Fraud?) . For Sensors, Events – problem? Good News?
Association Rules
Find the items that tend to be purchased together and specify
their relationship – market basket analysis
Clustering
Segment demographic data into clusters and rank the probability
that an individual will belong to a given cluster. For customers /
govt constituents, what do they want to know about?
Feature Extraction
Given demographic data about a set of customers, group the
attributes into general characteristics of the customers
F1 F2 F3 F4
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Global Digital Data Growth: Exceeds Storage Mfg
Growing leaps and bounds by 40+% YoY!
2009 = .8 Zetabytes
= .08 ZB Structured Data
= .72 ZB Unstructured Data
2020 = 35 Zetabytes
LEGEND
= 3.5 ZB Structured Data
Structured Data
= 31.5 ZB Unstructured Data*
Unstructured Data
(1 Zetabyte = 1 Trillion Gigabytes)
•
•
Chart conservatively assumes a constant 9:1 ratio of unstructured data vs. structured data (based upon IDC’s estimate that 90% of all digital data is unstructured).
Chart does not reflect IDC’s projection that unstructured data is currently growing twice as fast as structured data at the rate of 63.7% vs. 32.3% CAGR.
Source: IDC Digital Universe Study, A Digital Universe Decade – Are Your Ready?, 2010
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ILM: Hot/Cold Data Classification – Auto Archiving
Enhanced Insight into Data Usage: “heat map”
ACTIVE
Recently inserted,
actively updated
FREQUENT
ACCESS
Infrequently updated,
Frequently Queried
• Block and Segment level
statistics on last Read and last
Update
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DORMANT
Retained for long term analytics and
compliance with corporate policies
and regulations
Data Compression / Archiving; Repurposing
Remember: Versioning, Disaster Recover Copies
Warm Data
Hot Data
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1101001
3X
10X
15X
Columnar Query Compression
Columnar Archive Compression
Advanced Row Compression
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Archive Data
1010101011101010011010111000010100010110111
0101010010100100100001000101010110100101101
0011100001010010010100001001000010001010101
11001101110011000111010
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Copyright
© Oracle
2013, Oracle
its affiliates.
All reserved.
rights reserved.
Copyright
© 2012,
and/or and/or
its affiliates.
All rights
Confidential – Oracle Restricted
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Collaboration: Geospatial Value Core
Real-time Data Streams
External Data Sources
Transactional &
Operational Systems
Contents Repository
Databases
Web resources
Blogs, Mails, news
Automatic
Responses and
Publishing
SMS Console Alerts
Search, Presentation, Report,
Visualization, Query
EV Grid Management
Secured
Enterprise Data Management Infrastructure
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GeoSpatial
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Historical
Records,
Archives
Workflow Initiation
Documents
POIs
Demographics
Customer Data
Call Records
Real-time Dashboards
Ontology-driven Geospatial Applications Application Ontologies
Actionable Knowledge
National Map
Core Datasets
RDF & OWL Data
Geographic
Names
Spatial
Data
Theater
Raster
Data
• Simple Features
• GeoRaster
• Topology
• Networks
• Gazetteers
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Situational
Awareness
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•
•
•
•
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Data Integration
National Map schemas
Geographic names
Temporal
Naïve Geography
Targeting
Convergence: Breadth of Enterprise Data
Above / Across Stovepipes
End-user and Developer Environments
Developers
Data Integration
Business Users
Data Scientists
JDeveloper
Statistics
Discovery
Business Intelligence
Mining
Dashboards
Semantic Metadata Layer
Data Services
Big Data Sources
Structured
Data
Unstructured
Data
Streaming
Services
Event
Processing
Statistics
Text
Analytics
Data Mining
Natural Lang.
Processing
Data Management
NoSQL
Graph
Analytics
Applications
Spatial
App Services
Sound and Video
ODBC
Images
JDBC
Hadoop
Social Media
Relational
Web-log
Sessionization
and
Enrichment
Sentiment
Analysis
Reference
Architecture
Compression
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Security & Encryption
Vertical
Applications
Horizontal
Applications
Linking Your Cloud with other Clouds
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Convergence / Collaboration must Respect
Information Security and Privacy
Monitoring
Oracle Database
Encryption & Masking
Access Control
Monitoring
Blocking & Logging
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• Configuration Management
• Audit Vault
• Total Recall
Access Control
• Database Vault
• Label Security
Encryption & Masking
• Advanced Security
• Secure Backup
• Data Masking, Redaction
Privacy: Redacting Sensitive Data
Mask Application Data Dynamically –
Policy
enforced
redaction of
sensitive data
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Soc. Sec. #
DOB
PIN
115-69-3428
11/06/71
5623
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Copyright
© Oracle
2013, Oracle
its affiliates.
All reserved.
rights reserved.
Copyright
© 2012,
and/or and/or
its affiliates.
All rights
Confidential – Oracle Restricted
By Location, if Needed
Call Center
Operator
Payroll
Processing
Cloud Choices from Oracle: Public, Private
Run on private shared platform
or public SaaS model
Cloud Services
Public Clouds
SaaS
SaaS
PaaS
PaaS
IaaS
IaaS
Private Cloud
I
N
T
E
R
N
E
T
Oracle Technology in public clouds
•Enterprise deployment option
•Power
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3rd
party public clouds
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I
N
T
R
A
N
E
T
Apps
SaaS
PaaS
PaaS
IaaS
IaaS
Users
Oracle Private
Cloud Platform
Seeking Order through Standards
"We intend to complete development for a new suite of tools for developing the next generation of
applications. And there are several interesting things with the next generation of tools, but
perhaps the single most interesting thing about them is that for the first time a major application
company is going to commit to an absolute standards-based development environment.“
– Larry Ellison
•
ISO
–
TC 211
–
TC 204
Open Geospatial Consortium
–
Simple Features
–
GML
–
Web Services
De-facto Standards
–
SHP, MGE, DXF, KML
Professional Standards
–
ISPRS, FIG, WMO
Java, .NET, Flash
TAGGED METADATA – agree on tags
•
•
•
•
•
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SQL3/MM Spatial
Convergence & Collaboration:
Best Success Requires Complete Platforms
Big Data
Simplified
Spatial
Simplified
IT IT
Volunteered
Geographic
Information
Sensors
Streaming Data
Georeferenced
Video,
3D, LiDAR
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Support for
Open Standards
Spatial Database,
Application Server,
BI, tools
Support by
Leading Partner
solutions
Spatiallyenabled
Engineered
Systems
Deep
Analytics
Real-time Spatial
Event Processing
On Premise,
On Cloud,
Shared
Services
Dense
Visualization
Spatial Analysis
Shared GeoSpatial Services
Location Aware Everything
Oracle Engineered
Systems
Cloud
Computing
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Insert Information Protection Policy Classification from Slide 8