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December 2013




Some Smarter Analytics                                                A Talk with students of
Innovation Trends                                                            University of Bari (Italy) –
                                                                             Computing Science Department
Cognitive Computing, Big Data e                                              Knowledge Bases and Data
Statistical Analytics                                                        Mining (Basi di Conoscenza e
                                                                             Data Mining) Course




Pietro Leo
IBM GBS Executive Architect – Member of IBM Academy of Technology Leadership Team
  @pieroleo    www.linkedin.com/in/pieroleo
                                                                                               © 2012 IBM Corporation
December 2012



My Personal IT Mind-Map
Data Models                World Instrumentation                                                      eBusiness
                                                                       Services/Legacy Applications
  Enterprise Data             Pervasive Computing
  Storage
  (IMS, DBMS,                                                               Portals – Webization
                                Internet of Things
  Etc,)
                                      Big Data                         Social-ization        App-ization
                               (structured & unstructured)
Virtualization
                                                                                             Web-App-ization
     Cloud                     Cloud Services                          IT Consumerization/BYOD
     Computing

                                                 Cognitive
 Workload-                                       Computing
 Optimizied
                                 Business Analytics                                   Mobile Computing
         Parallel
         Computing                                           Optimization
                            Data Warehousing /
Computing Models,           Business Intelligence                                   Social Business &
Architectures & Styles               Analytics - Information-based Intelligence     Mobility

         = Conceptual connection, Evolution Path, Cause-Effect, etc.            @pieroleo   www.linkedin.com/in/pieroleo
                                                                                                     © 2012 IBM Corporation
December 2012


Agenda

  Research Overview and Grand Challenges

  1      Cognitive Systems Era
          Data Centric ← Beyond Big Data
          Statistical Analytics ← Beyond Machine Learning

  2      Cognitive Systems Strategic challenges for Our Organizations


  3      Statistical Analytics Strategy


  4       Examples of Statistical Analytics Problems & Benefits




                                                             @pieroleo   www.linkedin.com/in/pieroleo
                                                                                  © 2012 IBM Corporation
December 2012


  IBM - Continually Looking Forward


                                                                          C-suite Studies



        Executive Exchange: http://www-935.ibm.com/services/c-suite/insights/index.html




    IBM Institute for                                                          IBM Global
     Business Value                                                        Technology Outlook

                                     Smarter Planet
                                                                          @pieroleo   www.linkedin.com/in/pieroleo
                                                                                               © 2012 IBM Corporation
December 2012


 Nothing Is Changing More than IT …


          The way         The way          The way
       it’s accessed    it’s applied   it’s architected




                                            Integrated
       ubiquitously     for insight
                                           and flexible
                                       @pieroleo   www.linkedin.com/in/pieroleo
                                                            © 2012 IBM Corporation
December 2012


 Grand Challenges are the trigger of new changes…
       IBM Is Founded      The IBM Punched Card           RAMAC             FORTRAN          IBM 1401: The
                                                                                               Mainframe




         1911                     1920                   1954                  1957             1959
     Magnetic Stripe         Universal Product Code      The PC             Scanning Tunneling
      Technology                 (UPC) barcode                                 Microscope




        1969                      1973                   1981                      1986
 Optimizing the Food Chain                                                       The Globally
                                     e-business             Linux            Integrated Enterprise




         1988                         1990s                 2000                   2006
   Breaking the Petaflop
                             The DNA Transistor       Smarter Planet    A Computer Called Watson
          Barrier




         2008                     2009                   2008          @pieroleo   2011
                                                                                    www.linkedin.com/in/pieroleo
                                                                                              © 2012 IBM Corporation
December 2012


 Ultimately Leading to
 Tremendous New Value

  Provide New Types of Insights




                                  @pieroleo   www.linkedin.com/in/pieroleo
                                                       © 2012 IBM Corporation
December 2012


Agenda

   1      Cognitive Systems Era
           Data Centric ← Beyond Big Data
           Statistical Analytics ← Beyond Machine Learning

   2      Cognitive Systems Strategic challenges for Our Organizations


   3      Statistical Analytics Strategy


   4       Examples of Statistical Analytics Problems & Benefits




                                                              @pieroleo   www.linkedin.com/in/pieroleo
                                                                                   © 2012 IBM Corporation
December 2012


            Eras of computing
                                                            Cognitive
                                                            Systems Era




                                      Programmable
Computer Intelligence




                                      Systems Era



                        Tabulating
                        Systems Era




                        Time                         Time
                                                                   @pieroleo   www.linkedin.com/in/pieroleo
                                                                                        © 2012 IBM Corporation
December 2012




Cognitive Systems                 Cognitive
                                  Systems Era

                                  1. Data-centric
           Programmable           2. Statistical analytics
           Systems Era            3. Scale in
                                  4. Automated systems/
           1. Processor-centric     workload managemen
           2. Fixed calculation
           3. Scale up/out
           4. Manual systems        Cognitive
             management             Systems Era


                Programmabl
                e Systems Era




                                           @pieroleo   www.linkedin.com/in/pieroleo
                                                                © 2012 IBM Corporation
December 2012




Cognitive Systems                 Cognitive
                                  Systems Era

                                  1. Data-centric
           Programmable           2. Statistical analytics
           Systems Era            3. Scale in
                                  4. Automated systems/
           1. Processor-centric     workload managemen
           2. Fixed calculation
           3. Scale up/out
           4. Manual systems        Cognitive
             management             Systems Era


                Programmabl
                e Systems Era




                                           @pieroleo   www.linkedin.com/in/pieroleo
                                                                © 2012 IBM Corporation
December 2012


            Data-Centric: Big Data this is just the beginning
                                                              Cognitive
                                                              Systems Era



                                     Programmable
                                     Systems Era
Computer Intelligence




                                                                                                                       Percentage of uncertain data
                        Tabulating                         Percentage of uncertain data
                        Systems
                        Era




                                                    Time
                                                                         @pieroleo   www.linkedin.com/in/pieroleo
                                                                                              © 2012 IBM Corporation
December 2012


 Data-centric models are driving us to a new era of
 computing


                   Volume                                     Variety
                                                                                Structured, Semi-
Terabytes to exabytes of
                                                                            structured Unstructured,
     existing data                 <20%        Content
                                Data                                            text & multimedia
      to process                                       >80%




                                    Traditional
                    Velocity      Enterprise Data              Veracity

      Streaming data,                     Social
                               Data from and about People                     Uncertainty from
milliseconds to seconds to                                                     inconsistency,
          respond                                                             ambiguities, etc.
                                       Physical
                                   Sensors & Streams
                                                                @pieroleo      www.linkedin.com/in/pieroleo
13               Nove                                                                   © 2012 IBM Corporation
Big data is a business priority – inspiring new models and
processes for organizations, and even entire industries




14   | ©2012 IBM Corporation
December 2012

            Statistical analytics: Develop tools that augment human intelligence and
            productivity
                                                                              Cognitive
                                                                              Systems Era



                                                      Programmable
                                                      Systems Era
Computer Intelligence




                        Tabulating
                        Systems
                        Era                                                                            Information-based
                                                                                                       Intelligence


                                                                                                              The Singularity!
                                                                                                              Kurzweil > 2045: The
                                                                                                              Year Man Becomes
                            Artificial Intelligence                                                           Immortal
                                                                            Strong Approach
                                                                            Surpass Humans in Intelligence

                                                                     Time               @pieroleo   www.linkedin.com/in/pieroleo
                                                                                                             © 2012 IBM Corporation
December 2012



                                                             Information-based Intelligence
                                                                       Approach
                                                        Statistical, brute force approach based on analyzing
             Strong Approach                            vast amounts of information using powerful computers
Early efforts approached AI based on programming        and sophisticated algorithms
 logic, reasoning, planning, learning


A number of government supported academic efforts
                                                        Scales very nicely: the more information you have, the
 in the 1960s and 1970s, primarily in the US (MIT,      more powerful the computer, the more sophisticated
 Stanford, etc) and UK. Many felt that problem was      the analytical algorithms . . . the better the results
 speed of machines - therefore machines would catch
 up with human intelligence within a generation based
 on advances in technology                              Data & Knowledge Integration  more insights you
                                                        have, more methods and approaches you have, more
Fifth Generation Project: Major Japanese effort in
 1980s to leap ahead of US in computer development      longitudianlabilities you have to generato point of views
 by creating new generation of intelligent, reasoning   … more effective will be the final result
 machines


All these efforts failed. Grossly underestimated        Originated in science, especially high energy physics
 difficulty of developing machines exhibiting human
 intelligence                                                                                      Statistical
                                                        Data mining (mainly from 1990s)
                                                                                                   Analytics
                                                        Deep Blue (1997)

                                                        Watson (2011)             @pieroleo   www.linkedin.com/in/pieroleo
                                                                                                       © 2012 IBM Corporation
December 2012


Agenda

   1      Cognitive Systems Era
           Data Centric ← Beyond Big Data
           Statistical Analytics ← Beyond Machine Learning

   2      Cognitive Systems Strategic challenges for Our Organizations


   3      Statistical Analytics Strategy


   4       Examples of Statistical Analytics Problems & Benefits




                                                              @pieroleo   www.linkedin.com/in/pieroleo
                                                                                   © 2012 IBM Corporation
December 2012


Statistical Analytics challenges for Our Organizations
                    From Data to Insight to Context
                From Data to Insight to Context
                  Not about bigger or                         …It’s about fusing data and
                faster data from any one                      analytics from 100s-1000s of
                         source…                                         sources

Analyze Structured, Un-
     structure and
Unstructured Data and
  Integrate Insights




Analyst                                                                                                Social
                                                                        Web/digital
                From the Field             Contact Center -
                                             Interactions
                                                                            @pieroleo   www.linkedin.com/in/pieroleo
  These capabilities exist today: High Value Context Requires a Wide Variety of High-V Data SourcesCorporation
                                                                                            © 2012 IBM
December 2012



Cognitive Systems Strategic challenges for Our Organizations
 Create an integrated view of from Data & Content coming from ALL data channels
 including social business
                                                    Data Channels
  Data               Analysts/Cases     From the Field     Interactions   Web/digital          Social




                                             Semi-structured and
                      Structured             Unstructured                 Structured
 Data & Content       Agent/case Data        Call logs, Web Logs,         Observation Data
                                             Transcripts, Emails…


        Big Data
     & Business          Integrate and Analyze Structured and Unstructured Data
                                                                                                                 Organization /
       Analytics                                                                                                  Enterprise

         Insights     Crime Intelligence          Statistical Reports      Predictive Models
     Distribution     Alerts & warning            Analytics Reports        Geo-spatial Display
     & Utilization     generation                  Relation Resolution      Deep Text analytics
                      Identity Resolution


                                                                                         @pieroleo      www.linkedin.com/in/pieroleo
19              Nove                                                                                             © 2012 IBM Corporation
December 2012


Analytics challenge: Fusion reduces uncertainty by constructing context
                                                                             Required: tight integration to
                                                                             maximize context discovery
   Credit       Loyalty
                                           Data                              Required: common practices followed




                            FUSION
                                           finds                             by multiple standards for representing
       Michael                             Data                              uncertain data and uncertainty of all
     San Jose, CA                                         Mother             types, provenance, and lineage and
                                                               Date          other metadata
                 Buyin
                 Buyin                              Son
                 g
                 g                          Fact     Birthday                                               $560
                 DSLR
                 DSLR
                 today !!                   Discovery                                                         OR
                 today
 Influencers    Intent                 A                                                                      $999
                                         &
                                                                                          NY                       Buying
                                     Spatial Reasoning
                                                                                                                   a
                                                                      Sense Making                                 DSLR
                                         &                                                                         today !
   Customer at Mall                  Temporal
                                     Reasoning

                                                                                          Maximum Context
                                                                                                For
 Customer in Store #42
                                                   Correlation
                                                                                              Minimum
                                                                                            Uncertainty
                                                                             Required: common APIs to enable
 $999               $560                                                     sharing across the uncertainty
                                           Corroboration
                                                                             management pipeline
     In-Store Pricing                      (Evidence Combination)
      And Discounts                                          ETC.            No such common practices,
                                                                             standards or APIs exist today

                                                                              @pieroleo      www.linkedin.com/in/pieroleo
                                                                                                                      20
                                                                                                      © 2012 IBM Corporation
December 2012



  The value of analytics grows by incorporating new sources of data,
  composing a variety of analytic techniques, spanning organizational
  silos, and enabling iterative, user-driven interaction

  New format or
  usage of data
                                                                                          Multi-modal
                                                  Intent-to-buy trends                 demand forecasting
                Sources and types of data




                                                                Segmentation-
                                                                   based
                                                                market impact
                                                                  estimates
                                                                                         Price-based
                                                                                      demand forecasting
                                              Sales-based                             (own & competitors)
                                                 demand
                                               forecasting
   Structured or
   standardized

                                            Low                          Scope of decision                                   High
                                                                                                 @pieroleo   www.linkedin.com/in/pieroleo
                                                                                                                                      21
                                                                                                                      © 2012 IBM Corporation
December 2012


Analytics toolkits will be expanded to support ingestion and interpretation of
unstructured data, and enable adaptation and learning

                Adaptive Analysis                                   Responding to context                                 Learn
                                                                                                                            In the context of the
                Continual Analysis                                  Responding to local change/feedback
                                                                                                                            decision process
                Optimization under Uncertainty                      Quantifying or mitigating risk
                                                                                                                          Decide and Act
s doh e M w N




                Optimization                                        Decision complexity, solution speed
           e




                Predictive Modeling                                 Causality, probabilistic, confidence levels

                Simulation                                          High fidelity, games, data farming
                                                                                                                          Understand
     t




                Forecasting                                         Larger data sets, nonlinear regression                 and Predict

                Alerts                                              Rules/triggers, context sensitive, complex events

                Query/Drill Down                                    In memory data, fuzzy search, geo spatial
l anoti da T
          r




                Ad hoc Reporting                                    Query by example, user defined reports                Report
    i




                Standard Reporting                                  Real time, visualizations, user interaction

                Entity Resolution                                   People, roles, locations, things
                                                                                                                          Collect and
                Relationship, Feature Extraction                    Rules, semantic inferencing, matching                  Ingest/Interpret
                                                                                                                             Decide what to count;
                Annotation and Tokenization                         Automated, crowd sourced
a aD w N




                                                                                                                             enable accurate counting
      e




Extended from: Competing on Analytics, Davenport and Harris, 2007
                                                                                                             @pieroleo     www.linkedin.com/in/pieroleo
                                                                                                                                                      22
                                                                                                                                      © 2012 IBM Corporation
 t
December 2012


Analytics solution development requires several interacting design steps
                                     Algorithm Composition and Invention
  Data Evaluation and Fusion                                                        Testing and Execution Optimization


      Streaming data
                                                          Data mining
                                                          & statistics
      Text data

                                                   Optimization
      Multi-dimensional                            & simulation

                                                          Semantic
      Time series                                         analysis

                                                      Fuzzy
      Geo spatial                                     matching

      Video
      & image                                             Network
                                                          algorithms

       Relational
                                                    New
                                                    algorithms
      Social network

                                   ✔
                              Filtering and
                                                                  Business Rules Engine

                                                                                Composition and
    Data Acquisition                                 Core Analytics                                        Deployment
                          Extraction Validation                                   Packaging


                                                                                      @pieroleo   www.linkedin.com/in/pieroleo
                                                                                                                           23
                                                                                                           © 2012 IBM Corporation
December 2012


Agenda

   1      Cognitive Systems Era
           Data Centric ← Beyond Big Data
           Statistical Analytics ← Beyond Machine Learning

   2      Cognitive Systems Strategic challenges for Our Organizations


   3      Statistical Analytics Strategy


   4       Examples of Statistical Analytics Problems & Benefits




                                                              @pieroleo   www.linkedin.com/in/pieroleo
                                                                                   © 2012 IBM Corporation
December 2012




Statistical Analytics Strategy
 Content Access              Content & data                                  Insight Distribution
  & Integration               Organization           Analytics
                                                                                 & Utilization




   Disorganized                  Organized             Investigation               Knowledge
  And/OR Siloed                   Content              added-value              Accumulation &
     Content                                          from Contend                Distribution
                                                         and Data
                From the chaos to the   New visibility and      Insight generation and
                       order              knowledge              investigation support
                                                                 @pieroleo     www.linkedin.com/in/pieroleo
25              Nove                                                                                         25
                                                                                        © 2012 IBM Corporation
December 2012


A full set of functional capabilities needed to support a A
Statistical Analytics Strategy
  Content Access                Content & data                                                 Insight Distribution
                                 Organization                      Analytics
   & Integration                                                                                   & Utilization
                                                             Natural LP                 Social Media Analytics

                       Content Management                       Inf.                   Brodcast News Monitoring
                                                             Extraction
                                                                                             Image Analytics

                                                               Adv.               Advanced User Profiles Analytics
                Process Management          Enterprise
                                                             Analytics
                                             Search                                     Deep Question & Answer
                Content Federation                           & Mining
                                                             Content       Predictive          Reporting & Dashboards
                              Entity                         Analytics     Analytics
          Master Data      resolution &      Business                                            Network Visualization
          Management         Relation         Rules
                                                                                                Adv. Case Management
                            discovery                         Content Classification

                                               Standard Datawarehouse models

                                     Advanced Big Data models (streams and restfull data)

    Disorganized                     Organized                    Investigation                      Investigation
   And/OR Siloed                      Content                   added-value from                      Knowledge
      Content                                                   Contend and Data                    Accumulation &
                                                                                                      Distribution
                                                                                 @pieroleo        www.linkedin.com/in/pieroleo
26              Nove                                                                                       © 2012 IBM Corporation
December 2012


Agenda

   1      Cognitive Systems Era
           Data Centric ← Beyond Big Data
           Statistical Analytics ← Beyond Machine Learning

   2      Cognitive Systems Strategic challenges for Our Organizations


   3      Statistical Analytics Strategy


   4       Examples of Statistical Analytics Problems & Benefits




                                                              @pieroleo   www.linkedin.com/in/pieroleo
                                                                                   © 2012 IBM Corporation
December 2012




Examples of Statistical Analytics Benefits

        Retail Banking Customer Care                                       Retail Customer Care
        Analyzing: Call logs, internal and external media, claim           Analyzing: Call logs, online media
        For: Buyer Behavior                                                For: Brand Reputation Management
        Benefits: Improve Customer satisfaction, marketing                 Benefits: Improve customer sat, marketing campaigns
        campaigns, find new revenue opportunities



        Healthcare Analytics                                              Crime Analytics
        Analyzing: Care records                                           Analyzing: Police records, Emergency calls…
        For: Clinical analysis; treatment protocol optimization           For: Rapid crime solving & crime trend analysis
        Benefits: Better management of chronic diseases; optimized drug   Benefits: Safer communities & optimized force deployment
        formularies; improved patient outcomes




         Insurance Fraud                                                  Automotive Quality Insight
         Analyzing: Insurance claims                                      Analyzing: Tech notes, call logs, online media
         For: Detecting Fraudulent activity & patterns                    For: Brand Reputation Management
         Benefits: Reduced losses, faster detection, more efficient       Benefits: Reduce warranty costs, improve customer
          claims processes                                                satisfaction, marketing campaigns



                                                                                                 @pieroleo       www.linkedin.com/in/pieroleo
28               Nove                                                                                                     © 2012 IBM Corporation
December 2012


Agenda




                Ongoing Research Project with University of Bari:
                Recognise a “Complex Event” from Social Media Data




        Students:
        Francesco Tangari
        Rocco Caruso




                                                    @pieroleo   www.linkedin.com/in/pieroleo
                                                                         © 2012 IBM Corporation
December 2012
                                                                             Dipartimento di Informatica
                                                                             Università degli Studi di Bari
Research challenge and its business value (1/2)
 A complex event has               People Attributes
 a defined spatio-                 who is planning? who is going to                                          Spatial
 temporal connotation:             participate/attend?, who is interested                                    Where is it
                                   and follows, which is the network                                         located? it can be
 It involves one or                created around the event…                                                 a square, a station,
 more individuals                                                                                            a virtual a place,
 (Who) that organize                                                                                         etc. where
 and/or                                                                    Who                               everyone can
 Participate and/or are                                                                                      see the event
 followers to set up a
 defined action (what)                                                   Complex Event
 in a defined location,       Argument                                    & its Dimensions
 real or virtual, (where)     what was               What                     PROFILE              Where
                              planned
 in a given moment            for the
 (when).                      Event? Whis
                              is the topic
                              and the
 A “flas mob” is an
                              motivation?,
                                                                                When
 example of a complex         es People
 event, other examples        will dance, will
 are srikes, sport            freeze, etc...                                                 Temporal
 events, protests, etc.                                                    The date and the time at which the event
                                                                          will take place, the date and the time where
                                                                            the event preparation will take place….
                                                                                            @pieroleo   www.linkedin.com/in/pieroleo
                            Un approccio Statistico per la Predizione di Flashmob da Reti                        © 2012 IBM Corporation
                                                        Sociali                                                     2
December 2012


Research challenge and its business value (2/2 )

                                 ….in the case of predicting a Flash Mob

Leveraging social
media data and
                     •Ex. 2: Knowing that a flash mob will be
generate insights    used for the promotion of a new product, a
about complex        firm which is in competition on the same
                     market can organize counter-action.
business relevant
phenomena by         • Ex 2: A law enforcement org knowing that
connecting the       a flash mob will be organized for political
                     purpose or for demonstration can
dots                 effectively relocate law forces.




                                            @pieroleo   www.linkedin.com/in/pieroleo
                                                                 © 2012 IBM Corporation
December 2012

 Information about the Event are spread on a number of social
 media channels: An example of Flash Mob organization dynamic




                                              @pieroleo   www.linkedin.com/in/pieroleo
                                                                   © 2012 IBM Corporation
December 2012


General System Context: First Prototype & Experimentation based on
Twitter channel

Twitter Channel (*)

                                                                                                                                 Flash Mobs Profile
                                                                                                                                   Recostruction
                                                                                                                                      & Alerts
                                                          Information
                                                           Extraction                                                                Who


                                                                                                                              What FlashMob      Where


                                                                                                                                         When
                                                                                                                                                              Who
                                                   (POS, named entities: person,
                       Data Access                 organization, Locations, data,            Event                                                      What FlashMob      Where
                         & Basic                   etc. High-level, concepts,
                                                   wikification, etc..)                    Prediction                              Who
                         Feature                                                                                                                                When

                                                                                           & Alerting
                        Extraction                                                                                           What FlashMob      Where


                                                                                                                                     When                       Who


                       (Tokenization, hashtags,                                       (Clustering, Incremental                                           What FlashMob      Where
                       URs, Geotags, social            Social Network                 Clustering, Burst
                       network metadata, etc.)            Analysis                    recognition..)                                                                When




                                                    (Clique, Relevant Nodes, Page
                                                    Rank ndes, etc..



                                              Implemented path                        Planed integration
                                                                                                                                          Analytics
                                                                                                                                         Consumers
      Acquiring tweets including
        the #flashmob hashtag
                                              (*) In our vision a number of “channels” should provide data                      (What’s up App for
    and/or the keyword “flashmob”             to the system such as Facebook, YouTube, etc. As well as also                     smartphone, Social analytics
          and/or “flash mob”                  Other social analytics applications such as IBM COBRA or CCO, etc.
                                                                                                                 @pieroleo      client, etc. etc.)
                                                                                                                                 www.linkedin.com/in/pieroleo
                                                                                                                                                   © 2012 IBM Corporation
December 2012


Working on real data and applying the prediction model



                                                               Period: 1/gen – 29/Feb

        Alerts/Clusters = 59
                                                 Analyzed 5148 (English
                                                 language) Tweets that
                                                 included the word or the
                                                 hashtag “flashmob”

                                                 Generated in total 59 Flash
                                                 Mob Alerts (clusters)
                                                 involving 1267 tweets

                                                 20 Alerts correctly
                                                 aggregated data about 20
                                                 Flash Mobs with an
                                                 accuracy about of 100%


                                                   @pieroleo      www.linkedin.com/in/pieroleo
                                                                           © 2012 IBM Corporation
December 2012


In a new research phase we are now extending the predicton model to
recostruct main Complex Event attributes
                    Complex Event
                    Attributes
                                                                           Hadoop

                                            CLUSERING
                                            CLUSERING
                                             DBSCAN
                                              DBSCAN
    COMPLEX
     COMPLEX
    EVENT
     EVENT
    PROFILER
     PROFILER                                                 NLP ANNOTATION
    EXTRACTOR
     EXTRACTOR                                                     TOOLS
                                                                           {1..N}




                                              HIVE DW
                                              HIVE DW

                 Streaming
                 JSON                          HDFS

                                                  @pieroleo    www.linkedin.com/in/pieroleo
                                                                        © 2012 IBM Corporation
December 2012


Agenda




                Wrap-up!



                           @pieroleo   www.linkedin.com/in/pieroleo
                                                © 2012 IBM Corporation
December 2012



My Personal IT Mind-Map
Data Models                World Instrumentation                                                      eBusiness
                                                                       Services/Legacy Applications
  Enterprise Data             Pervasive Computing
  Storage
  (IMS, DBMS,                                                               Portals – Webization
                                Internet of Things
  Etc,)
                                      Big Data                         Social-ization        App-ization
                               (structured & unstructured)
Virtualization
                                                                                             Web-App-ization
     Cloud                     Cloud Services                          IT Consumerization/BYOD
     Computing

                                                 Cognitive
 Workload-                                       Computing
 Optimizied
                                 Business Analytics                                   Mobile Computing
         Parallel
         Computing                                           Optimization
                            Data Warehousing /
Computing Models,           Business Intelligence                                   Social Business &
Architectures & Styles               Analytics - Information-based Intelligence     Mobility

         = Conceptual connection, Evolution Path, Cause-Effect, etc.            @pieroleo   www.linkedin.com/in/pieroleo
                                                                                                     © 2012 IBM Corporation
December 2012




          Grazie!




                    @pieroleo   www.linkedin.com/in/pieroleo
                                         © 2012 IBM Corporation

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Cognitive computing big_data_statistical_analytics

  • 1. December 2013 Some Smarter Analytics A Talk with students of Innovation Trends University of Bari (Italy) – Computing Science Department Cognitive Computing, Big Data e Knowledge Bases and Data Statistical Analytics Mining (Basi di Conoscenza e Data Mining) Course Pietro Leo IBM GBS Executive Architect – Member of IBM Academy of Technology Leadership Team @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 2. December 2012 My Personal IT Mind-Map Data Models World Instrumentation eBusiness Services/Legacy Applications Enterprise Data Pervasive Computing Storage (IMS, DBMS, Portals – Webization Internet of Things Etc,) Big Data Social-ization App-ization (structured & unstructured) Virtualization Web-App-ization Cloud Cloud Services IT Consumerization/BYOD Computing Cognitive Workload- Computing Optimizied Business Analytics Mobile Computing Parallel Computing Optimization Data Warehousing / Computing Models, Business Intelligence Social Business & Architectures & Styles Analytics - Information-based Intelligence Mobility = Conceptual connection, Evolution Path, Cause-Effect, etc. @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 3. December 2012 Agenda Research Overview and Grand Challenges 1 Cognitive Systems Era  Data Centric ← Beyond Big Data  Statistical Analytics ← Beyond Machine Learning 2 Cognitive Systems Strategic challenges for Our Organizations 3 Statistical Analytics Strategy 4 Examples of Statistical Analytics Problems & Benefits @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 4. December 2012 IBM - Continually Looking Forward C-suite Studies Executive Exchange: http://www-935.ibm.com/services/c-suite/insights/index.html IBM Institute for IBM Global Business Value Technology Outlook Smarter Planet @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 5. December 2012 Nothing Is Changing More than IT … The way The way The way it’s accessed it’s applied it’s architected Integrated ubiquitously for insight and flexible @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 6. December 2012 Grand Challenges are the trigger of new changes… IBM Is Founded The IBM Punched Card RAMAC FORTRAN IBM 1401: The Mainframe 1911 1920 1954 1957 1959 Magnetic Stripe Universal Product Code The PC Scanning Tunneling Technology (UPC) barcode Microscope 1969 1973 1981 1986 Optimizing the Food Chain The Globally e-business Linux Integrated Enterprise 1988 1990s 2000 2006 Breaking the Petaflop The DNA Transistor Smarter Planet A Computer Called Watson Barrier 2008 2009 2008 @pieroleo 2011 www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 7. December 2012 Ultimately Leading to Tremendous New Value Provide New Types of Insights @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 8. December 2012 Agenda 1 Cognitive Systems Era  Data Centric ← Beyond Big Data  Statistical Analytics ← Beyond Machine Learning 2 Cognitive Systems Strategic challenges for Our Organizations 3 Statistical Analytics Strategy 4 Examples of Statistical Analytics Problems & Benefits @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 9. December 2012 Eras of computing Cognitive Systems Era Programmable Computer Intelligence Systems Era Tabulating Systems Era Time Time @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 10. December 2012 Cognitive Systems Cognitive Systems Era 1. Data-centric Programmable 2. Statistical analytics Systems Era 3. Scale in 4. Automated systems/ 1. Processor-centric workload managemen 2. Fixed calculation 3. Scale up/out 4. Manual systems Cognitive management Systems Era Programmabl e Systems Era @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 11. December 2012 Cognitive Systems Cognitive Systems Era 1. Data-centric Programmable 2. Statistical analytics Systems Era 3. Scale in 4. Automated systems/ 1. Processor-centric workload managemen 2. Fixed calculation 3. Scale up/out 4. Manual systems Cognitive management Systems Era Programmabl e Systems Era @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 12. December 2012 Data-Centric: Big Data this is just the beginning Cognitive Systems Era Programmable Systems Era Computer Intelligence Percentage of uncertain data Tabulating Percentage of uncertain data Systems Era Time @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 13. December 2012 Data-centric models are driving us to a new era of computing Volume Variety Structured, Semi- Terabytes to exabytes of structured Unstructured, existing data <20% Content Data text & multimedia to process >80% Traditional Velocity Enterprise Data Veracity Streaming data, Social Data from and about People Uncertainty from milliseconds to seconds to inconsistency, respond ambiguities, etc. Physical Sensors & Streams @pieroleo www.linkedin.com/in/pieroleo 13 Nove © 2012 IBM Corporation
  • 14. Big data is a business priority – inspiring new models and processes for organizations, and even entire industries 14 | ©2012 IBM Corporation
  • 15. December 2012 Statistical analytics: Develop tools that augment human intelligence and productivity Cognitive Systems Era Programmable Systems Era Computer Intelligence Tabulating Systems Era Information-based Intelligence The Singularity! Kurzweil > 2045: The Year Man Becomes Artificial Intelligence Immortal Strong Approach Surpass Humans in Intelligence Time @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 16. December 2012 Information-based Intelligence Approach Statistical, brute force approach based on analyzing Strong Approach vast amounts of information using powerful computers Early efforts approached AI based on programming and sophisticated algorithms logic, reasoning, planning, learning A number of government supported academic efforts Scales very nicely: the more information you have, the in the 1960s and 1970s, primarily in the US (MIT, more powerful the computer, the more sophisticated Stanford, etc) and UK. Many felt that problem was the analytical algorithms . . . the better the results speed of machines - therefore machines would catch up with human intelligence within a generation based on advances in technology Data & Knowledge Integration  more insights you have, more methods and approaches you have, more Fifth Generation Project: Major Japanese effort in 1980s to leap ahead of US in computer development longitudianlabilities you have to generato point of views by creating new generation of intelligent, reasoning … more effective will be the final result machines All these efforts failed. Grossly underestimated Originated in science, especially high energy physics difficulty of developing machines exhibiting human intelligence Statistical Data mining (mainly from 1990s) Analytics Deep Blue (1997) Watson (2011) @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 17. December 2012 Agenda 1 Cognitive Systems Era  Data Centric ← Beyond Big Data  Statistical Analytics ← Beyond Machine Learning 2 Cognitive Systems Strategic challenges for Our Organizations 3 Statistical Analytics Strategy 4 Examples of Statistical Analytics Problems & Benefits @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 18. December 2012 Statistical Analytics challenges for Our Organizations From Data to Insight to Context From Data to Insight to Context Not about bigger or …It’s about fusing data and faster data from any one analytics from 100s-1000s of source… sources Analyze Structured, Un- structure and Unstructured Data and Integrate Insights Analyst Social Web/digital From the Field Contact Center - Interactions @pieroleo www.linkedin.com/in/pieroleo These capabilities exist today: High Value Context Requires a Wide Variety of High-V Data SourcesCorporation © 2012 IBM
  • 19. December 2012 Cognitive Systems Strategic challenges for Our Organizations Create an integrated view of from Data & Content coming from ALL data channels including social business Data Channels Data Analysts/Cases From the Field Interactions Web/digital Social Semi-structured and Structured Unstructured Structured Data & Content Agent/case Data Call logs, Web Logs, Observation Data Transcripts, Emails… Big Data & Business Integrate and Analyze Structured and Unstructured Data Organization / Analytics Enterprise Insights  Crime Intelligence  Statistical Reports  Predictive Models Distribution  Alerts & warning  Analytics Reports  Geo-spatial Display & Utilization generation  Relation Resolution  Deep Text analytics  Identity Resolution @pieroleo www.linkedin.com/in/pieroleo 19 Nove © 2012 IBM Corporation
  • 20. December 2012 Analytics challenge: Fusion reduces uncertainty by constructing context Required: tight integration to maximize context discovery Credit Loyalty Data Required: common practices followed FUSION finds by multiple standards for representing Michael Data uncertain data and uncertainty of all San Jose, CA Mother types, provenance, and lineage and Date other metadata Buyin Buyin Son g g Fact Birthday $560 DSLR DSLR today !! Discovery OR today Influencers Intent A $999 & NY Buying Spatial Reasoning a Sense Making DSLR & today ! Customer at Mall Temporal Reasoning Maximum Context For Customer in Store #42 Correlation Minimum Uncertainty Required: common APIs to enable $999 $560 sharing across the uncertainty Corroboration management pipeline In-Store Pricing (Evidence Combination) And Discounts ETC. No such common practices, standards or APIs exist today @pieroleo www.linkedin.com/in/pieroleo 20 © 2012 IBM Corporation
  • 21. December 2012 The value of analytics grows by incorporating new sources of data, composing a variety of analytic techniques, spanning organizational silos, and enabling iterative, user-driven interaction New format or usage of data Multi-modal Intent-to-buy trends demand forecasting Sources and types of data Segmentation- based market impact estimates Price-based demand forecasting Sales-based (own & competitors) demand forecasting Structured or standardized Low Scope of decision High @pieroleo www.linkedin.com/in/pieroleo 21 © 2012 IBM Corporation
  • 22. December 2012 Analytics toolkits will be expanded to support ingestion and interpretation of unstructured data, and enable adaptation and learning Adaptive Analysis Responding to context  Learn In the context of the Continual Analysis Responding to local change/feedback decision process Optimization under Uncertainty Quantifying or mitigating risk  Decide and Act s doh e M w N Optimization Decision complexity, solution speed e Predictive Modeling Causality, probabilistic, confidence levels Simulation High fidelity, games, data farming  Understand t Forecasting Larger data sets, nonlinear regression and Predict Alerts Rules/triggers, context sensitive, complex events Query/Drill Down In memory data, fuzzy search, geo spatial l anoti da T r Ad hoc Reporting Query by example, user defined reports  Report i Standard Reporting Real time, visualizations, user interaction Entity Resolution People, roles, locations, things  Collect and Relationship, Feature Extraction Rules, semantic inferencing, matching Ingest/Interpret Decide what to count; Annotation and Tokenization Automated, crowd sourced a aD w N enable accurate counting e Extended from: Competing on Analytics, Davenport and Harris, 2007 @pieroleo www.linkedin.com/in/pieroleo 22 © 2012 IBM Corporation t
  • 23. December 2012 Analytics solution development requires several interacting design steps Algorithm Composition and Invention Data Evaluation and Fusion Testing and Execution Optimization Streaming data Data mining & statistics Text data Optimization Multi-dimensional & simulation Semantic Time series analysis Fuzzy Geo spatial matching Video & image Network algorithms Relational New algorithms Social network ✔ Filtering and Business Rules Engine Composition and Data Acquisition Core Analytics Deployment Extraction Validation Packaging @pieroleo www.linkedin.com/in/pieroleo 23 © 2012 IBM Corporation
  • 24. December 2012 Agenda 1 Cognitive Systems Era  Data Centric ← Beyond Big Data  Statistical Analytics ← Beyond Machine Learning 2 Cognitive Systems Strategic challenges for Our Organizations 3 Statistical Analytics Strategy 4 Examples of Statistical Analytics Problems & Benefits @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 25. December 2012 Statistical Analytics Strategy Content Access Content & data Insight Distribution & Integration Organization Analytics & Utilization Disorganized Organized Investigation Knowledge And/OR Siloed Content added-value Accumulation & Content from Contend Distribution and Data From the chaos to the New visibility and Insight generation and order knowledge investigation support @pieroleo www.linkedin.com/in/pieroleo 25 Nove 25 © 2012 IBM Corporation
  • 26. December 2012 A full set of functional capabilities needed to support a A Statistical Analytics Strategy Content Access Content & data Insight Distribution Organization Analytics & Integration & Utilization Natural LP Social Media Analytics Content Management Inf. Brodcast News Monitoring Extraction Image Analytics Adv. Advanced User Profiles Analytics Process Management Enterprise Analytics Search Deep Question & Answer Content Federation & Mining Content Predictive Reporting & Dashboards Entity Analytics Analytics Master Data resolution & Business Network Visualization Management Relation Rules Adv. Case Management discovery Content Classification Standard Datawarehouse models Advanced Big Data models (streams and restfull data) Disorganized Organized Investigation Investigation And/OR Siloed Content added-value from Knowledge Content Contend and Data Accumulation & Distribution @pieroleo www.linkedin.com/in/pieroleo 26 Nove © 2012 IBM Corporation
  • 27. December 2012 Agenda 1 Cognitive Systems Era  Data Centric ← Beyond Big Data  Statistical Analytics ← Beyond Machine Learning 2 Cognitive Systems Strategic challenges for Our Organizations 3 Statistical Analytics Strategy 4 Examples of Statistical Analytics Problems & Benefits @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 28. December 2012 Examples of Statistical Analytics Benefits Retail Banking Customer Care Retail Customer Care Analyzing: Call logs, internal and external media, claim Analyzing: Call logs, online media For: Buyer Behavior For: Brand Reputation Management Benefits: Improve Customer satisfaction, marketing Benefits: Improve customer sat, marketing campaigns campaigns, find new revenue opportunities Healthcare Analytics Crime Analytics Analyzing: Care records Analyzing: Police records, Emergency calls… For: Clinical analysis; treatment protocol optimization For: Rapid crime solving & crime trend analysis Benefits: Better management of chronic diseases; optimized drug Benefits: Safer communities & optimized force deployment formularies; improved patient outcomes Insurance Fraud Automotive Quality Insight Analyzing: Insurance claims Analyzing: Tech notes, call logs, online media For: Detecting Fraudulent activity & patterns For: Brand Reputation Management Benefits: Reduced losses, faster detection, more efficient Benefits: Reduce warranty costs, improve customer claims processes satisfaction, marketing campaigns @pieroleo www.linkedin.com/in/pieroleo 28 Nove © 2012 IBM Corporation
  • 29. December 2012 Agenda Ongoing Research Project with University of Bari: Recognise a “Complex Event” from Social Media Data Students: Francesco Tangari Rocco Caruso @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 30. December 2012 Dipartimento di Informatica Università degli Studi di Bari Research challenge and its business value (1/2) A complex event has People Attributes a defined spatio- who is planning? who is going to Spatial temporal connotation: participate/attend?, who is interested Where is it and follows, which is the network located? it can be It involves one or created around the event… a square, a station, more individuals a virtual a place, (Who) that organize etc. where and/or Who everyone can Participate and/or are see the event followers to set up a defined action (what) Complex Event in a defined location, Argument & its Dimensions real or virtual, (where) what was What PROFILE Where planned in a given moment for the (when). Event? Whis is the topic and the A “flas mob” is an motivation?, When example of a complex es People event, other examples will dance, will are srikes, sport freeze, etc... Temporal events, protests, etc. The date and the time at which the event will take place, the date and the time where the event preparation will take place…. @pieroleo www.linkedin.com/in/pieroleo Un approccio Statistico per la Predizione di Flashmob da Reti © 2012 IBM Corporation Sociali 2
  • 31. December 2012 Research challenge and its business value (2/2 ) ….in the case of predicting a Flash Mob Leveraging social media data and •Ex. 2: Knowing that a flash mob will be generate insights used for the promotion of a new product, a about complex firm which is in competition on the same market can organize counter-action. business relevant phenomena by • Ex 2: A law enforcement org knowing that connecting the a flash mob will be organized for political purpose or for demonstration can dots effectively relocate law forces. @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 32. December 2012 Information about the Event are spread on a number of social media channels: An example of Flash Mob organization dynamic @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 33. December 2012 General System Context: First Prototype & Experimentation based on Twitter channel Twitter Channel (*) Flash Mobs Profile Recostruction & Alerts Information Extraction Who What FlashMob Where When Who (POS, named entities: person, Data Access organization, Locations, data, Event What FlashMob Where & Basic etc. High-level, concepts, wikification, etc..) Prediction Who Feature When & Alerting Extraction What FlashMob Where When Who (Tokenization, hashtags, (Clustering, Incremental What FlashMob Where URs, Geotags, social Social Network Clustering, Burst network metadata, etc.) Analysis recognition..) When (Clique, Relevant Nodes, Page Rank ndes, etc.. Implemented path Planed integration Analytics Consumers Acquiring tweets including the #flashmob hashtag (*) In our vision a number of “channels” should provide data (What’s up App for and/or the keyword “flashmob” to the system such as Facebook, YouTube, etc. As well as also smartphone, Social analytics and/or “flash mob” Other social analytics applications such as IBM COBRA or CCO, etc. @pieroleo client, etc. etc.) www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 34. December 2012 Working on real data and applying the prediction model Period: 1/gen – 29/Feb Alerts/Clusters = 59 Analyzed 5148 (English language) Tweets that included the word or the hashtag “flashmob” Generated in total 59 Flash Mob Alerts (clusters) involving 1267 tweets 20 Alerts correctly aggregated data about 20 Flash Mobs with an accuracy about of 100% @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 35. December 2012 In a new research phase we are now extending the predicton model to recostruct main Complex Event attributes Complex Event Attributes Hadoop CLUSERING CLUSERING DBSCAN DBSCAN COMPLEX COMPLEX EVENT EVENT PROFILER PROFILER NLP ANNOTATION EXTRACTOR EXTRACTOR TOOLS {1..N} HIVE DW HIVE DW Streaming JSON HDFS @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 36. December 2012 Agenda Wrap-up! @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 37. December 2012 My Personal IT Mind-Map Data Models World Instrumentation eBusiness Services/Legacy Applications Enterprise Data Pervasive Computing Storage (IMS, DBMS, Portals – Webization Internet of Things Etc,) Big Data Social-ization App-ization (structured & unstructured) Virtualization Web-App-ization Cloud Cloud Services IT Consumerization/BYOD Computing Cognitive Workload- Computing Optimizied Business Analytics Mobile Computing Parallel Computing Optimization Data Warehousing / Computing Models, Business Intelligence Social Business & Architectures & Styles Analytics - Information-based Intelligence Mobility = Conceptual connection, Evolution Path, Cause-Effect, etc. @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation
  • 38. December 2012 Grazie! @pieroleo www.linkedin.com/in/pieroleo © 2012 IBM Corporation