- Location data from sources like social media, sensors, and smart devices is increasingly important for improving city services, security, and operations in smart cities (paragraph 1, 2) - Oracle provides tools for managing and analyzing large volumes of spatial and location data using big data technologies like Hadoop and streaming data platforms to enable use cases like predictive analytics (paragraph 3, 4, 5, 19) - Oracle's spatial capabilities allow for indexing, visualization, and analysis of geospatial vector and raster data at scale, including tools for data preparation, spatial queries, and analyzing streaming location data (paragraph 10, 13, 14, 20)

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Big Data, Fast Data, Spatial Data
Making Sense of Location Data in a Smart City
Hans Viehmann
Product Manager EMEA
ORACLE Corporation
July 9, 2015

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Everyone uses and shares Location Data
Location Information in Smart Cities
1.259 ... N 53° 35.469, E 10° 01.261 ... ... N 53° 35.473, E 10° 01.263 ... ... N 53° 35.477, E 10° 01.265 ... N 53° 35.481, E 10
Where is ... How do I get to ...
Find me the nearest ... When is the bus coming?
Today I‘m at AGIT 2015
I have checked in at ... on Foursquare.

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Importance of Location Information
• Improved Citizen Services
– Better Public Transport
– Social Media Interaction
• Improved Citizen Security
– Predictive Policing
– Social Media Analytics
• Improved City Operations
– Streamlined Process Management
– Optimized Field Service
3
Some examples based on the Oracle City Platform

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Typical Big Data Challenges
VOLUME VELOCITY VARIETY VALUE
SOCIAL
BLOG
SMART
METER

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Using Hadoop to Address Big Data Challenges
The Apache Hadoop software library is a framework that
allows for the distributed processing of large data sets across
clusters of computers using simple programming models.
Hadoop is designed to scale up from single servers to
thousands of machines, each offering local computation and
storage. Rather than rely on hardware to deliver high-
availability, the library itself is designed to detect and handle
failures at the application layer, so delivering a highly-
available service on top of a cluster of computers, each of
which may be prone to failures.

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Benefit of Big Data Technologies
• Low cost and high horizontal scalability infrastructure
• Allowing storage of more data, more details over longer time periods
• Cost-effective way to analyse huge amounts of data
• Dealing with variable data by means of „schema-on-read“ capability
• Complementary to existing data warehouse technologies
6

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Conventional database or Big Data technologies
Typical technical decision criteria
0
1
2
3
4
5
Tooling maturity
Stringent Non-Functionals
ACID transactional
requirement
Security
Variety of data formats
Data sparsity
ETL simplicity
Cost effectively store low
value data
Ingestion rate
Straight Through Processing
(STP)
Hadoop
Relational

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MapReduce Paradigm – mostly batch oriented
SHUFFLE
/SORT
SHUFFLE
/SORT
MAP
MAP
MAP
MAP
SHUFFLE
/SORT
REDUCE
REDUCE
INPUT
2
INPUT
1
MAP
MAP
MAP
MAP
MAP
REDUCE
REDUCE
REDUCE
MAP
MAP
MAP
MAP
MAP
REDUCE
REDUCE
REDUCE
OUTPUT

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Data Architecture for Big Data
WarehouseFactoryReservoir
Events and Streaming
Data Platform
Data Discovery Lab
Analytic
Tools
Actionable
Information
Actionable
Insights
Actionable
Events
Actionable
Discoveries
Business
Data
Enterprise
Data
Other Data
Sources
Data
Streams
Social/Log
Data

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But does this work for geospatial data?
• Standard MapReduce works for geometric operations on single objects
– eg. determining the centroid
– Needs to deal with projections, complex operations such as buffering, ...
• More complex processing usually requires spatial indexing
– eg. spatial joins
• Spatial data usually comes in specific formats (Shapefiles, GeoJSON, ...)
• Needs to cope with location information which is only included implicitly
– requires geo-enrichment
• Visualization is very valuable for inspection of source data and results
10
Location Intelligence has specific requirements

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Oracle Big Data Spatial and Graph
Spatial Analysis
Features for:
• Location Data
Enrichment
• Proximity and
containment
analysis
• Vector and raster
data preparation
• Map visualization
Property Graph
Features for:
• Flexible,
schema-less
data storage and
maintenance
• Support of huge
volumes of
connected data
• In-memory
graph analytics

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Spatial Features – Technical overview
• Support for spatial data in 2D or 3D in various formats, geodetic or projected
• Support for geo-referenced imagery such as satellite images in many formats
• MapReduce framework for resolution of placenames and determination location
hierarchies, including GeoNames dataset as a reference
• Spatial indexing techniques for fast retrieval of spatial data
• Library of spatial operators for geometric analysis (inside, within distance, anyinteract,
...)
• Library of image processing functions (mosaic, reprojection, format conversion, analysis,
...)
• Console for visual analysis, indexing, processing
– Sample JEE application to be deployed in Jetty

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Example – aggregating tweets per state
Create Index on
spatial data in HDFS

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Example – aggregating tweets per state
Run Map Reduce
job to perform
categorization
based on spatial
hierarchy

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Example – aggregating tweets per state
Results in Console
“Tweets in May by
State”

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Examples: Raster data preparation
Mosaic images
Terrains and contours
Shaded reliefs
Pyramiding: layers at different resolution

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The Big Picture – Oracle Big Data Management System
SOURCES
DATA RESERVOIR DATA WAREHOUSE
Oracle Database
Oracle Industry
Models
Oracle Advanced Analytics
Oracle Spatial & Graph
Big Data Appliance
Apache
Flume
Oracle
GoldenGate
Oracle Event
Processing
Cloudera Hadoop
Oracle Big Data SQL
Oracle NoSQL
Oracle R Distribution
Oracle Big Data Spatial and
Graph
Oracle Database
In-Memory, Multi-tenant
Oracle Industry Models
Oracle Advanced
Analytics
Oracle Spatial & Graph
Exadata
Oracle
GoldenGate
Oracle Event
Processing
Oracle Data
Integrator
Oracle Big Data
Connectors
Oracle Data
Integrator

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Data Architecture for Big Data
WarehouseFactoryReservoir
Events and Streaming
Data Platform
Data Discovery Lab
Analytic
Tools
Actionable
Information
Actionable
Insights
Actionable
Events
Actionable
Discoveries
Business
Data
Enterprise
Data
Other Data
Sources
Data
Streams
Social/Log
Data

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Geospatial data from positioning sensors
„Fast Data“ – Streaming Data and Event Processing
• Increasing numbers of sensors deliver location data
– Streaming data: continuous, time ordered, does not end
• May be hard to process in realtime using relational technologies
• Typical use case for Event Processing / Event-Driven Architectures
– Focus on changes in data rather than the individual data point
– Filtering, Aggregation, Correlation, etc., as well as spatial analysis on streaming data
• Lightweight engines supporting distributed pre-processing
– Reducing network load by moving pre-processing close to the source, eg. RFID
Scanner
19

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Oracle Stream Explorer – High-level Architecture
20
CEP Engine
Query
Query
InputAdapter
OutputAdapter
event
event
event
Real-time event data
Context-aware filtering, correlation,
aggregation and processing of data
Processed business events for
downstream applications
event
event
event
Sensors
Backend
Applications

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Event Processing – Modelling of Event Processing Networks
21
SELECT vehicleId
FROM in-channel [now] gps, ContextualServiceData route,
WHERE inside@spatial(gps.location, route.geometry) = false

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Summary
• New technologies have evolved over the last years to address Big Data
challenges for Smart Cities
– Dealing with larger volumes of unstructured or semi-structured data
– Managing streaming data
– Using semantic technologies to address interoperability
• Oracle provides spatial data management capabilities and location analytics
– On data warehouses as well as on Big Data platforms such as hadoop
– Including 2D, 3D, vector, raster or point cloud support as well as visualization
– Including Geo-enrichment to Big Data environments and semantic technologies
– Including spatial analysis on streaming data
22

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• „Introducing Oracle Big Data Spatial
and Graph“
• Jim Steiner,
VP Product Management
• 12.00pm – 1.00pm US CDT
IOUG Spatial SIG Techcast on July 21, 2015

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Further information
• Overview on oracle.com
– http://www.oracle.com/database/big-data-spatial-and-graph
• Oracle Technology Network
– http://www.oracle.com/technetwork/database/database-technologies/bigdata-
spatialandgraph
• Big Data Spatial and Graph blog
– http://blogs.oracle.com/bigdataspatialgraph
• „Oracle Spatial and Graph“ (!) group on LinkedIn
• ... or try it out using the latest „Big Data Lite“ VM (v4.2)
– Available for download here
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