IBM Systems and Cognitive Solutions Share · Cognitive Solutions in the Context of IBM Systems ... business measures ... Watson Explorer Watson Analytics

© 2016 IBM Corporation

Cognitive Solutions in the Context of IBM SystemsCognitive Analytics / Integration Scenarios / Use Cases

Mano SrinivasanOpen Source Solutions Architect [email protected]

© 2016 IBM Corporation2

Topics and Questions to be addressed

What is Cognitive Analytics

What does IBM has to offer and What are the key Use Cases?

Integration Scenarios and the role of Open Source, incl. SystemML and Apache Spark

Summary


What is Cognitive Analytics?An Introduction


99%60%10%

Understands natural language and human speech

Adapts and Learns from user selections and responses

Generates and evaluates

hypothesis for better outcomes

3

2

1

Cognitive Analytics in the Context of Big Data IBM Watson drives optimized Outcomes


Why Cognitive is Intensive..

Normal Image What Cognitive Algorithm Sees


When Would we use Cognitive Analytics?

When patterns exists in our data− Even if we don’t know what they are

We can not pin down the functional relationships mathematically − Else we would just code up the algorithm

When we have lots of (unlabeled) data− Data is of high-dimension

• High dimension “features” (For example, sensor data)


The need for cognitive analytics is driven by the confluence of SoLoMo (Social, Local, Mobile), Big Data, and Cloud

VeracityVeracity VarietyVariety

VelocityVelocity VolumeVolume

Cognitive Systems

Cognitive Analytics in the Context of Big Data – Key Drivers



What is Cognitive Analytics ??

What does IBM has to offer?

Key Use Cases and Integration Scenarios

Summary and Takeaway


The Evolution of Analytics

CognitiveAnalytics

PredictiveAnalytics

PrescriptiveAnalytics

DescriptiveAnalytics

Descriptive“After-the-facts” analytics by analyzing historical data Provides clarity as to where an enterprise or an organization stands related to defined business measures Applied to all LoB for fact finding, visualization of success and failure

CognitivePertaining to the mental processes of perception, memory, judgment, learning, and reasoningRange of different analytical strategies that are used to learn about certain types of business related functionsNatural language processing

PredictiveLeverages data mining, statistics and ML algorithms, etc. to analyze current and historical data to predict future events and business outcome. Discovers patterns derived from historical and transactional data to optimize business measures

PrescriptiveSynthesizes big data, mathematical and computational sciences, and business rules to suggest decision optionsTakes advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option


Scope of Advanced Analytics – leading towards Cognitive Business

IBM Analytics breadth covers the full spectrum of decisions IBM z Analytics contributes and enables this breadth of analytics

Descriptive

Prescriptive

Predictive

Cognitive

What has happened?

What could happen?

How can we achieve the best outcome?

How can we learn dynamically?

IBM BrandedBig Data and

AnalyticsPlatform

IBM BrandedBig Data and

AnalyticsPlatform B

usin

ess

Valu

e

Information Layer How is data managed and stored?

How can everyone be more right….more often?

Source: IBM and IDC Business Analytics, Business Rules Management Systems 2012 WW market estimates

IOP


Cognitive Business and its Analytics Foundation in IBMA Watson-centric View

Offerings:

Applications:

Watson ExplorerWatson AnalyticsWatson Curator

Watson Services on BlueMixWatson Developer Cloud (Bluemix)Watson ToolingWatson Health

Solutions:

IBM Analytics

Source: http://www.ibm.com/analytics/us/en/ and http://www.ibm.biz/cognitivera

Watson Engagement AdvisorWatson Discovery AdvisorWatson Policy AdvisorWatson Decision AdvisorWatson Company Analyzer

Watson for Wealth ManagementWatson for OncologyChef Watson

Products:

Platform:

Behavior Based Customer Insight Regulatory & Compliance AnalyticsMulti-Channel Fraud Analytics

IBM Analytics Platform:

DB2 Analytics AcceleratorQMF 11.2.1Spark on z/OS

DataWorksDataWorks ForgeData Science Experience (DSX)

InfoSphere Information ServerInformation Governance Catalog. . .


The key is open standards

XML-based industry standard

Defines statistical and data mining models to share across applications

Eliminates the need for custom code

Compatibility | Enterprise Grade | Simple Management


Big Data and Analytics HPC Cloud

IBM Blue Stack – primary BD&A

(Note: DB2 BLU focus remains for AIX & Linux)

ISV Stack– Data focus

ISV Stack– Application focus Cluster focus MSP focus

• BigInsights w/ IOP – IBM Data Engine for Hadoop & Spark

• BigInsights + Analytics - IBM Data Engine for Analytics

• Cognos/SPSS – IBM Solution for Analytics

•WebSphere

• Relational DBs;MariaDB, PostgreSQL, EnterpriseDB

• NoSQL DBs;MongoDB, Redis, Cassandra, Neo4J

• In-Memory DBs;Hana, DB2 Blu

• SAP applications with Hana + S4Hana

• Infor and PegaSystems with EnterpriseDB

• Magento, SugarCRM, WordPress with MariaDB

•NFV for Telco

• Elastic Storage Server

• Life Sciences / Genomics

• Research, Oil and Gas, Seismic, CAE

•Climate Modeling, Weather Prediction

• EasyScale

• Hybrid Cloud opportunities (e.g. SoftLayer, ScaleMatrix, etc)

13

vRealize

Open Source Products your way..


Linux on z Systems

Power

Spark

DB2

Spark

Spark Spark

x86

Spark Spark

Leverage non-z data

Leverage Linux on z virtualization benefits

Leverage z/OS data and transactions

CICS WAS

DB2 VSAM

z/OS

IMS

IMS

Spark

Leverage all your data without moving itApache Spark – A unified analytics platform

Spark

Spark

Spark


IBM Open Sources its Machine Learning Algorithm..


Apache SystemML : Client Use Case



What is Cognitive Analytics ??

What does IBM has to offer?

What are the key Use Cases and Integration Scenarios ?



Behavior Based Customer Insight (BBCI) for BankingCustomer Details View in Branch Office Application

The view includes, for instance:− Customer current products − Products that could be offered − Levels related to the possibility for the customer to be in overdraft, to churn etc.− . . .


Behavior Based Customer Insight (BBCI) for BankingOverview

Predictive customer insight− Predictive analytics− Data models− Analytical models− Scoring components

Sentiment analytics− E-Mail tone analyzer

Rest APIs− Insight consumption

E-Mail Tone Analyzer

Cashflow Analysis

Product Propensity

Upsell Propensity

Churn Propensity Analysis

Behavior-based Segmentation

Financial Event Prediction

Peer Segmentation

Life State Prediction

CustomerProfile

TransactionData

AccountData

InteractionData

(structured)

InteractionData

(e-Mails)

CensusData

Data Sources

Stru

ctur

edN

on-S

truct

ured

Internal External

. . .

. . .

Business Use Cases


Behavior Based Customer Insight (BBCI) for BankingLeveraging IDAA for BBCI

DB2 for z/OS&

DB2 AnalyticsAccelerator

IBM z Systems

Application(s)

In-DBTransformation

BBCI

BBCI Rest API

BBCI DB

SQL Queries

Application Layer

Wrapper Service

SPSS

Ana

lytic

al M

odel

s

SPSS

Col

labo

rativ

e &

Dep

loym

ent S

ervi

ces

ETL


Target Solution ArchitectureUse Case: Web and Mobile Bank Application to increase User Experience

DB2 for z/OS&

DB2 AnalyticsAccelerator

IOP & BigInsightsHDFS / (GPFS)

IBM z Systems

Big SQL

Hive / HCatalog

Application(s) SQL

Split_Query_1Split_Query_2

Application Layer

SOAP EnvelopsMetaInformation

(Apache Flume)

BigIntegrate

Spark / R

IBM StreamsAnalytical model

Scoring deployment

Scala / Python / Java / R / SQL

Big

Inte

grat

e(o

ptio

nal)

Agg

rega

tion

and

trans

form

atio

nof

new

with

his

toric

al d

ata

Spark Streaming


Cognitive Solution – IBM Systems Prespective


Analytics and Machine Learning

Watson – Cognitive Solutions

Interactions with varied Data stores

Legacy Integration

Open source Analytics Integration

Parallelization and GPUs

IO Bandwidth

Data Compression

Memory and Cache Sizes

Storage – SAS / SSD


General Purpose CPU - Multicore GPU – Thousands of

Cores

GPUs are well suited for parallel processing tasks. They have thousands of core that can work in parallel.

Significant Analytics Acceleration can be achieved with concurrent execution of Analytics workloads.

Common Programming Languages for offloading.

Parallelization and GPUsParallelization and GPUs

Processor Caching

Data and function calls are placed in the Caches.

Effiency and Latency improvement, when data addresses are kept in caches.

Good Cache hierarchy improves overall Performance.


IBM FlashSystem

IBM Flashsystems are optimized for high volumes of unstructured data for Analytics.

Supplement your existing Analytic’s infrastructure.

Decrease overall response times.

Increase efficiency/utilization across the IT stack.

Completely eliminate storage performance issues

Resilient Memory Bandwidth

SMT – Thread Per Core

Cache Latency

Virtualization and On-Demand creation of Clusters

Systems Hardware

Storage Configuration


CAPI Attached Flash Optimization

Issues Read/Write Commands from applications to eliminate 97% of instruction path length CAPI Flash controller Operates in User Space

Pin buffers, Translate, Map DMA, Start I/O

Application

LVM

Disk & Adapter DD

Read/WriteSyscall

strategy() iodone()

FileSystemstrategy() iodone()

Interrupt, unmap, unpin,Iodone scheduling

< 500 Instructions

ApplicationPosix AsyncI/O Style API

User LibraryShared Memory Work Queue

aio_read()aio_write()20K Instructions

Attach flash memory to POWER8 via CAPI coherent Attach

CAPI ( Coherence Accelerator Processor Interface)


Increased parallelism to enable analytics processing

A3 B3 C3

A2 B2 C2

ScalarSINGLE INSTRUCTION, SINGLE DATA

SIMDSINGLE INSTRUCTION, MULTIPLE DATA

Instruction is performed for every data element

Perform instructions on every element at once

Sum and Store

C1

C2

C3

A1 B1

A2 B2

A3 B3

INSTRUCTION

A1 B1 C1

Sum and Store

ValueEnable new applicationsOffload CPUSimplify coding

Smaller amount of code helps improve execution efficiencyProcess elements in parallel enabling more iterationsSupports analytics, compression, cryptography, video/imaging processing

SIMD (Single Instruction Multiple Data) processing




Close the gap

Integrated Hardware | Data | Analytics Software | Business Process

IBM Systems and Storage



Integration of various offerings is key to enable Cognitive Business− IOP and BigInsights− Big SQL− Spark Integration

IBM Systems contributes to Cognitive Business by making z/OS and other data stores easily accessible and consumable for Cognitive Analytics tasks− DB2 Analytics Accelerator− DataWorks with Data Science Experience (DSX) − Spark on z/OS

Industry specific opportunities for z Analytics to enable Cognitive Business, e.g.− FinTech


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IBM Systems and Cognitive Solutions Share · Cognitive Solutions in the Context of IBM Systems ... business measures ... Watson Explorer Watson Analytics