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Aircraft Data Management
Information to Insight
Oracle OpenWorld 2014
October 1 , 2014
Copyright © 2014, Oracle and/or its affiliates. All rights reserved. |
Safe Harbor Statement
The following is intended to outline our general product direction. It is intended for
information purposes only, and may not be incorporated into any contract. It is not a
commitment to deliver any material, code, or functionality, and should not be relied upon
in making purchasing decisions. The development, release, and timing of any features or
functionality described for Oracle’s products remains at the sole discretion of Oracle.
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal/Restricted/Highly Restricted
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Agenda
1
Introductions
2
Aircraft Data Management
3
Oracle Airline Data Model
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Introductions
Vijay Anand
Senior Director & Global Lead
Travel, Transportation & Logistics Industries, Oracle Corporation
Sudip Majumder
Senior Director & Head of Industry DW Development, Oracle Corporation
Michael Parsons
Global Industry Solution Director - MRO, Oracle Corporation
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Oracle Powers Travel and Transportation Industries
Logistics Service Providers
Hospitality
Ports & Shipping
Aviation
Rail
 20 of the Top 20 Airlines
 17 of the Top 20 Hotels
 20 of the Top 20 Third Party Logistics Providers
 8 of the Top 10 Ports
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Oracle in Travel & Transportation Industries
Airlines
Airports
Ports
LSPs
Shipping
Lines
Rail / Metro
Hospitality
Thailand
Hyatt
Heathrow Express
Chengdu
SinoTrans
Copyright © 2014, Oracle and/or its affiliates. All rights reserved. |
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Agenda
1
Introductions
2
Aircraft Data Management
3
Oracle Airline Data Model
Copyright © 2014, Oracle and/or its affiliates. All rights reserved. |
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Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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The Aviation Data Challenges
Connected
Complex
Cognitive
• Sensors
• RFID / Wireless
• Automated
• Statistics
• Visualization
• Analytics
• Predictions
• Transformation
• Elaboration
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Harness the Data to Drive Improvement
$1,200 Average Cost Of
Maintenance Per Flight Hour
50 million Annual Flying Hours
$60 Billion
Source: IATA, Airline Maintenance Cost Executive Commentary, January 2011
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Harness the Data to Drive Improvement
1%
Improvement
in Maintenance Efficiency
Cost Saving of
$60 Million
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Improving Operational Insight to Deal with Disruptions
• Adopt a proactive approach to ground time –
complete awareness of aircraft status,
maintenance requirements, defects, logistics
and operational capabilities
• Fault First - minimize downtime by beginning
before the aircraft arrives
• Combining aircraft systems data with
unstructured data such as pilot reports, crew
reports & passenger feedback
• Need to make more real time decisions with
fewer engineers, ensure effective collaboration
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Improving Maintenance Effectiveness
• The ability to modify aircraft maintenance
check times and content, based on actual
performance data
• Manage data to substantiate removals and
component life / hard times extension – use
performance trending as removal trigger
• Technician and Operations interaction and
ability to diagnose and rapidly dispatch an
aircraft
• Adopting a different maintenance approach
“Predict and Prevent” rather “Find and Fix”
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Challenges
Effective Trouble Shooting
Schedule Disruptions
On Time Performance
Diagnostic Support
Technical Dispatch Reliability
Aircraft Swaps
Delays Due to Lacks of parts
MCC
OCC
Availability of
Spares
LM
ENGR
NFF Rates
Reliability
Analysis
Aircraft Sensor Data
MEL Clearance Time
Maintenance History
Delays and Cancelations Due to Maintenance
Maintenance Scheduling Effectiveness
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Why Aren’t These Challenges Being Met?
• Data capture and analysis often
manual - islands of information
• Lack of visibility across
operational, technical and logistics
data to make “right-time”
operational, tactical and strategic
decisions
• Disconnected systems supporting
siloed asset team members
• Limited team collaboration
Capture
Analyze
Execute
“Total asset
awareness”
“Right-time analysis
and decisionmaking”
“Timely execution”
Maintenance &
Engineering
Operations
Inventory &
Logistics
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Benefits of Integrated Aircraft Data Management
Transforms data into
insightful, actionable
information
Supports cost effective
maintenance assessment
and execution
Aligns Engineering and
Maintenance with
Operations to support on
time performance & reduce
operational interruptions
Enables assessment of
performance and supports
identification of
improvement opportunities
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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A Practical Approach
Discover insights not visible through traditional business intelligence approaches
Proper Input
Practical Outputs
Actionable Insight into
Operational Systems
Sensor
Data
Calculations/Algorithms
for Ad Hoc Analysis
Operations
Data
Asset
Data
Engineering
Data
KPI Dashboards with
Drill Down Capability
Performance
Data
Reports
Real Results
Sample Outputs
REDUCED
• On Time Performance
• First Time Fix %
Maintenance &
Inspection Costs
• Average MEL
Clearance time
INCREASED
• Value of Loans &
Borrows
Collaboration between
M&E and Operations
• Maintenance Priorities
• Spares & Line
Capabilities
REDUCED
Aircraft disruptions and
down time
Maintenance
Data
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Capturing the Necessary Information
The Oracle Aircraft Data Management Solution will leverage the existing data acquisition
instrumentation layer & industry specific data model
Sensor
Sensor
Fault Data
FDAU
CFR /
ACARS/
CMS/
QARS
Sensor
Sensor
Sensor
Sensor
Aggregator
FDAU
Data Acquisition
System
LM
MRO OPS
INV
AHM
FDAU = Flight Data Acquisition Unit
FIN
QARS = Quick Access Recorders
CFR = Current Flight Report
CMS= Central Maintenance System,
ACARS = Aircraft Communications Addressing and Reporting System
Protocol
Oracle Aircraft
Data Management
Solution
Operational
& Historical
Operations &
Maintenance
Data
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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-
AIRCRAFT
MEL Advisory
Specific data request
Decision making
Help to pilots
Automatically Sent Reports
DMU reports
CMS/CFDS reports
-
- Post Flight Report (PFR)
- Current Flight Report (CFR)
- Real Time Failure messages
- BITE report (e.g. Trouble shooting data)
- Avionics Configuration Reports
- Servicing Report
- ECAM warnings
- Class 3 reports
Aircraft Cruise Performance Report
Engine Trim Balance
Engine Start Report
Engine Divergence Report
Engine Gas Path Advisory Report
Engine On Request Report
Engine Mechanical Advisory Report
Engine Run up Report
Engine Take-Off Report
APU Shutdown Report
Engine Cruise Report
APU Main Engine Start
APU idle Report
Hard Landing/Structural Load Report
Environmental Control System Report
Ram Air Turbine Test Report
System conf report (P/N, Hw/Sw…)
Free text
Airline
Applications
Unscheduled maintenance
Preparation
DMU = Data Maintenance Unit
CMS= Central Maintenance System
CFDS = Centralized Fault Data System
Manually Sent Reports
Maintenance Telex:
Maintenance
Department
Speeds / temperatures
Engines data
Oil data
Compass error report:
Snag report:
Compass heading
ADIRU heading
Errors
Technical malfunction
Diversion report
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Monitoring
Flight crew monitoring
APU health monitoring
Aircraft Performing Monitoring
Engine condition monitoring:
Engine Trend Monitoring
Engine Exceedance Monitoring
Recording / Statistics
Data recording
Maintenance Log History
Special investigation
Trouble shooting
Hard Landing Detection
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Agenda
1
Introductions
2
Aircraft Data Management
3
Oracle Airline Data Model
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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General Thoughts on Implementing This Next Generation
DW (DW 2.0) – Wisdom (1/…)
• Don’t skip over Algebra to get to Differential Equations
• Use Practical Examples that Relate to their Life, not “academic truth” Transfer
Ownership
– There are VERY few referees in the Hall of Fame
• Enterprise Shared Information IS A NEW PARADIGM
– We’re here to drain the swamp, but our user community are good at fighting alligators
• We’re here to productively implement the next gen DW, NOT win converts to our
religion
• We’re dealing with people, not technology, problems
– Recognize each individual has individual objectives
– Jerks are people, too (and sometimes smart people)
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Information Management Reference Architecture
Data Reservoir & Enterprise Information Store – complete view
Data Sources
Enterprise
Performance
Management
Structured
Data
Sources
Data Ingestion
Access & Performance Layer
Past, current and future interpretation of
enterprise data. Structured to support
agile access & navigation
•
•
•
Operational Data
COTS Data
Streaming & BAM
Master &
Reference
Data Sources
Foundation Data Layer
Immutable modelled data. Business
Process Neutral form. Abstracted
from business process changes
Raw Data Reservoir
Immutable raw data reservoir
Raw data at rest is not interpreted
SMS
Docs
Web & Social Media
Project based data stores
to support specific
discovery objectives
Pre-built &
Ad-hoc
BI Assets
Information
Services
Information Interpretation
Rapid Development Sandboxes
Discovery Lab Sandboxes
Content
Virtualisation &
Query Federation
Data Engines &
Poly-structured
sources
Project based data
stored to facilitate rapid
content / presentation
delivery
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Data
Science
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The Oracle Airline Data Model
Oracle Airline Data Model
Derived Tables
Foundation
Layer
Analytic
Layer
Presentation
Layer
• Industry-standard compliant based Enterprise-wide
Data Model
– Over 370+ tables and 8500+ columns
– Over 250+ industry measures and KPIs
• Contains Logical and Physical Data Models Third
Normal Atomic, Dimensional Schema
• Industry specific Airlines Measures and KPI
• Pre-built OLAP cubes, Mining Models & Reports
• Automatic Data Movement Among Layers
• Extensive business intelligence metadata
• Easily extensible and customizable
• Usable within any GDS, GCS Applications
• Central repository for atomic level data
• Complete metadata (end-to-end)
• Rapid implementation
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Oracle Industry Data Model
A DB Option – Supportable, Upgradable, Patchable, Licensable, Unique
Release Cycle, OUI Install, Security , Code Coverage, Multiple Platform
Porting, NLS Compliant, Dedicated DB Dev team
• Better Business Insight
– Industry specific data model
– Based on industry standards
– Packaged advanced analytics
• Extreme Performance
– Improve query performance
10-100x with Exadata
• Fast Time-to-Value
– Jumpstart development
– Lower cost, risk, and complexity
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Oracle Big Data Management System
Oracle Big Data SQL
Cloudera Hadoop
Oracle Big Data
Connectors
Oracle NoSQL
Database
Oracle R Advanced
Analytics for Hadoop
Oracle R Distribution
Oracle Spatial & Graph
Oracle
Exadata
SOURCE
S
Big Data Appliance
B
Oracle Data
Integrator
Oracle Database
Oracle Database
Oracle Industry
Models
Oracle
Advanced
Security
Oracle Advanced
Analytics
Oracle
Industry Data
Models
Oracle Spatial &
Graph
Oracle
Advanced
Analytics
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Traditional Big Data Appliance – Lots of different things !!!
• Hadoop- which runs map reduce
program on files stored in HDFS.
• Hue/Eclipse-GUI tool to manage
hadoop.
• JAQL- Java query language High
level access to map reduce.
• Hive, Hbase, mahout, amazon
webserver-High level interfaces.
• Flume/Scribe-Unstructured data
collectors.
• Sqoop/Hiho- to load and extract
data from HDFS to relational.
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Use R enterprise to create mining model
In R enterprise we can view the mining results directly
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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OADM Advanced analysis with Social Media Data
Now we can do advanced analysis in BIEE
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Optimized for Oracle DB Technology
Enabling Maximum Performance & Scalability
Data Warehousing
• Partitioning
•
•
Base and derived tables all partitioned by default
Also aggregate materialized views corresponding to base
• Compression
Analytical Preparation
•
•
•
Used for all base, derived and aggregates tables
Significantly reduces disk utilization
Improves large data movement performance
• Parallel Execution
OLAP
•
Parallel query and parallel DML features leveraged by
all base, derived and aggregate tables
• Materialized Views
Data Mining
•
•
All aggregate tables are either MV or its derivatives
Partition change tracking supports fast refresh of MV
• Intra-ETL
•
Utilizes pipeline function for in-memory processing
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Aircraft Data Management
Business Intelligence
Airlines Data Model
Exadata
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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With an Advanced Architecture
Visualize &
Analyze
Analytics & Reports
(OBIEE, Oracle
Enterprise R)
Maintenance Operations
Maintenance Productivity
Inventory & Supply
Financial Performance
Maintenance Quality
Planning & Forecasting
Reliability
Customer Satisfaction
People, Skills &
Qualifications
Compliance & Safety
Oracle Big Data Appliance
Organize
Hotspot JavaVirtual
Machine
Oracle R Distribution
Oracle NoSQL
Enterprise Manager
Plug-In
Cloudera’s Distribution
/ Apache & Hadoop 4
SOA Abstraction Layer
Process Manager
Acquire
Oracle Data Integrator
ELT/ETL
Data Transformation
Bulk Data Movement
Data Lineage
Stream
Complex Event Processing
Business System Integration
(SOA- BEPEL)
Oracle Exadata
Cloudera Manager 4
Oracle Airline Data
Model
Historical Enterprise
DW
Operational Data
Store
Pre-built Analytics
Extreme Performance
Complete redundancy
Service Bus
Replication
Rules Engine
MRO
ERP
Supply Chain
Flight Operations
Reliability
Inventory
Data Services
Oracle GoldenGate
Real-time Data
Log-based CDC
Data Verification
Alerting
(BAM)
Data Federation
Oracle Data Quality
Data Profiling
Data Parsing
Data Cleansing
Apache Flume
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Volume, Velocity,
Variety
Match and Merge
Aircraft On Board
Systems
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Oracle Airline Data Model for Aircraft Data Management
• Oracle is the only vendor with the
complete stack to deliver an Aircraft
Data Management solution …
• … that will enable airlines to deploy
automated analytical processes …
• … across Aircraft, Maintenance,
Operations, Logistics, Passenger &
Revenue …
• … in order to make optimal near realtime, tactical and strategic decisions
Maintenance
Operations
Maintenance
Productivity
People, Skills &
Qualifications
Customer
Satisfaction
Maintenance
Quality
Oracle Airline
Data Model
Reliability
Planning &
Forecasting
Financial
Performance
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
Compliance &
Safety
Inventory &
Supply
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Oracle Powers Travel and Transportation Industries
Logistics Service Providers
Hospitality
Ports & Shipping
Aviation
Rail
 20 of the Top 20 Airlines
 17 of the Top 20 Hotels
 20 of the Top 20 Third Party Logistics Providers
 8 of the Top 10 Ports
Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |
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Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal/Restricted/Highly Restricted
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