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

Transcription

1 Cognitive Solutions in the Context of IBM Systems Cognitive Analytics / Integration Scenarios / Use Cases Mano Srinivasan Open Source Solutions Architect manojs@sg.ibm.com

2 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 2

3 What is Cognitive Analytics? An Introduction

4 Cognitive Analytics in the Context of Big Data IBM Watson drives optimized Outcomes 1 Understands natural language and human speech 2 Generates and evaluates hypothesis for better outcomes 99% 60% 10% 3 Adapts and Learns from user selections and responses 4

5 Why Cognitive is Intensive.. Normal Image What Cognitive Algorithm Sees 5

6 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) 6

7 Cognitive Analytics in the Context of Big Data Key Drivers The need for cognitive analytics is driven by the confluence of SoLoMo (Social, Local, Mobile), Big Data, and Cloud Veracity Velocity Variety Volume Cognitive Systems 7

8 Topics and Questions to be addressed What is Cognitive Analytics?? What does IBM has to offer? Key Use Cases and Integration Scenarios Summary and Takeaway 8

9 The Evolution of Analytics Descriptive Analytics Predictive Analytics Prescriptive Analytics Cognitive Analytics Descriptive Predictive Prescriptive Cognitive 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 Leverages 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 Synthesizes big data, mathematical and computational sciences, and business rules to suggest decision options Takes advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option Pertaining to the mental processes of perception, memory, judgment, learning, and reasoning Range of different analytical strategies that are used to learn about certain types of business related functions Natural language processing 9

10 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 How can everyone be more right.more often? Cognitive How can we learn dynamically? IOP IBM Branded Big Data and Analytics Platform Prescriptive How can we achieve the best outcome? Predictive What could happen? Descriptive What has happened? Business Value Information Layer How is data managed and stored? 10 Source: IBM and IDC Business Analytics, Business Rules Management Systems 2012 WW market estimates

11 Cognitive Business and its Analytics Foundation in IBM A Watson-centric View Solutions: Behavior Based Customer Insight Regulatory & Compliance Analytics Multi-Channel Fraud Analytics Offerings: Watson Engagement Advisor Watson Discovery Advisor Watson Policy Advisor Watson Decision Advisor Watson Company Analyzer Products: Watson Explorer Watson Analytics Watson Curator Applications: Watson for Wealth Management Watson for Oncology Chef Watson Platform: Watson Services on BlueMix Watson Developer Cloud (Bluemix) Watson Tooling Watson Health IBM Analytics IBM Analytics Platform: DB2 Analytics Accelerator QMF Spark on z/os DataWorks DataWorks Forge Data Science Experience (DSX) InfoSphere Information Server Information Governance Catalog... Source: and 11

12 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 12

13 Open Source Products your way.. 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) vrealize 13 13

14 Leverage all your data without moving it Apache Spark A unified analytics platform Spark CICS IMS WAS Spark Spark Spark Power Spark Spark Spark Spark Spark Spark IMS x86 Leverage non-z data DB2 DB2 VSAM Linux on z Systems Leverage Linux on z virtualization benefits z/os Leverage z/os data and transactions 14

15 IBM Open Sources its Machine Learning Algorithm.. 15

16 Apache SystemML : Client Use Case 16

17 Topics and Questions to be addressed What is Cognitive Analytics?? What does IBM has to offer? What are the key Use Cases and Integration Scenarios? Summary and Takeaway 17

18 Behavior Based Customer Insight (BBCI) for Banking Customer 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

19 Behavior Based Customer Insight (BBCI) for Banking Overview Data Sources Internal External Business Use Cases Predictive customer insight Cashflow Analysis Predictive analytics Customer Profile Census Data Financial Event Prediction Data models Analytical models Structured Transaction Data Account Data... Behavior-based Segmentation Churn Propensity Analysis Upsell Propensity Scoring components Sentiment analytics tone analyzer Rest APIs Insight consumption Interaction Data (structured) Product Propensity Tone Analyzer Non-Structured Interaction Data ( s)... Peer Segmentation Life State Prediction 19

20 Behavior Based Customer Insight (BBCI) for Banking Leveraging IDAA for BBCI IBM z Systems Application Layer Application(s) Wrapper Service In-DB Transformation BBCI Rest API DB2 for z/os & DB2 Analytics Accelerator ETL BBCI DB SQL Queries SPSS Analytical Models SPSS Collaborative & Deployment Services BBCI 20

21 Target Solution Architecture Use Case: Web and Mobile Bank Application to increase User Experience IBM z Systems Scala / Python / Java / R / SQL Application Layer Application(s) SQL Spark / R DB2 for z/os & DB2 Analytics Accelerator Split_Query_1 Big SQL Split_Query_2 Hive / HCatalog HDFS / (GPFS) IOP & BigInsights BigIntegrate (optional) Aggregation and transformation of new with historical data (Apache Flume) SOAP Envelops MetaInformation BigIntegrate IBM Streams Analytical model Scoring deployment Spark Streaming 21

22 Cognitive Solution IBM Systems Prespective

23 Watson Cognitive Solutions Analytics and Machine Learning Open source Analytics Integration Interactions with varied Data stores Legacy Integration Parallelization and GPUs Data Compression IO Bandwidth Memory and Cache Sizes Storage SAS / SSD 23

24 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. Parallelization and GPUs 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. General Purpose CPU - Multicore GPU Thousands of Cores Common Programming Languages for offloading. 24

25 Systems Hardware 9 Resilient Memory Bandwidth 9 SMT Thread Per Core 9 Cache Latency 9 Virtualization and On-Demand creation of Clusters Storage Configuration 9 IBM Flashsystems are optimized for high volumes of unstructured data for Analytics. 9 Supplement your existing Analytic s infrastructure. IBM FlashSystem 9 Decrease overall response times. 9 Increase efficiency/utilization across the IT stack. 9 Completely eliminate storage performance issues 25

26 CAPI ( Coherence Accelerator Processor Interface) CAPI Attached Flash Optimization Read/Write Syscall strategy() strategy() Application FileSystem LVM iodone() iodone() Disk & Adapter DD 20K Instructions < 500 Instructions Attach flash memory to POWER8 via CAPI coherent Attach Posix Async I/O Style API Shared Memory Work Queue Application User Library aio_read() aio_write() Pin buffers, Translate, Map DMA, Start I/O Interrupt, unmap, unpin,iodone scheduling Issues Read/Write Commands from applications to eliminate 97% of instruction path length CAPI Flash controller Operates in User Space 26

27 SIMD (Single Instruction Multiple Data) processing Increased parallelism to enable analytics processing Smaller amount of code helps improve execution efficiency Process elements in parallel enabling more iterations Supports analytics, compression, cryptography, video/imaging processing Value Enable new applications Offload CPU Simplify coding Scalar SINGLE INSTRUCTION, SINGLE DATA 27 A1 A2 A3 B1 B2 Sum and Store B3 C1 C2 C3 Instruction is performed for every data element SIMD SINGLE INSTRUCTION, MULTIPLE DATA INSTRUCTION A3 A2 A1 B3 B2 B1 Sum and Store C3 C2 C1 Perform instructions on every element at once

28 Summary and Takeaway

29 Close the gap IBM Systems and Storage Integrated Hardware Data Analytics Software Business Process 29

30 Summary and Takeaway 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 30

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