Ipedo Company Overview

Ipedo XIP Overview for {Customer Name} July 2006 {Name}

Agenda

Introductions

EII Opportunities for <customer>

Ipedo XIP Overview

{Customer Name} {Issues/Fits/Needs}

Summarize issues or needs of the prospect

Ipedo Background

Focus on Enterprise Information Integration (EII) software

Customers and partners worldwide

Based in Redwood City, CA

Founded in 2000; Venture backed by Draper Fisher Jurvetson and AsiaTech Management

50+ Customers Quick Facts Influential Partners

EII Market Opportunity Growing Batch Realtime Process Data EAI EII ETL Sources: * Forrester Research;  TDWI/BI Research 10/05

Target is a user

Supports ad hoc applications

Results moved in realtime at query time

Supports relational + XML

Data warehouse can be source

EII EII

Target is a database

Supports scheduled applications

Batch; data moved ahead of time

Supports relational + multidimensional

Data warehouse is the target

ETL EII Use Plans 

EII Brings Advantages to New Projects Solution Key Advantages of EII Dashboards

Intelligently combine company-wide information

Provide view customization per context or user

Maintain or enhance enterprise security model

Risk Management BI / Reporting

Enable better reports with up-to-date information

- Access diverse data sources via queries, not code

- Mix real-time data with historical information

Extract key information across company data silos

Reduce cost of compliance

Manage complex XML schemas such as FpML

EII Enables Integration Infrastructure Solution Key Advantages of EII Metadata Management

Consolidate metadata across the enterprise

Exchange data models with other modeling tools

Append business definitions to integrated data

Data Service Layer for Service-Oriented Architectures (SOA) Master Data Management (MDM)

Maintain consistent view of core business entities

Avoid political issues around data ownership

Reduce data replication

Create data services layer that simplifies SOA

Allow universal info exchange as Web Services

Manage both HTTP and SOAP Web Services

Use Case: Real-Time Reporting Reporting Trade Data Market Data Risk Data Oracle OLTP Sybase OLTP Web Service EII Server Virtual DB

Use Case: Data Warehouse Prototyping Relational Databases Sales Ops Applications/Marts EII Server Virtual DB Data Warehouse Reporting & BI Tools Migrate ETL Prototype Permanent

Use Case: Database Migration - Insulation from Change Reporting Trade Data Trade Data Sybase DB2 EII Server Virtual DB Migrate

Use Case: Migrating to SOA Relational Databases Sales Ops Applications/Marts EII Server Virtual DB

EII and SOA:

Make relational models available as services

Protect against a performance hit

Maintain adequate governance

Let DBAs be DBAs, and developers be developers

View 1 View 2 Registry Application 1 Application 2

Ipedo Creates a Virtual Data Services Layer from Multiple Remote Data Sources Ipedo XIP Sales Business Unit A Business Unit B Customer System Finance System Operations System Finance System Operations System Mfg. System Finance Marketing Subset Data Distributed Data Mixed Data Query Query Query Query

Cross business boundaries

No data replication

Mix data types in real time

TIBCO SQL Server SAP Oracle SQL BAPI SOAP WSDL JMS SOAP WSDL SQL View 2 Operations View 1 Sales by Region View 3 Financials by Account View 4 Financials Quarterly

Ipedo Provides a Data Services Layer

Views Virtualize Data Access Custom Metadata Data Source Connections Data Mappings Transformations Output Schema View Caching Policy Validation Security View Data Warehouse Relational Database Web Services Packaged Apps XML Documents Message Queues Ipedo Views A View is a metadata construct. It contains no data, unless explicitly cached. When a query is executed against a View, Ipedo returns the data to fulfill the request.

Deploying Ipedo XIP Ipedo XIP Development/QA Ipedo XIP Monitor & Manage Production App Server Reporting Dashboards Web Apps SOA End Users

Views are designed, developed and tested

Views are then deployed to production server

Additional production servers can be added to support more users

View 1 View 2 View 3 View 1 View 2 View 3 Design Time Runtime Deploy

Ipedo XIP Solves Integration Challenges  Faster Development $ Cheaper Deployment  Lower Risk $ Data discovery tools and wizards for RDB, XML and SOA  Data source introspection with statistic and key gathering  Metadata collection and management $ Drag and Drop View creation $ Automatic SQL and XQuery generation  Rule and aggregate tools  Automatic XML to RDB bridging $ Automatic query plan generation  Cost-based query optimization  Policy-driven caching for SQL and XML result sets $ Virtualized data access without replication  Customizable result set presentation  Adjustable configurations for scaling up, down or out  Detailed data access controls  Performance thresholds and limits  Data lineage and query forensics reporting Automated Discovery Powerful Modeling Intelligent Optimization Flexible Deployment Comprehensive Management Where does our data reside and in what format? How can I quickly combine all the data I need for my application? How can I meet my users’ performance needs and not break existing systems? How can I tailor data to suit the various and changing user needs? How can I make sure we adhere to our security and data governance policies?

Ipedo Speeds Discovery and Data Combination Finance Sales Ops Source Introspection Data Discovery Metadata Collection Tables, Keys, Statistics Stored Proc, Keys, Statistics Services, Methods Catalog Read Catalog Read UDDI Lookup Design Time Automated Discovery Powerful Modeling GUI View Creation Auto-Query Generation Intelligence SQL Statement XQuery Statement Metadata Lookup Metadata Addition Metadata Catalog Rules Aggregates

Auto-Discovery Simplifies Data Integration XIP > Automated Discovery Lists all tables Displays column names Shows data types Generates SQL DDL Ipedo automatically Introspects remote data sources.

Custom Metadata Annotates Data XIP > Automated Discovery Ipedo Metadata Catalog Add custom metadata to annotate: * data sources * remote tables * columns * views Retrieve metadata from Ipedo system tables using standard SQL system functions

GUI View Builder Simplifies Development XIP > Powerful Modeling ‘ Drag and Drop’ tables into the design pane Build logic using intuitive graphical tools: TotalPrice>$50,000 Ipedo automatically generates SQL, which can be modified

Ipedo Ideal for SOA

Consuming Web Services

Blending multiple web services

Conditional invocation of web services in a specific order

Combining web services with other data sources

Processing Web Services

Business rules checking on web services data

Caching of web services data for reuse

Ipedo uses a query-based approach, preserving the format of the original data

‘ Web Services’ Tables Allows SQL on XML Customer Records (Oracle) Market Exposure (SOAP Web Service) Credit Record (HTTP Web Service) XML XML Rel. Rel. XMLQuery XMLQuery XML Core SQL Core BI/Reporting Client ODBC / JDBC SQL SQL SQL SQL Data Sources Remote Tables Relational View Use familiar SQL syntax with Web Services: Join, Group By, hints XIP > Powerful Modeling

Map Web Services to Relational Tables XIP > Powerful Modeling Select a Web Service Map results to relational columns Modify data types to fit your application’s needs

Ipedo Application Adapters Integrate Data Into Ipedo Framework

Pre-built templates and objects

Accessible via SQL, Web Services, XQuery

Visual drag and drop, API/script access

Broadest range of applications (Librados)

Simple pricing structure ($15K/CPU per adapter)

Application View Templates Application Data Objects Oracle Integration Tables Integration Objects API BAPI SOQL Oracle Siebel SAP R/3 Salesforce.com Common Reports Common Objects

Ipedo Approach to SAP BW Data

Librados adapters for relational data

Simba OLAP adapters for cube data

Best-in-class vendors of standards-based advanced analytical data adaptors

Perform more complex analyses using broader range of data sources

Integrate SAP BW with other data sources

Ipedo Optimizes Data Access Data Virtualization Customizable Presentation Deployment Time Flexible Deployment Intelligent Optimization Query Optimization Query Execution Cache Policy Cost-Based Optimizer Query Plan Advanced Tuning XML View Cache SQL View Cache Schedule Refresh Policy

Result Set Streaming

Query Throttling

Concurrency

Hybrid Intermediate Result Processing

Virtual View Virtual View Virtual View SQL/JDBC SQL/ODBC XML/WS XML/WS JSP/HTTP Order View Risk View Leads View

Ipedo Appears as a Database

External applications see tables within Ipedo

Each table can contain data from disparate sources (relational or Web Services)

XIP > Flexible Deployment

Performance Optimization

Cost-Based Query Optimization

CBO means knowing how a query will perform; Rules-based optimization means guessing

Examines statistics in remote data sources

Size of database (cardinality)

Indexes - presence, type, selectivity

Allows user to assign cost function to remote procedure

Determines join order and join algorithm based on “cost” of performing query

Minimizes intermediate results to speed performance

CBO greatly enhances EII performance

XIP > Intelligent Optimization

Query Plan Shows Optimization Approach

Automatic Query

Plan Generation

Join Order

Join Algorithms

Pushdowns

Joins

Predicates

Aggregates

Advanced Tuning Provides Ultimate Control

Hints

override Ipedo’s query engine with domain-specific knowledge

SQL pass-through

Ipedo sends data source specific SQL to the data source for execution

Polymorphic remote function mapping

XIP > Intelligent Optimization pass sql "dbcc show statistics (customers, zipcode_stat)" to mssqlsvr select a.* from properties joinOrder=fixed orders o, customers c properties joinStrategy=NESTEDLOOP where c.c_customerid = o.o_customerid

Query Execution

Result Set Streaming

Immediate response to user

Query Throttling

Control load on back-end systems

Concurrency

Support large numbers of users with optimal performance

Hybrid memory-disk intermediate result set processing

Manage memory to handle extremely large data sets

Flexible Security Model

Common user credential across data sources

Dynamic session-based user credentials for data sources

View-based access control

Intelligent Caching Cuts Response Time, System Load

Speeds Response to Repeated Queries

Reduces Load on Back-End Data Sources

Cache relational or XML data natively

Sub-queries on cache

Scheduled or on-demand cache loading

Automatic cache invalidation

Top-Down Modeling Tools Can Create Tables and Views in Ipedo 1. Build Logical Views In enterprise modeling software 2. Export Data Definitions Using standard ODBC 3. Select Options For schemas, tables, columns, indexes 4. Customize DDL for Ipedo Syntax reflects virtual table construct 5. View Model in Ipedo All metadata appears, available for integration

Reuse Ipedo Views in Modeling Tools 1. Build Views in Ipedo Map tables and create relationships across disparate data sources 2. Reverse Engineer Start with blank template inside ERwin 3. Select Options Choose what information to import 4. Model Appears in ERwin Tables, metadata, joins, etc., available to share and reuse

Comprehensive Monitoring and Reporting Simplifies System Management Partners Finance Marketing Management Dashboard

Users

Session Details

Session Controls

Query Controls

Data Sources

Source Status

Source Impact

Thresholds

Limits on Views

Limits on Users

Data Lineage

View Source Search

Historical Access Reports and Search

Ipedo XIP Users Administrators Ipedo Billing View Order View Risk View Leads View

Detailed User and Source Management Session Status XIP > Comprehensive Management Database Availability See Query Plan Kill Session or Cancel Query

Ipedo Facilitates Data Governance Where is the information in this view coming from? How does change to data source affect downstream views?

Dynamic Failover Redirection

Many large customers have database replicas worldwide - want EII Views to failover to replica database

Ipedo XIP will failover after it reaches retry limit

SOA Sybase 1 Oracle 2 Oracle 1 New York London New York Tokyo Sybase 2

Enterprise Class Product

Clustering and Failover - support for standard clustering environment

Scales to manage millions of documents and terabytes of storage

Industry-standard management APIs

Robust archival & backup capabilities

Automated schema migration

Integrated document versioning and journaling

Ipedo XIP Components SQL Engine • Query Optimization XQuery Engine • Query Optimization Federation Engine • Query Orchestration • Statistical Analysis Data Services Manager • Web Services • SQL Data Services • XQuery Data Services • APIs • Web Tag Library • Outbound Pipelines Rules / Intelligence Engine • Visual Rules Wizard • Rules Processing • Compound Aggregate Indexing Integration / Transformation Engine • Relational Views • XML Views • Graphical View Builder • Feeds • Inbound Pipelines Metadata Manager • Sources • Mappings Cache Manager • Relational • XML XML Store • Schema Management

HP - Finance Dashboard

Application

Financial Dashboard

Challenges

Accessing data from disparate sources

Frequently changing data needs

Requirement to keep interface simple

Granular security model

Scalability for 25,000 users

Solution

Use Ipedo to aggregate data from multiple sources, filter using XML, and transform for delivery into HP’s internal portal,

Results

Makes new information available for real-time business decisions

My Dashboard Financial Databases (Oracle) Data Warehouse (Hyperion Essbase) MS Word Analysis Reports Corporate Portal Framework Ipedo XIP Product View Region View Account View Channel View

Assurant - Insurance Policy Management

Application

Insurance Company: next-generation policy management system

Challenges

Manage large transaction volumes of policies in ACORD XML schema

Integrate with complex workflow processes using a service-oriented architecture (SOA)

Maintain uptime in high-availability environment

Solution

Use Ipedo to manage and integrate information from underwriting, accounting, claims processing and billing systems,

Results

Streamlines operations, improves response time to inquiries

Billing Database Ipedo XIP Profitable Accounts Claims Status Expensive Claims Arrears Sales View Workflow View Claims View Acct. View Claims System Acct. System Web Services Policies ACORD Schema

Sun - Customer Service

Application

Customer Service

Challenges

Combining XML messages, Web Services, databases, other data

Connecting with external risk engine

Providing rapid response to inquiries

Managing huge data volumes

Solution

Embed Ipedo into Sun application to deliver risk analysis as a service

Results

Improves system uptime and availability and reduces incident volume and cost

Customer Reference Data (Oracle) Risk Engine (Fair Isaac) System Information (XML) Preventive Services Ipedo XIP Hardware View Software View Account View Risk View Risk Assessment Advanced Diagnostics System Configurations Web Services JDBC

The Ipedo Difference

High Performance

Cost-based Query Optimization

Intelligent Caching

Investment Protection

Choice of data model: Relational (SQL) or XML (XQuery)

Choice of SOA deployment model – WSDL/SOAP or REST

One cohesive product

Seamless Deployment

Fully scriptable with complete SQL DDL support

Integrated Data Lineage and Impact Analysis reporting

Comprehensive APIs: Java, Web Services, .NET

Highly Scalable

Concurrency for hundreds of simultaneous users

Easily accommodates additional data sources

Additional Resources 2005 EII Survey Guide to EII ROI Guide

Background Slides

Enterprise Information Integration is…

… a new kind of virtual data integration technology that makes several remote data sources appear as one local database.

Ipedo XIP: Integration + Intelligence Vision: Intelligence without boundaries: Give business decision makers “information on demand, from anywhere, at any time” Product: Ipedo XIP integrates and manages information from disparate and complex data sources on demand

EII Generates Real Financial Benefits

Developer Productivity

EII allows developers to write to one, common interface to access multiple data sources, reducing the development cost for new applications

What % of your IT budget is spent on data integration development?

Analyst Utilization

EII allows analysts to simplify the data collection process and spend more time producing information and less time gathering data

How much time do your analysts spend collecting data?

Increased Flexibility

EII allows companies to respond quickly to changes in market conditions without incurring a major IT investment

What effort is involved to link new data sources to existing reports or applications?

Data Consolidation

Using EII to create a virtual centralized database can cut the cost of data maintenance, and reduce the need for data replication

How much does it cost to deploy each new data mart?

Integration Platform Built for Developers/Integrators Data and Cache Management • TTL • Performance • Versioning Publishing and API Access • XML • JSP Tag Lib • JDBC/ODBC • .NET • WS/SOA Transformation & Virtualization • XML Schema • CSV • SQL <-> XML • Pipelines Connectivity • Relational Views • XML Views • SOA/WS Views • JMS Feeds • HTTP Views Query Management • XQuery • SQL • Cost-based Optimizer Rules Processor • SQL SP • XQuery Modules • Java Functions Security • ACLs • Roles Management • JMX • JSP • APIs GUI Tools • View Builder • Query Builder • Rules Builder

EII Brings Advantages to New Projects - Create data services layer that simplifies SOA MDM - Maintain consistent view of core business entities - Avoid political issues around data ownership SOA - Allow universal info exchange as Web Services Solution Key Advantages of EII Dashboards - Intelligently combine company-wide information - Provide view customization per context or user Risk Management - Extract key information across company data silos - Reduce cost of compliance BI / Reporting - Enable better reports with up-to-date information - Access diverse data sources via queries, not code

Metadata Slides

Views Use Metadata Extensively Ipedo XIP View Output Schema Transformations Data Mappings Data Source Connections View Definitions of Fields Business Definitions Caching Policies Security Primary / Foreign Keys Security Data Source Metadata Source Repository Ipedo

Metadata Flows Throughout the Framework Metadata Manager Enterprise Data Sources Introspection Ipedo XIP Metadata Repositories Metadata Mgmt/Mod Tools Databases Web Services XML Doc Collections Other Data Sources Supported by Ipedo Modeling Tools BI/Reporting Tools SOA Tools App Dev Tools JDBC/ODBC/SQL WSDL/UDDI JDBC/ODBC/SQL WSDL/UDDI XMI JDBC/ODBC/SQL JDBC/ODBC/SQL XMI/Proprietary

Additional Customer Case Studies

Anhui Electric - Fraud and Risk Management

Application

Power Utility Billing Risk Management Dashboard

Challenges

Forecasting business risk

No aggregated meter monitoring data to prevent rampant theft of power

Lack of credit information on new customers

Solution

Use Ipedo to integrate energy meter collection system, customer databases and financial systems across multiple sites

Results

Significant reduction in fraud and theft; better management reporting

Billing Database Customer Database Energy Meter Collection System Ipedo Credit View Theft View Risk View Forecast View

Application

Ring tone, music, and mobile game digital rights management platform

Challenges

Ability to address diverse content and transaction data types

Adapting rapidly to changing market and customer requirements

Scalability for millions of buyers and millions of content assets

Solution

Use Ipedo to integrate user, payment, and ring tone/game metadata for presentation through Web interface

Results

Allows rapid response to customers and easy integration of new content libraries into client Web sites

Navio Card Navio Merchant Navio Store ASP Applications Report Server Title Manager Content Lockbox Digital Commerce Engine Ipedo XIP Ipedo XIP Ipedo XIP Navio - Rights-Based Commerce Web Storefront P2P Net

HP OpenView - Customer Service Portal

Application

Customer Service Portal

Challenges

Flexible search interface

Frequently changing content

Scalability for global users

High Availability

Solution

Use Ipedo to manage granular content and deliver in response to Web-based user inquiries

Results

Improve customer experience by directing users to relevant software downloads or reference materials

Customer Service Marketing Databases (RDBMS) OpenView Web Site Ipedo XIP Product Info Customer Info Marketing Info

Pharma R&D Data Integration Ipedo Cuts Costs, Speeds Discovery BEFORE IPEDO AFTER IPEDO

Too many data sources

Unable to consolidate genomic data

Data sources in proprietary formats

Unable to analyze all genomic data

Scientists manually research genes

Scientists’ observations are not shared

Ipedo XIP Observations Annotations

Deliver one-stop shop for research data

Capture scientists’ annotations

Keep scientists’ information synchronized

Views Integration

Query Analysis

Pipelining Presentation

“ 10x researcher productivity gain.”

Internal Data Warehouse GenBank BLAST MedLine Internal Data Warehouse GenBank BLAST MedLine Scientists Scientists Research Application Web Site Web-Based Queries Subscription Web Site ROI in Time to Market Research Portal

British Telecom Price Book October 2003

Vertical Market Unique Needs

Financial Services Has Unique Needs

360 degree view of the customer

See across systems in subsidiaries/acquisitions

Integrate data from different databases

Combine live and historical data

Satisfy regulatory requirements

Cross-divisional reporting

Granular security / data ownership

Insurance Has Unique Needs

Integrate underwriting, policy management, billing, claims systems

Perform ad-hoc reports

Leverage industry standard schemas

Produce reports for regulators

Post-merger/acquisition consolidation

Health Care Has Unique Needs

HIPAA compliance

Protect patient privacy - granular security model

Mix historical and real-time information

Customize reports for different uses: administration, insurance, treatment

360 degree view of the patient

Retail Has Unique Needs

Generate real-time sales information

Track inventory across multiple locations

Share information with supply chain

Spot trends as they occur

Post-merger/acquisition consolidation

Business Intelligence

Business Intelligence and Reporting

Use Business Objects, Cognos, MicroStrategy, or any BI tools to analyze data from:

Relational databases

Unstructured data sources

Enterprise applications

Web Services

Combine historical information with real-time information from message queues or Web Services

Ipedo Supports BOBJ Three Ways Ipedo XIP BusinessObjects Universe Business Objects BI Platform Data Sources Data Mart

Ipedo Bridges Data Partitions

Application

Ad-hoc reporting across separate databases using BI tools

Challenges

Overcoming size limitation on Sybase servers

Shielding reports from changes in underlying database partitions

Combining data from disparate sources into a single “Universe”

Maintaining high-performance for complex queries

Solution

Use Ipedo to create virtual union views

BI tools connect to Ipedo views

Ipedo optimizes and federates queries to all databases

Business Intelligence / Enterprise Reporting Tools Ipedo XIP All Customers View All Orders View All Sellers View ... Sybase1 Sybase2 Sybase10

Ipedo Appears as a Database to Business Intelligence Applications In this example, Business Objects Web Intelligence sees Ipedo as a single relational database. Ipedo is really pulling data from three different relational sources.

Ipedo Complements BOBJ

Data Integration

Real-time, query-based access to data sources

Combining web services with other data sources

Query optimization

Validation of data in real-time

Web Services

Blending multiple web services

Conditional invocation of web services in a specific order

Business rules checking on web services data

Caching of web services data for reuse

Master Data Management

Master Data Management

Create a master index that leverages Ipedo’s high performance engine

Cost-based query optimization

Union join pushdown

Reduce data replication

Customize departmental subsets of customer data to avoid data politics and ownership issues

Perform data cleansing (through Ipedo partnership)

Help improve the performance of the denormalization process and boost the flexibility in handling underlying schema changes

Ipedo XIP for MDM BI/Reporting Client ODBC / JDBC SQL SQL SQL Data Sources Remote Tables Relational View Join data from multiple RDBMS using Master Customer Key in MDM Ipedo Customer View Join View Union View Master Customer Key SQL SQL CRM Data (DB2) Trading Data (Sybase) Master Data (DB2)

Data Quality

Data Quality Enhances EII Data Source Mappings Virtual View Mappings User Query on View EII Step: Join DQ Call: Matching Join Key EII Step: Filter DQ Call: Matching Other Columns Data Sources Live Views (seconds of latency) User Query on View EII Step: Filter, Other DQ Call: Cleanse or Match Data Cached Views (minutes to hours of latency)

Caching Options:

Scheduled Cache (Push) – Include cleanse/match option for admin

User-driven Cache (Pull) – Include cleanse/match option after initial view

Aligning data between different sources for efficient join operations

Reconciling differences between data from different sources

Cleaning up cached information for subsequent reuse

Risk Management

Risk Management

Present complete set of information for analysis

Real-time feeds from message queues (trading data, interest rates, etc.)

Analyze complex XML documents (such as FpML or other trading schemas) and combine seamlessly with relational data

Cache XML for subsequent query, analysis, and integration

Compound Aggregate Indexes (CAI) track key data fields and automatically compute aggregates across different data feeds

Maintain audit trail for compliance and SOX

Ipedo for Risk Analysis Financial View Ipedo XIP JDBC ODBC SQL Convert to Relational Tables Calculate Aggregates Message Queue Message Queue XBRL FpML XML Caching BI/Reporting Client Trading Data In XML Format Order View Risk View

Enterprise Readiness

Ipedo Delivers Enterprise Readiness

Performance

Cost-based Query Optimization

Result-set Streaming

Policy-based Caching

Advanced Tuning

Scalability

Clustering

Concurrency

Security

Data Governance

Ipedo Joins Huge Data Sets… SELECT CUSTOMER.C_NAME, CUSTOMER.C_CUSTKEY, CUSTOMER.C_ADDRESS, COUNT(ORDERS.O_ORDERKEY), SUM(ORDERS.O_TOTALPRICE ) FROM CUSTOMER, ORDERS WHERE (CUSTOMER.C_CUSTKEY=ORDERS.O_CUSTKEY) AND CUSTOMER.C_NAME = 'Customer#000000250' GROUP BY CUSTOMER.C_NAME, CUSTOMER.C_CUSTKEY, CUSTOMER.C_ADDRESS

Select all ORDERS of a given CUSTOMER

ORDERS and TIME_DIM tables from Oracle (Orders Management App) 750,000 rows

CUSTOMER and NATION tables from SQL Server (CRM) 10 0 , 0 0 0 r o w s

…And Delivers Sub-Second Performance

Query Optimization:

Search predicate is pushed down to remote RDBMS.

Join predicate will be pushed into remote inner table if nested loop join is cost-effective.

Number of rows of the where clause is taken into consideration in Cost-Based Optimization (CBO).

Performance:

average query time = 0.1 sec

Query Plan:

Ipedo Security Protects Data and Views

User and Group Level Hierarchical Access Control List

Integration with external LDAP directories

Document/Attribute-level filtering (node-level)

JAAS-compliant encryption algorithm flexibility for 2-way secure authentication (from client, as well as server)

Low Overhead in IT Environment

Connection Pooling allows you to protect back-end data sources from excessive load

Intuitive setup and administration means no DBA required to run Ipedo

Runs on Windows, Linux, Solaris, HP-UX, AIX - any environment with a JVM

Runs on Tomcat (included), WebLogic, WebSphere, JBoss, JRocket

Professional Services Simplifies Implementation

Experienced Team

SQL, XML, Java, Web Services

Flexible Engagement Model

Off-site development, non-invasive setup (1-5 days)

On-site training for your team

Sample Deployments

Portal for global computer hardware company

Trading application for commodity exchange

Claims management system for insurance company

Risk management system for power utility

Ipedo Performance Testing

Ipedo XIP SQL Performance Testing

ORDERS and TIME_DIM tables from Oracle (Orders Management App)

CUSTOMER and NATION tables from SQL Server (CRM)

100,000 rows 750,000 rows

Testing conducted in July 2005 on dual-CPU Intel XEON 2.2GHz with 2.0Gb memory

SQL Query #1 SELECT CUSTOMER.C_NAME, CUSTOMER.C_CUSTKEY, CUSTOMER.C_ADDRESS, COUNT(ORDERS.O_ORDERKEY), SUM(ORDERS.O_TOTALPRICE ) FROM CUSTOMER, ORDERS WHERE (CUSTOMER.C_CUSTKEY=ORDERS.O_CUSTKEY) AND CUSTOMER.C_NAME = 'Customer#000000250' GROUP BY CUSTOMER.C_NAME, CUSTOMER.C_CUSTKEY, CUSTOMER.C_ADDRESS

Select all ORDERS of a given CUSTOMER

Query Plan #1 and Performance

Query Optimization:

Search predicate is pushed down to remote RDBMS.

Join predicate will be pushed into remote inner table if nested loop join is cost-effective.

Number of rows of the where clause is taken into consideration in Cost-Based Optimization (CBO).

Performance:

Query Plan:

SQL Query #2 SELECT TIME_DIM.YEAR, COUNT(ORDERS.O_ORDERKEY), SUM(ORDERS.O_TOTALPRICE ) FROM TIME_DIM, ORDERS, CUSTOMER WHERE (ORDERS.O_ORDERDATE=TIME_DIM.NATIVEDATE) AND CUSTOMER.C_CUSTKEY=ORDERS.O_CUSTKEY) AND ( ( CUSTOMER.C_CUSTKEY ) = CASE WHEN char(hour(current_timestamp)) = '24' THEN 250 ELSE 251 END )) GROUP BY TIME_DIM.YEAR

Conditionally select a customer’s total order count and sum

Query Optimization

ANSI SQL standard case-when-else conditional expression is pushed down to remote RDBMS.

Column transitivity is supported to allow better query plan.

e.g. (t1.c1 = t2.c2 and t2.c2 = constant)  (t1.c1=constant and t2.c2 = constant)

Performance:

Query plan:

SQL Query #3 SELECT V_MERGED_CUSTOMER.C_NATIONKEY, sum(V_MERGED_ORDERS.O_TOTALPRICE) FROM V_MERGED_CUSTOMER, V_MERGED_ORDERS WHERE (V_MERGED_CUSTOMER.C_CUSTKEY=V_MERGED_ORDERS.O_CUSTKEY) AND (V_MERGED_CUSTOMER.C_MKTSEGMENT = 'MACHINERY ‘) GROUP BY V_MERGED_CUSTOMER.C_NATIONKEY

Query efficiently across data partitions in Oracle and SQLServer:

View v_merged_customer unions customer tables in both partitions.

View v_merged_order unions order tables in both partitions.

Query Optimization

Search predicates on views are pushed down to data sources

Joins across union partitions are regrouped and pushed down to data sources, so that smaller joined result sets are combined in XIP

Performance:

average query time = 2 sec

Query plan:

SQL Query #4

Query Optimization

Shows stability and scalability in handling large result set with high concurrency without running out of memory.

The first record is returned immediately without waiting for the entire result set to be processed.

Ipedo support cursor fetching all the way through from client to Ipedo and to remote RDBMS.

Query can be cancelled without fetching the entire result set, and resources are released immediately.

SELECT ORDERS.O_ORDERKEY, ORDERS.O_ORDERDATE, ORDERS.O_CLERK FROM ORDERS

Select large result set (750,000 rows)

Query Performance #4

Cursor is used throughout the fetch chain to achieve high responsiveness:

Cursor is used from client to Ipedo:

Client obtains first row : 0.174s

Cursor is used from Ipedo to remote RDBMS:

Database returns first row in 0.171 s.

This shows cursor is used in back end when Ipedo fetches data from remote RDBMS.

Ipedo XIP Reference Architectures

Ipedo XIP Sizing Considerations

Number of Concurrent Users

Query Load

Number of queries

Complexity of queries (number of joins)

Amount of information being returned (row count)

Additional Processing

Data transformations (relational - XML)

Rules processing (number and complexity of rules)

High-Availability Requirements

Clustering (configuration)

Replication (writing or reading transaction logs)

Basic EII Server Load Concurrent users: < 75 Queries: < 50/sec Server Configuration CPUs: 1-4 RAM: 2 G Typical Usage Enable departmental reporting across multiple sources

Basic EII with Failover Ipedo XIP Primary Application-aware session pooling Ipedo XIP Secondary Server Load Concurrent users: < 75 Queries: < 50/sec Server Configuration CPUs: 1-4 RAM: 2 G Typical Usage Enable reporting across multiple sources for a business unit Ipedo can also use a clustering server for failover

HA Clustered - “Shared-Disk” Architecture Shared Disk Ipedo XIP Primary Ipedo XIP Secondary LAN Hub Private Network Windows Advanced Server Clustering Server Load Concurrent users: < 75/node Queries: < 50/sec/node Server Configuration CPUs: 1-4 per node RAM: 2 G per node Typical Usage Enable reporting across multiple sources for an enterprise or build an SOA data services layer Ipedo also supports clustering software on Linux, Solaris, HP-UX (HA = High Availability)

HA Clustered - “Shared-Nothing” Architecture SCSI Disk Ipedo XIP Primary Ipedo XIP Secondary LAN Hub Private Network Windows Advanced Server Clustering SCSI Disk Server Load Concurrent users: < 75/node Queries: < 50/sec/node Server Configuration CPUs: 1-4 per node RAM: 2 G per node Typical Usage Enable reporting across multiple sources for an enterprise or region, or build an SOA data layer Ipedo also supports clustering software on Linux, Solaris, HP-UX (HA = High Availability)

EII Server Farm (Clustered for Load Balancing) Ipedo XIP Ipedo XIP LAN Load Balancer Ipedo XIP Server Load Concurrent users: < 75/node Queries: < 50/sec/node Server Configuration CPUs: 1-4 per node RAM: 2 G per node Typical Usage Enable reporting across multiple sources for an enterprise or build an SOA data services layer