Big Data - Fresh Ideas for Your Business Value
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Transcript of Big Data - Fresh Ideas for Your Business Value
0 Copyright 2014 FUJITSU
Human CentricInnovation
Fujitsu Forum2014
19th – 20th November
1 Copyright 2014 FUJITSU
Big Data – Fresh Ideas for Your Business Value
Gernot FelsGlobal Services & Solutions Marketing, Fujitsu
2 Copyright 2014 FUJITSU
The Promise of Big Data
Discover hidden secrets
Predict opportunities
Identify and minimize unknown risks
Take better and faster decisions
Accelerate business processes
Increase performance and productivity
Improve efficiency and effectiveness
Profitability and competitive advantage
Better utilize our planet‘s resources
A convincing value proposition – For business and society.
3 Copyright 2014 FUJITSU
Entering new dimensions …
Versatile data sources –internal and external
DB
Text
Multi-media
Machine logs
Web logs
Social media
Internet
Sensors (GPS, RFID, …, IoT)
…
Versatility
4 Copyright 2014 FUJITSU
Entering new dimensions …
Versatile data sources –internal and external
DB
Text
Multi-media
Machine logs
Web logs
Social media
Internet
Sensors (GPS, RFID, …, IoT)
…
Various data types
Structured
Unstructured
Semi-structured
Poly-structured Variety
Versatility
5 Copyright 2014 FUJITSU
Entering new dimensions …
Versatile data sources –internal and external
DB
Text
Multi-media
Machine logs
Web logs
Social media
Internet
Sensors (GPS, RFID, …, IoT)
…
Various data types
Structured
Unstructured
Semi-structured
Poly-structured
Large data volumes
TB to PB
Volume
Variety
Versatility
6 Copyright 2014 FUJITSU
Entering new dimensions …
Versatile data sources –internal and external
DB
Text
Multi-media
Machine logs
Web logs
Social media
Internet
Sensors (GPS, RFID, …, IoT)
…
Various data types
Structured
Unstructured
Semi-structured
Poly-structured
Large data volumes
TB to PB
High velocity
Generation and processing
Real-time demands
Volume
Variety
Versatility
Velocity
7 Copyright 2014 FUJITSU
Entering new dimensions …
Versatile data sources –internal and external
DB
Text
Multi-media
Machine logs
Web logs
Social media
Internet
Sensors (GPS, RFID, …, IoT)
…
Various data types
Structured
Unstructured
Semi-structured
Poly-structured
Large data volumes
TB to PB
High velocity
Generation and processing
Real-time demands
Generate new value by correlation of data and new ways of analytics.
Volume
Variety
Versatility
Velocity
Value
8 Copyright 2014 FUJITSU
Big Data is the new oil
Volume
Variety
Versatility
Velocity
Value
Discover
Extract
Refine
Value
9 Copyright 2014 FUJITSU
Big Data is the new oil
Volume
Variety
Versatility
Velocity
Value
Discover
Extract, collect
Transform
Analyze, visualize
Decide,act
Discover
Extract
Refine
Value
10 Copyright 2014 FUJITSU
Big Data is the new oil
Volume
Variety
Versatility
Velocity
Value
Discover
Extract
Refine
Value
Infr
astr
uct
ure
Serv
ices
You need an infrastructure and services to make it happen.
How to get from Big Data to Big Value?
A lot of questions …
Which use case (business priority)?
Which data from which sources?
Is external data trustworthy, and always (free) available?
How to transform data into high quality?
How to explore data and discover its meaning?
What to look for? Which questions to ask?
Which actions to trigger? Are actions executable?
Which analytic methods?
How to visualize results effectively?
Which tools? How to use them?
How to ensure data security and privacy?
What about data retention and deletion?
Which skills?
Fresh ideas, deep knowledge of data, tools and intended results.
12 Copyright 2014 FUJITSU
Example: Retail
Fresh ideas step by step – to become more attractive and improve business.
13 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW Customers, partners, suppliers, products, revenues, margins
Too little insight.
14 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW
Mail, notes, call records
Customers, partners, suppliers, products, revenues, margins
Customer interaction, problems
New data sources, more insight.
15 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW
Mail, notes, call records
Social media
Web logs, click streams
Customers, partners, suppliers, products, revenues, margins
Customer interaction, problems
Customer sentiment, preferences, behavior
Foresee opportunities, prevent churn, web site optimization.
16 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW
Mail, notes, call records
Social media
Web logs, click streams
Customers, partners, suppliers, products, revenues, margins
Customer interaction, problems
Customer sentiment, preferences, behavior
Future demand, stock requirements
Always the right goods, at the right quantity in stock – in every store.
17 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW
Mail, notes, call records
Social media
Web logs, click streams
Customers, partners, suppliers, products, revenues, margins
Customer interaction, problems
Customer sentiment, preferences, behavior
Future demand, stock requirements
Optimized store layout
Optimum combinations of products in shelves.
18 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW
Mail, notes, call records
Social media
Web logs, click streams
Weather data
Customers, partners, suppliers, products, revenues, margins
Customer interaction, problems
Customer sentiment, preferences, behavior
Future demand, stock requirements
Optimized store layout
Weather affects purchase.
19 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW
Mail, notes, call records
Social media
Web logs, click streams
Weather data
Internet
Customers, partners, suppliers, products, revenues, margins
Customer interaction, problems
Customer sentiment, preferences, behavior
Future demand, stock requirements
Optimized store layout
Market, competition
Recognize general trends, opportunities, risks.
20 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW
Mail, notes, call records
Social media
Web logs, click streams
Weather data
Internet
Location data, RFID cards
Customers, partners, suppliers, products, revenues, margins
Customer interaction, problems
Customer sentiment, preferences, behavior
Future demand, stock requirements
Optimized store layout
Market, competition
Personalized coupons and ads in real-time
Improve customer experience.
21 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW
Mail, notes, call records
Social media
Web logs, click streams
Weather data
Internet
Location data, RFID cards
Machine logs
Customers, partners, suppliers, products, revenues, margins
Customer interaction, problems
Customer sentiment, preferences, behavior
Future demand, stock requirements
Optimized store layout
Market, competition
Personalized coupons and ads in real-time
Preventive maintenance
Foresee and prevent problems.
22 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW
Mail, notes, call records
Social media
Web logs, click streams
Weather data
Internet
Location data, RFID cards
Machine logs
Sensors (temperature)
Customers, partners, suppliers, products, revenues, margins
Customer interaction, problems
Customer sentiment, preferences, behavior
Future demand, stock requirements
Optimized store layout
Market, competition
Personalized coupons and ads in real-time
Preventive maintenance
HVAC control
Improve customer experience. Avoid spoiled goods.
23 Copyright 2014 FUJITSU
Example: Retail – Which data sources what for?
Transactional DB and DW
Mail, notes, call records
Social media
Web logs, click streams
Weather data
Internet
Location data, RFID cards
Machine logs
Sensors (temperature)
Cameras
Customers, partners, suppliers, products, revenues, margins
Customer interaction, problems
Customer sentiment, preferences, behavior
Future demand, stock requirements
Optimized store layout
Market, competition
Personalized coupons and ads in real-time
Preventive maintenance
HVAC control
Surveillance
Customer flow according to gender, age, time.
24 Copyright 2014 FUJITSU
Example: Retail – Not the end of the story …
… steadily looking for new creative ideas to move the business forward.
Insatiable appetite to analyze new sources …
25 Copyright 2014 FUJITSU
Big Data infrastructure challenges
Big Data requires new solution approaches.
Exponential growth of
data
Keep processing time constant while volumes increase
Store and process large data volumes at affordable cost
Ad-hoc queries andreal-time demands
Process continuously generated event streams
26 Copyright 2014 FUJITSU
Distributed Parallel Processing
DataNode
TaskTracker
DataNode
TaskTracker
DataNode
TaskTracker
NameNode
JobTracker
Clie
nt
Master
Slaves DFSConcept
Distribute data and I/O to many nodes
Local server storage
Move computing to where data resides
Shared nothing architecture
Data replication to several nodes
Benefits
High performance, fast results
Unlimited scalability
Fault-tolerance
Cost-effective (standard servers with OSS)
Examples
27 Copyright 2014 FUJITSU
Applications
Distilled Essence
What it is
Consolidated result of data transformation
Usually much smaller than initial data volumes
All essential information for use case included
Base for analytics
Where it is stored
Local disks in server cluster
…
DB allow better queries than file systems.
DistilledEssence
28 Copyright 2014 FUJITSU
Databases – Designed for Big Data
Flexible data model
Designed to be distributed
Schema-less (simple structure)
Easily cope with new data types
Change format of data without app disruption
Ease of app development
Data automatically spread across servers
Support distributed query execution
Designed for scale-out
Add server to cluster on demand
Standard server HW
Integrated caching
Fault tolerance
Reduce latency, increase throughput
Improve app responsiveness
Data replication (across cluster / DC)
Automatic recovery from failure
Various flavors
Graph DB
Key-value stores
Document stores
Columnar stores
NoSQL databases help overcome the limitations of RDBMS.
29 Copyright 2014 FUJITSU
Column-oriented DB
Row-oriented data store
Column-oriented data store
Millions of rows (records)
Many queries relate to small number of columns
Inefficient to read all rows
Reduced I/O, fast analysis
Access only relevant columns
Split column for parallel processing
High compression
Column contains only 1 data type
Often only few values per column (to store)
Typical compression factor 10-50
NoSQL databases help overcome the limitations of RDBMS.
Row-oriented storeKey Region Product Sales Vendor
0 Europe Mice 750 Tigerfoot1 America Mice 1100 Lionhead2 America Rabbits 250 Tigerfoot3 Asia Rats 2000 Lionhead4 Europe Rats 2000 Tigerfoot
Column-oriented storeKey Region Product Sales Vendor
0 Europe Mice 750 Tigerfoot1 America Mice 1100 Lionhead2 America Rabbits 250 Tigerfoot3 Asia Rats 2000 Lionhead4 Europe Rats 2000 Tigerfoot
SELECT SUM (Sales) GROUP BY Region
Data required
Data read, but not required
30 Copyright 2014 FUJITSU
Applications
Distilled Essence
What it is
Consolidated result of data transformation
Usually much smaller than initial data volumes
All essential information for use case included
Base for analytics
Where it is stored
Local disks in server cluster
HDD storage
AFA
…
What if immediacy matters?
DistilledEssence
31 Copyright 2014 FUJITSU
In-Memory Data Grid (IMDG)
Concept
Distributed in-memory store between apps and data
Any data object (suitable for apps)
Any data size (even > RAM capacity)
Scale-up, scale-out
Benefits
Optimal response of apps by reducing I/O
Performance as you grow
How to ensure business continuity?
Applications
DistilledEssence
RAM
RAM
RAM
IMDG
32 Copyright 2014 FUJITSU
In-Memory Data Grid (IMDG)
Concept
Distributed in-memory store between apps and data
Any data object (suitable for apps)
Any data size (even > RAM capacity)
Scale-up, scale-out
Persistent storage for automatic recovery
Benefits
Optimal response of apps by reducing I/O
Performance as you grow
Meet real-time demands, ensure business continuity.
Applications
DistilledEssence
RAM
RAM
RAM
IMDG
Snapshots
Change log
Persistence
33 Copyright 2014 FUJITSU
In-Memory Data Grid (IMDG)
Concept
Distributed in-memory store between apps and data
Any data object (suitable for apps)
Any data size (even > RAM capacity)
Scale-up, scale-out
Persistent storage for automatic recovery
Data mirror on other server for ultra-fast recovery
Use apps unmodified (optimization possible)
Pre-defined queries
Benefits
Optimal response of apps by reducing I/O
Performance as you grow
Meet real-time demands, ensure business continuity.
Applications
DistilledEssence
RAM
RAM
RAM
IMDG
Snapshots
Change log
Persistence
RAM
RAM
RAM
Mirror
34 Copyright 2014 FUJITSU
Benefits
In-Memory Database (IMDB)
Meet real-time demands for ad-hoc queries, …
Concept
Entire DB and operation in RAM of server(s)
No disk access for analysis
Scale-up, scale-out
Data access 1K-10K x faster than disk
Ad-hoc queries in real-time
Fast decisions by eliminating I/O
Applications
RAM
RAM
RAM
IMDB
DistilledEssence
35 Copyright 2014 FUJITSU
In-Memory Database (IMDB)
Concept
Entire DB and operation in RAM of server(s)
No disk access for analysis
Scale-up, scale-out
Persistent storage for automatic recovery
Data mirror on other server for ultra-fast recovery
Condition: Data size < RAM capacity
Benefits
Data access 1K-10K x faster than disk
Ad-hoc queries in real-time
Fast decisions by eliminating I/O
Meet real-time demands for ad-hoc queries, ensure business continuity.
Applications
RAM
RAM
RAM
RAM
RAM
RAM
Snapshots
Change log
Persistence
IMDBMirror
36 Copyright 2014 FUJITSU
Complex Event Processing (CEP)
Concept
Critical parameters
Collect and analyze continuously generated data
Rules for logical and temporal correlation
Event pattern matching
Trigger action in real-time
Throughput: 10K’s to M’s of events per sec
Latency: µs to ms (time from event input to output)
Event input
Ru
les
State transition
Apply Event output
ControlCEP Engine
Sensor
37 Copyright 2014 FUJITSU
Complex Event Processing (CEP)
Concept
Critical parameters
Collect and analyze continuously generated data
Rules for logical and temporal correlation
Event pattern matching
Trigger action in real-time
Throughput: 10K’s to M’s of events per sec
Latency: µs to ms (time from event input to output)
Streaming
Event input
Ru
les
State transition
Apply Event output
ControlCEP Engine
Routing
Options for acceleration
Data in RAM / IMDG (long time window)
Parallelization (large streams, many / complex rules)
CEP requires parallel processing and in-memory technologies.
Sensor Sensor Sensor
38 Copyright 2014 FUJITSU
μsecmsecsecminhrs
CEP (Complex Event Processing)
Response time
Conventionaltechnologies(e.g. RDBMS)
In-Memory(IMDB, IMDG)
GB
TB
PB
Dat
a vo
lum
eBig Data technologies at a glance
Select the right technology depending on volume and time requirements.
DistributedParallelProcessing
39 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
t
DB / DW
40 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
t
DB / DW
DistributedParallel
Processing
41 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
t
DB / DW
DistributedParallel
Processing
FS
NoSQL
DataReservoir
42 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
t
DB / DW
DistributedParallel
Processing
FS
NoSQL
AppsServicesQueries
….
VisualizationReporting
Notification
DataReservoir
43 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
t
DB / DW
DistributedParallel
Processing
FS
NoSQL
AppsServicesQueries
….
VisualizationReporting
Notification
DB / DW
DataReservoir
44 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
t
DB / DW
DistributedParallel
Processing
FS
NoSQL
AppsServicesQueries
….
VisualizationReporting
Notification
DB / DW
IMDGDataReservoir
45 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
t
DB / DW
DistributedParallel
Processing
FS
NoSQL
AppsServicesQueries
….
VisualizationReporting
Notification
DB / DW
IMDG
IMDB
DataReservoir
46 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
t
DB / DW
DistributedParallel
Processing
FS
NoSQL
AppsServicesQueries
….
VisualizationReporting
Notification
DB / DW
IMDG
IMDB
DataReservoir
47 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
t
DB / DW
DistributedParallel
Processing
FS
NoSQL
AppsServicesQueries
….
VisualizationReporting
Notification
DB / DW
IMDG
IMDB
NoSQL
DataReservoir
48 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
t
DB / DW
DistributedParallel
Processing
FS
NoSQL
AppsServicesQueries
….
VisualizationReporting
Notification
DB / DW
IMDG
IMDB
NoSQL
DataReservoir
49 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
tD
ata
in m
otio
n
DB / DW
DistributedParallel
Processing
FS
NoSQL
AppsServicesQueries
….
VisualizationReporting
Notification
DB / DW
IMDG
IMDB
NoSQL
ComplexEvent
Processing
50 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
tD
ata
in m
otio
n
DB / DW
DistributedParallel
Processing
FS
NoSQL
AppsServicesQueries
….
VisualizationReporting
Notification
DB / DW
IMDG
IMDB
NoSQL
ComplexEvent
Processing
51 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
Dat
a at
res
tD
ata
in m
otio
n
DB / DW
DistributedParallel
Processing
FS
NoSQL
AppsServicesQueries
….
VisualizationReporting
Notification
DB / DW
IMDG
IMDB
NoSQL
CEP
IMDG
52 Copyright 2014 FUJITSU
Un- / Semi-/Poly-
structureddata
Big Data infrastructure
IMDB
Data Sources Analytics Platform Access
Extract, Collect Clean, Transform Decide, ActAnalyze, Visualize
Consolidated data Distilled essence Applied knowledgeVarious data
IMDB
AppsServicesQueries
….
VisualizationReporting
Notification
DistributedParallel
Processing
IMDG
Dat
a at
res
tD
ata
in m
otio
n
FS
IMDG
DB / DW
DB / DWNoSQL NoSQL
IMDG
CEP
53 Copyright 2014 FUJITSU
How can help
Optimum solution for every situation
Combination of technologies
Infrastructure products
SW / MW (OSS, Fujitsu, ISV)
End-to-end services
Assessment, consulting
Solution design
Deployment, integration, maintenance
Attractive financing options
Sourcing options
Self-managed (on-premise)
Managed services (on- / off-premise)
Fujitsu Cloud
One-stop shop for Big Data: Reduce complexity, time and risk.
…
54 Copyright 2014 FUJITSU
Integrated Systems for Big Data Infrastructures
PRIMEFLEX for Hadoop PRIMEFLEX for Terracotta BigMemory
PRIMEFLEX for SAP HANA
Fujitsu Hadoop Services
Pre-integrated, ready-to-run: Fast deployment. Short time to production.
Analytical apps / templates (free)
Datameer (Visual Analytics)
Hadoop (HDFS, MapReduce)
Linux OS
PRIMERGY RX / CX
Brocade switches
Fujitsu Optimization Services
Terracotta BigMemory
Java Runtime Environment
Linux OS
PRIMERGY RX200 / CX4770
Local SSD
Network (1 / 10Gb Ethernet)
Fujitsu HANA Services
SAP HANA DB
Linux OS
PRIMERGY / PRIMEQUEST
Persistent storage
NW switches (multi-node only)
55 Copyright 2014 FUJITSU
Integrated Systems for Big Data Infrastructures
PRIMEFLEX for Hadoop PRIMEFLEX for Terracotta BigMemory
PRIMEFLEX for SAP HANA
Pre-integrated, ready-to-run: Fast deployment. Short time to production.
Datameer (Visual Analytics)
Hadoop (HDFS, MapReduce)
Linux OS
PRIMERGY RX / CX
Brocade switches
Fujitsu Optimization Services
Terracotta BigMemory
Java Runtime Environment
Linux OS
PRIMERGY RX200 / CX4770
Local SSD
Network (1 / 10Gb Ethernet)
Fujitsu HANA Services
SAP HANA DB
Linux OS
PRIMERGY / PRIMEQUEST
Persistent storage
NW switches (multi-node only)
Fujitsu Hadoop Services
Analytical apps / templates (free)
56 Copyright 2014 FUJITSU
Big Data – Important pillar of Fujitsu’s vision
Create innovationthrough people
Power business and society with information
Optimize ICT Systems from end to end
Big Data makes the Human-Centric Intelligent Society come true.
57 Copyright 2014 FUJITSU
Big Data in the Internet
http://www.fujitsu.com/fts/solutions/high-tech/bigdata/
Global Internet site under construction
Umbrella around topic and all building blocks
Links to individual building blocks and offerings
58 Copyright 2014 FUJITSU
Summary
Big Data offers enormous potential for business opportunities and value
If you ignore it, your competitor won’t !
It changes the way companies make decisions, do business, succeed or fail
New ideas and use cases
New types of analytics
New skills
New tools, SW and MW
New infrastructure concepts
New value
Fujitsu – A one-stop shop for Big Data
Enabler, making your dreams come true
Fujitsu – Turning fresh ideas into business value.
Thank you for listening
60 Copyright 2014 FUJITSU