ibm cec big data 2011 06-11 final
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COMMON Europe Congress 2012 - ViennaTRANSCRIPT
© 2012 IBM Corporation
Smarter Computing - Big Data11 June 2012
Dipl.Ing.W olfgang Nimführ
Information Agenda Executive ConsultantBig Data Tiger TeamIBM Software Group Europe
© 2012 IBM Corporation2
Legal Disclaimer
© IBM Corporation 2012. All Rights Reserved.
The information contained in this publication is provided for informational purposes only. While efforts were made to verify the completeness and accuracy of the information contained in this publication, it is provided AS IS without warranty of any kind, express or implied. In addition, this information is based on IBM’s current product plans and strategy, which are subject to change by IBM without notice. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, this publication or any other materials. Nothing contained in this publication is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software.
References in this presentation to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in this presentation may change at any time at IBM’s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. Nothing contained in these materials is intended to, nor shall have the effect of, stating or implying that any activities undertaken by you will result in any specific sales, revenue growth or other results.
Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal ob ligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion.
© 2012 IBM Corporation3
Welcome to the Instrumented Interconnected World!
INSTRUMENTED
INTERCONNECTED
INTELLIGENT
Build a Smarter PlanetBuild a Smarter Planet
© 2012 IBM Corporation4
Why Big Data
“most enterprise data warehouse (EDW) and BI teams currently lack a clear understanding of big data technologies… They are increasingly asking the question, "How can we use big data to deliver new insights?"Gartner 2012
Searches for "big data" on Gartner's website have increased 981% between March 2011 -October 2011
Big Data - We are at a huge inflection point and this opportunity comes only once.
We are declaring that IBM is the #1 leader in providing a Big Data platform.
Alyse Passarelli, WW VP IM Sales Jan 10th 2012
“Big Data: The next frontier for innovation, competition and productivity”McKinsey Global Institute 2012 will be the year of 'big data' BBC
Nov 30 2011
Big Data will be the CIO Issue of 2012
IDC Prediction 2012 report
© 2012 IBM Corporation55
2009 800,000 petabytes
2020 35 zettabytes
as much Data and ContentOver Coming Decade
44xBusiness leaders frequently make decisions based on information they don’t trust, or don’t have
1 in 3
83%of CIOs cited “Business intelligence and analytics” as part of their visionary plansto enhance competitiveness
Business leaders say they don’t have access to the information they need to do their jobs1 in 2
of CEOs need to do a better job capturing and understanding information rapidly in order to make swift business decisions
60%Organizations Need Deeper Insights
Of world’s datais unstructured
80%
The Information Explosion in Data and Real World Ev ents
© 2012 IBM Corporation6
Data AVAILABLE to an organization
Data an organization can PROCESS
� The percentage of available data an enterprise can analyze is decreasing proportionately to the available to it
� Quite simply, this means as enterprises, we are getting “more naive” about our business over time
� We don’t know what we could already know….
The Blind Spot
The resulting explosion of information creates a ne ed for a new kind of intelligence
Missing Insights and Analytics
© 2012 IBM Corporation7
Challenge Study a Large Volume and Variety of Data to Find Ne w Insights
Identify criminals and threats from disparate video, audio, and data feeds
Make risk decisions and frauds detection based on real-time transactional data
Predict weather patterns to plan optimal wind turbine usage, and optimize capital expenditure on asset placement
Support medical diagnosticsDetect life-threatening conditions
Multi-channel customer sentiment and experience a analysis
© 2012 IBM Corporation8
How do you address the challenges presented by empo wered market participants generating mountains of data?
Source: 1 – Barrera, Clod and Wojtowecz. “Cloud Leads Five Storage Trends for 2011.” CIO. Januar y 27, 2011 . 2 – http: //www.i nternetworlds tats .com/stats.htm. 3 – http: //www.abiresearch.com/pr ess/3584-More+than+Seven+Trillion+SMS+Messages+Will+Be+Sent+in+2011
Can you capture data generated by these interactions?
Can you turn that data into insights to predictcustomer / competitive / market behavior?
Can you do it in real-time?
Leveraging Big Data Analytics
© 2012 IBM Corporation9
How does Big Data Analytics impact business?
Source: Outperforming in a Data Rich, H yper Connect ed W orld, an IB M Center for Applied Insights research report. Copyright © IBM 2012
Deploying these competencies extensively correlates to long-term financial performance
Listen and Anticipate consistently deployed across the enterprise correlate to higher compound annual growth rates (5-year CAGR, 2005-2010)
© 2012 IBM Corporation10
Information Management Capabilities
• Event triggers• Customer Profitability
analysis• Complaint Data• Voice to Text Data• Transactional data• Policy & Procedure
data
DashboardsData Scientist Call CenterClient Mgr
• Relationship / risk data
• Product profitability data
• Email correspondents
• Company website logs
Internal Data
Big Data Analytics
Hub
Natural Language
External Data
• Web Logs• Twitter feeds• Facebook chats• YouTube Video• Blogs/Posting• Appraisal data• Credit bureau data
Leveraging Big Data Analytics can improve Experienc e
……
© 2012 IBM Corporation11
On 16 Feb 2011 the IBM Watson system won Jeopardy!
Can we design a computing system that rivals a human’s ability to answer questions posed in natural language, interpreting meaning and context and
retrieving, analyzing and understanding vast amounts of information in real-time?
© 2012 IBM Corporation12
IBM Watson‘s project started 2007
“IBM is not in the entertainment business. But we are in the business of technology and pushing frontiers.”
David Shepler, IBM Research Program Manager
• Project started in 2007, lead David Ferrucci
• Initial goal: create a system able to process natural language & extract knowledge faster than any other computer or human
• Jeopardy! was chosen because it’s a huge challenge for a computer to find the questions to such “human” answers under time pressure
• Watson was NOT online!
• Watson weighs the probability of his answer being right – doesn’t ring the buzzer if he’s not confident enough
• Which questions Watson got wrong almost as interesting as which he got right!
© 2012 IBM Corporation13
Different Types of Evidence: Keyword Evidence
celebrated
India
In May 1898
400th anniversary
arrival in
Portugal
India
In May
Garyexplorer
celebrated
anniversary
in Portugal
Keyword MatchingKeyword Matching
Keyword MatchingKeyword Matching
Keyword MatchingKeyword Matching
Keyword MatchingKeyword Matching
Keyword MatchingKeyword Matching
arrived in
In May, Gary arrived in India after he celebrated hisanniversary in Portugal .
In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India.
Evidence suggests “Gary”is the answer BUT the system must learn that keyword matching may be weak relative to other types of evidence
© 2012 IBM Corporation14
On 27th May 1498, Vasco da Gama landed in Kappad Beach
On 27th May 1498, Vasco da Gama landed in Kappad Beach
celebrated
May 1898 400th anniversary
arrival in
In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India.
Portugal
landed in
27th May 1498
Vasco da Gama
Temporal Reasoning
Statistical Paraphrasing
GeoSpatialReasoning
explorer
On 27th May 1498, Vasco da Gama landed in Kappad BeachOn the 27 th of May 1498, Vasco da
Gama landed in Kappad Beach
Kappad Beach
Para-phrases
Geo-KB
DateMath
India
Stronger evidence can be much harder to find and score.
The evidence is still not 100% certain.
�Search Far and Wide
�Explore many hypotheses
�Find Judge Evidence
�Many inference algorithms
Different Types of Evidence: Deeper Evidence
© 2012 IBM Corporation15
Question100s Possible
Answers
1000’s of Pieces of Evidence
Multiple Interpretations
100,000’s scores from many simultaneous Text Analysis Algorithms100s sources
. . .
HypothesisGeneration
Hypothesis and Evidence Scoring
Final Confidence Merging & Ranking
SynthesisQuestion &
Topic Analysis
QuestionDecomposition
HypothesisGeneration
Hypothesis and Evidence Scoring
Answer & Confidence
DeepQA: Massively Parallel Probabilistic Evidence-Based Arc hitecture
© 2012 IBM Corporation16
Maximum Benefit Requires Combining Deep and Reactive Analytics
Predictive Analytics
100,000 records/sec, 6B/day10 ms/decision
6 PB f or Deep Analytics
DeepQA
100s GB for Deep Analytics
3 sec/decision
1 PB training corpus
Smart Traffic
250K GPS probes/sec
630K segments/sec
2 ms/decision, 4K vehicles
Real time Optimization
100,000 updates/sec,5 ms/decision
Round-trip automation10 PB f or Deep Analytics
Dat
a S
cale
Decision FrequencyOccasional Frequent Real-time
Traditional Data Warehouse and BusinessIntelligence
Integration
Inte
grat
ion
yr mo wk day hr min sec … ms µs
Exa
Peta
Tera
Giga
Mega
Kilo
Feedback
Reactive Analytics
Reality � Actions
Fast
Observations
History
DeepAnalytics
Deep
Hypotheses � Predictions
Integration
© 2012 IBM Corporation17
Traditional Approach vs Big Data Approach
IT
Structures the data to answer that question
IT
Delivers a platform to enable creative discovery
Business
Explores what questions could be asked
Business Users
Determine what question to ask
Monthly sales reportsProfitability analysisCustomer surveys
Brand sentimentProduct strategyMaximum asset utilization
Big Data ApproachIterative & Exploratory Analysis
Traditional ApproachStructured & Repeatable Analysis
© 2012 IBM Corporation18
Big Data use cases across all industries
Utilities� Weather impact analysis on
power generation� Transmission monitoring� Smart grid management
Retail� 360° View of the Customer� Click-stream analysis� Real-time promotions
Law Enforcement� Real-time multimodal surveillance� Situational awareness� Cyber security detection
Transportation� Weather and traffic
impact on logistics and fuel consumption
Financial Services� Fraud detection� Risk management� 360° View of the Customer
IT� Transition log analysis
for multiple transactional systems
� Cybersecurity
Health & Life Sciences� Epidemic early warning
system� ICU monitoring� Remote healthcare monitoring
Telecommunications� CDR processing� Churn prediction� Geomapping / marketing� Network monitoring
© 2012 IBM Corporation19
Monetizing Relationships - not just Transactions
Social Network PublicDatabase
Amy Bearn
32, Married, mother of 3,Accountant
Telco Score: 91CPG Score: 76Fashion Score: 88
Telc
oco
mpa
ny
How v aluable is Amy to my mobile phone network? How likely is she to switch carriers? How many other customers will f ollow
Merged NetworkCalling Network
How v aluable is Amy to my retail sales? Who does she influence? What do they spend?
Telc
o R
eta
il
© 2012 IBM Corporation20
Personal Attributes• Identifiers : name, address, age, gender, occupation…• Interests : sports, pets, cuisine…• Life Cycle Status : marital, parental
Personal Attributes• Identifiers : name, address, age, gender, occupation…• Interests : sports, pets, cuisine…• Life Cycle Status : marital, parental
Products Interests• Personal preferences of products• Product Purchase history• Suggestions on products & services
Products Interests• Personal preferences of products• Product Purchase history• Suggestions on products & services
Social Media based 360-degree
Consumer Profiles
Life Events• Life-changing events : relocation, having a baby, getting married, getting divorced, buying a house…
Life Events• Life-changing events : relocation, having a baby, getting married, getting divorced, buying a house…
Monetizable intent to buy products Life Events
Location announcementsIntent to buy a house
I'm thinking about buying a home in Buckingham Estates per a recommendation. Anyone have advice on that area? #atx #austinrealestate#austin
I'm thinking about buying a home in Buckingham Estates per a recommendation. Anyone have advice on that area? #atx #austinrealestate#austin
Looks like we'll be moving to New Orleans sooner than I thought.Looks like we'll be moving to New Orleans sooner than I thought.
College: Off to Stanford for my MBA! Bbye chicago!College: Off to Stanford for my MBA! Bbye chicago!
I'm at Starbucks Parque Tezontle http://4sq.com/fYReSjI'm at Starbucks Parque Tezontle http://4sq.com/fYReSj
I need a new digital camera for my food pictures, any recommendations around 300?
I need a new digital camera for my food pictures, any recommendations around 300?
What should I buy?? A mini laptop with Windows 7 OR a Apple MacBook!??!
What should I buy?? A mini laptop with Windows 7 OR a Apple MacBook!??!
Timely Insights• Intent to buy various products • Current Location• Sentiment on products, services, campaigns• Incidents damaging reputation• Customer satisfaction/attrition
Timely Insights• Intent to buy various products • Current Location• Sentiment on products, services, campaigns• Incidents damaging reputation• Customer satisfaction/attrition
Relationships• Personal relationships : family, friends and roommates…• Business relationships: co-workers and work/interest network…
Relationships• Personal relationships : family, friends and roommates…• Business relationships: co-workers and work/interest network…
Sample: Big Data 360 °°°°Lead Generation
© 2012 IBM Corporation21
Micro-segmentation of consumers by hobbies
Micro-segmentation of consumers by hobbies
Micro-segmentation of product intents by
occupation
Micro-segmentation of product intents by
occupation
Real-time product intents enriched with consumer attributes
Real-time product intents enriched with consumer attributes
Real-time tracking by micro-segmentation
Real-time tracking by micro-segmentation
Integration across Social Media sitesIntegration across Social Media sites
Entries contain promotional messages, wishful thinking, questions, etc
Entries contain promotional messages, wishful thinking, questions, etc
For many of the attributes we need to extract, cleanse, normalize and categorize
For many of the attributes we need to extract, cleanse, normalize and categorize
Sample: Big Data 360 °°°°Lead Generation
© 2012 IBM Corporation22
Sample: Institutional Risk ApplicationComprehensive view of publicly traded companies and related people based on regulatory filings
Extract
Integrate
© 2012 IBM Corporation23
Requirements for a Big Data Solution Platform
Analyze Information in MotionStreaming data analysis
Large volume data bursts & ad-hoc analysis
Analyze a Variety of InformationNovel analytics on a broad set of mixed information that could not be analyzed before
Multiple relational & non-relational data types and schemas
Discover & ExperimentAd-hoc analytics, data discovery & experimentation
Analyze Extreme Volumes of InformationCost-efficiently process and analyze petabytes of information
Manage & analyze high volumes of structured, relational data
Manage & PlanEnforce data structure, integrity and control to ensure consistency for repeatable queries
© 2012 IBM Corporation24
IBM Big Data Platform for Ingest, Data and Analytic s
IBM Big Data Platform
Systems Management
Application Development
Visualization & Discovery
Accelerators
Information Integration & Governance
HadoopSystem
Stream Computing
Data Warehouse
New analytic applications drive the requirements for a big data platform
• Integrate and manage the full variety, velocity and volume of data
• Apply advanced analytics to information in its native form
• Visualize all available data for ad-hoc analysis
• Development environment for building new analytic applications
• Workload optimization and scheduling
• Security and Governance
BI / Reporting
Exploration / Visualization
FunctionalApp
IndustryApp
Predictive Analytics
Content Analytics
Analytic Applications
© 2012 IBM Corporation25
Big Data Hadoop Capabilities
IBM BigInsightsHadoop-based processing for analytics on variety and volumes of data
IBM StreamsLow latency analytics for streaming data
IBM Big Data SolutionsBig Data Challenges
• Very high volumes (TBs to PBs) unstructured data
• Exploration and discovery• Text, Entity and Social Media Analytics
• Real time processing• Detect failure patterns• High volume, low latency processing
• Scoring and decision analytics
NoS
QL
Dat
a S
trea
min
g
© 2012 IBM Corporation26
Social
Unstructured
• Meter Data Management• Customer Portals• Smart Meter Analytics• Demand Forecasting• Generation Scheduling• Customer Segmentation• Campaign Management• Outage Management• Estimate Load Shedding• Time of Use Tariffs• Maintenance Scheduling
Foundational
Legacy
Applcations
Regulations
Sensors
Streaming
IBM Streams
Real Time Scoring and Response
• Smart Grid Analytics• Distribution Grid
Monitoring• Root Cause Failure
Analysis• Demand Response
Effectiveness
Streaming Structured or Unstructured
Impr
ove
d A
naly
tics
Structure
d
Analytics and
Reporting
Web/social
Exploration/Discovery Queryable Archive
• Sentiment analysis• Call Centre analysis• Log analysis• Outage Information • Micro customer
segmentation• Offering Management
Impr
ove
d A
naly
tics
Unstructured
IBM BigInsights
Structure
d
Analytics and
Reporting
Employee
Supplier
Maintenance
Orders
GIS
Generation DistributionTransmission
CustomerTrading
Marketing
Smart Meters
High Level Conceptual View *)
Data Asset Landscape
IBM power i
*) Example for Industry Energy & Utility
Operational
Systems
© 2012 IBM Corporation27
Based on open source & IBM technologies
Distinguishing characteristics
• Built-in analytics enhances business knowledge
• Enterprise software integration complements and extends existing capabilities
• Production-ready platform with tooling for analysts, developers, and administrators speeds time-to-value and simplifies development/maintenance
IBM advantage
• Combination of software, hardware, services and advanced research
IBM InfoSphere BigInsightsAnalytical platform for Big Data at-rest
BI / Reporting
Exploration / Visualization
FunctionalApp
IndustryApp
Predictiv e Analytics
Content Analytics
Analytic Applications
IBM Big Data Platform
Systems Management
Application Development
Visualization & Discovery
Accelerators
Information Integration & Governance
Stream Computing
Data Warehouse
HadoopSystem
© 2012 IBM Corporation28
IBM InfoSphere BigInsightsEmbrace and Extend Hadoop
HDFS
Storage HBase
GPFS-SNC *)
Application
AdaptiveMRZook
eepe
r
Avr
oPig Hive Jaql
MapReduce
Flume
Data Sources/Connectors
JDBC
Netezza BoardReader
DB2
Streams
Web Crawler
Oozie
Analytics ML Analytics *)Text Analytics Interface
Lucene
R
CSV/XML/JSONData Stage SPSS
IBM
LZ
O C
ompr
essi
on
BigSheets
BigIndexFLEX
Open Source
IBM
Management Console (browser based)
*) future release
Developing Tooling(Eclipse Plug-Ins)
Rest API(for Applications)
© 2012 IBM Corporation29
�A visual tool for data manipulation and prototyping BigSheets
• Ad-hoc analytics for LOB user
• Analyze a variety of data - unstructured and structured
• Spreadsheet metaphor for exploring/ visualizing data
• Browser-based
© 2012 IBM Corporation30
Pre-configured text annotators ready for distribute d processing on Big Data
Support for native languages including double-byte
Turns disparate words into measurable insights
Identify positive or negative sentiment,
NLP-based analytics, define
variables, macros and rules.
Physically assemble data,
standardize formats, address
auto-identify language, process punctuation and non-grammatical
characters, standardize
spelling.
Part-of-speech identification, standard
and customized extraction dictionaries,
proper noun identification, concept
categorization, synonyms, exclusions,
multi-terms, regular expressions, fuzzy-
matching
Iterative classification using
automated and manual techniques.
Concept derivation & inclusion, semantic
networks and co-occurrence rules
Reporting/Monitoring social commentary,
combination w /structured data, clustering,
associated concepts, correlated concepts, auto-
classification of documents, sites, posts.
Text Analytics
© 2012 IBM Corporation31
How it works
• Parses text and detects meaning with annotators
• Understands the context in which the text is analyzed
• Hundreds of pre-built annotators for names, addresses, phone numbers, along others
Accuracy
• Highly accurate in deriving meaning from complex text
Performance
• AQL language optimized for MapReduce
Football World Cup 2010 , one team distinguished themselves well, losing to the eventual champions 1-0 in the Final. Early in the second half, Netherlands’
striker, Arjen Robben, had a breakaway, but the keeper for Spain, Iker Casillas
made the save. Winger Andres Iniesta
scored for Spain for the win.
Unstructured text (document, email, etc)
Classification and Insight
Text AnalyticsHighly accurate analysis of textual content
© 2012 IBM Corporation32
Framework for machine learning (ML) implementations on Big Data
• Large, sparse data sets, e.g. 5B non-zero values
• Runs on large BigInsights clusters with 1000s of nodes
Productivity
• Build and enhance predictive models directly on Big Data
• High-level language – Declarative Machine Learning Language (DML)
• E.g. 1500 lines of Java code boils down to 15 lines of DML code
• Parallel SPSS data mining algorithms implementable in DML
Optimization
• Compile algorithms into optimized parallel code
• For different clusters and different data characteristics
• E.g. 1 hr. execution (hand-coded) down to 10 mins
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0 500 1000 1500 2000
# non zeros (million)
Exe
cutio
n T
ime
(sec
)Java Map-Reduce Sy stemML Single node R
ML AnalyticsStatistical and Predictive Analysis
© 2012 IBM Corporation33
Task Map(break task into small parts)
Adaptive Map(optimization —order small units of work)
Reduce(many results to a single result set)
Adaptive MapReduce
� Algorithm to optimize execution time of multiple small jobs
� Performance gains of 30% reduce overhead of task startup
Hadoop System Scheduler
� Identifies small and large jobs from prior experience
� Sequences work to reduce overhead
Workload OptimizationOptimized performance for big data analytic workloa ds
© 2012 IBM Corporation343434
� Public wind data is available on 284km x 284 km grids (2.5o LAT/LONG)
� More data means more accurate and richer models (adding hundreds of variables)
- Vestas wind library at 2.5 PB: to grow to over 6 PB in the near-term
- Granularity 27km x 27km grids: driving to 9x9, 3x3 to 10m x 10m simulations
� Reduced turbine placement identification from weeks to hours
� Perspective: The Vestas Wind library
34
© 2012 IBM Corporation35
Built to analyze data in motion
• Multiple concurrent input streams
• Massive scalability
Process and analyze a variety of data
• Structured, unstructured content, video, audio
• Advanced analytic operators
InfoSphere StreamsAnalytical platform for Big Data in-motion
BI / Reporting
Exploration / Visualization
FunctionalApp
IndustryApp
Predictiv e Analytics
Content Analytics
Analytic Applications
IBM Big Data Platform
Systems Management
Application Development
Visualization & Discovery
Accelerators
Information Integration & Governance
HadoopSystem
Data Warehouse
Stream Computing
© 2012 IBM Corporation36
Current fact finding
Analyze data in motion – before it is stored
Low latency paradigm, push model
Data driven – bring the data to the query
Historical fact finding
Find and analyze information stored on disk
Batch paradigm, pull model
Query-driven: submits queries to static data
Traditional Computing Stream Computing
Stream ComputingAnalyze Data in Motion
Data Base
© 2012 IBM Corporation37
Streams approach illustratedtuple
© 2012 IBM Corporation38
Linear Scalability
� Clustered deployments – unlimited scalability
Automated Deployment
� Automatically optimize operator deployment across clusters
Performance Optimization
� JVM Sharing – minimize memory use
� Fuse operators on same cluster
� Telco client – 25 Million messages per second
Analytics on Streaming Data
� Analytic accelerators for a variety of data types
� Optimized for real-time performance
IBM InfoSphere StreamsMassively Scalable Stream Analytics
Visualization
Streams Runtime
Deployments
SyncAdapters
AnalyticOperators
SourceAdapters
Automated and Optimized Deployment
Streaming DataSources
Streams Studio IDE
© 2012 IBM Corporation39
• Optimize building energy consumption with centralized monitoring
• Automate preventive and corrective maintenance
Capabilities Utilized:• Streaming Analytics• Hadoop System• Business Intelligence
Applications:• Log Analytics• Energy Bill Forecasting• Energy consumption optimization• Detection of anomalous usage• Presence-aware energy mgt.• Policy enforcement
Cisco turns to IBM big data for intelligent
infrastructure management
© 2012 IBM Corporation40
� Use case– Neonatal infant monitoring– Predict infection in ICU 24 hours in advance
� Solutions– 120 children monitored :120K msg/sec, billion msg/day– Trials expanding to include hospitals in US and China
University of Ontario Institute of Technology
SensorNetwork
Stream-based Distributed Interoperable Health care Infrastructure
Solutions (Applications)
Event Pre-processer
Analysis Framework
© 2012 IBM Corporation41
Without a Big Data Platform You Code…
Streams provides development, deployment, runtime, and infrastructure services
“TerraEchos developers can deliver applications 45% faster due to the agility of Streams
Processing Language…”– Alex Philip, CEO and President, TerraEchos
Multithreading
Custom SQLand
Scripts
PerformanceOptimization
Debug
ApplicationManagement
EventHandling
Connectors
CheckPointing
Security
HA Acceleratorsand
Tool kits
Over 100 sample applications and toolkits with industry focused toolkits with 300+ functions and operators
© 2012 IBM Corporation42
2012IBM is Committed to Innovation
2005* TeaLeaf, Varicent Vivismo pending acquisition close
• $16B+ in acquisit ions since 2005
• 10,000+ technical professionals
• ~8000 dedicated consultants
• 27,000+ business partner certifications
• 8 Analytics Solutions Centers
• 100 analytics-based research assets; almost 300 researchers
• $16B+ in acquisit ions since 2005
• 10,000+ technical professionals
• ~8000 dedicated consultants
• 27,000+ business partner certifications
• 8 Analytics Solutions Centers
• 100 analytics-based research assets; almost 300 researchers
IBM ResarchAlmadenAustinMelbourneSao PauloBeijingHaif aDelhiIrelandYamatoWatsonZurich
“Watson is going to revolutionize many, many industries and it will fundamentally change the way we interact with computers & machines.”
John Kelly, SVP & Head of IBM Research
Selected SW Acquisitions
© 2012 IBM Corporation43
bigdatauniversity.com/
ibm.com/software/data/bigdata/
Making Learning Easy and FunAsk for a Big Data Discovery Workshop
ibm.com/software/data/infosphere/biginsights/
youtube.com/user/ibmbigdata
© 2012 IBM Corporation44
Dipl.Ing.Wolfgang Nimführ
Information Agenda Executive ConsultantBig Data Tiger TeamIBM Software Group Europe
IBM AustriaObere Donaustrasse 95A1020 Vienna
Questions & Answers