DOCSLIB.ORG

Administering Data Integration for Oracle Enterprise Performance Management Cloud

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Administering Data Integration for Oracle Enterprise Performance Management Cloud Oracle® Cloud Administering Data Integration for Oracle Enterprise Performance Management Cloud E98147-34 Oracle Cloud Administering Data Integration for Oracle Enterprise Performance Management Cloud, E98147-34 Copyright © 2017, 2021, Oracle and/or its affiliates. Primary Author: EPM Information Development Team This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish, or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this is software or related documentation that is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, then the following notice is applicable: U.S. GOVERNMENT END USERS: Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs) and Oracle computer documentation or other Oracle data delivered to or accessed by U.S. Government end users are "commercial computer software" or "commercial computer software documentation" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, reproduction, duplication, release, display, disclosure, modification, preparation of derivative works, and/or adaptation of i) Oracle programs (including any operating system, integrated software, any programs embedded, installed or activated on delivered hardware, and modifications of such programs), ii) Oracle computer documentation and/or iii) other Oracle data, is subject to the rights and limitations specified in the license contained in the applicable contract. The terms governing the U.S. Government’s use of Oracle cloud services are defined by the applicable contract for such services. No other rights are granted to the U.S. Government. This software or hardware is developed for general use in a variety of information management applications. It is not developed or intended for use in any inherently dangerous applications, including applications that may create a risk of personal injury. If you use this software or hardware in dangerous applications, then you shall be responsible to take all appropriate fail-safe, backup, redundancy, and other measures to ensure its safe use. Oracle Corporation and its affiliates disclaim any liability for any damages caused by use of this software or hardware in dangerous applications. Oracle, Java, and MySQL are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Intel and Intel Inside are trademarks or registered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Epyc, and the AMD logo are trademarks or registered trademarks of Advanced Micro Devices. UNIX is a registered trademark of The Open Group. This software or hardware and documentation may provide access to or information about content, products, and services from third parties. Oracle Corporation and its affiliates are not responsible for and expressly disclaim all warranties of any kind with respect to third-party content, products, and services unless otherwise set forth in an applicable agreement between you and Oracle. Oracle Corporation and its affiliates will not be responsible for any loss, costs, or damages incurred due to your access to or use of third-party content, products, or services, except as set forth in an applicable agreement between you and Oracle. Contents Documentation Accessibility Documentation Feedback 1 Data Integration Prerequisites 1-1 2 Launching Data Integration Viewing the Data Integration Home Page 2-1 Working with the Data Integration Workflow 2-3 3 Registering Applications Launching the Applications Options 3-1 Navigating Applications 3-2 Registering EPM Local Applications 3-5 Registering EPM Cloud Applications 3-6 Using Data Source Based Applications 3-7 Registering an Oracle ERP Cloud Application 3-7 Registering Oracle NetSuite Applications 3-8 Defining Incremental File Applications 3-11 Defining an On-Premise Data Source Application 3-12 Registering Oracle ERP Cloud Applications 3-14 Defining Oracle ERP Cloud Custom Applications 3-15 Registering Oracle HCM Cloud Applications 3-17 Registering Peoplesoft GL Balance Applications 3-20 Registering E-Business Suite Applications 3-21 Connecting to E-Business Suite and Peoplesoft Data Sources 3-22 Registering an On-Premises File 3-24 Registering an Application for a Class of Dimensions or Dimension Type 3-26 iii Registering a Data Export File Application 3-27 Defining Application Dimension Details 3-28 Adding Lookup Dimensions 3-30 Defining Application Detail Options 3-31 Defining Application Details for Oracle ERP Cloud Data Sources 3-38 Editing Options 3-38 Setting Default Options 3-41 Loading Data to a Free Form Application 3-42 4 Configuring Source Connections Configuring an Oracle ERP Cloud Connection 4-1 Configuring an Oracle HCM Cloud Connection 4-2 Configuring a NetSuite Connection 4-4 5 Managing Period Mappings Global Mappings 5-3 Application Mappings 5-4 Source Mappings 5-6 Source Mappings for a File Source Type 5-6 Source Mappings for a Data Source Type 5-7 Source Mappings for an EPM Cloud Source Type 5-8 Source Mappings for an Oracle ERP Cloud Source Type 5-10 Source Mappings for an Oracle HCM Cloud Source Type 5-11 Source Mappings for an Oracle NetSuite Source Type 5-12 Deleting Period Mappings 5-13 Selecting A Default POV Period 5-15 6 Defining a Data Integration Creating File-Based Integrations 6-2 Selecting Location Attributes 6-3 Mapping Files 6-4 Previewing File Options 6-6 Mapping File Columns 6-6 Creating Direct Integrations 6-7 Mapping Dimensions 6-8 Creating the Dimension Maps 6-8 Using Target Expressions 6-12 Using Source Expressions 6-18 Adding Lookup Dimensions 6-20 iv Mapping Members 6-21 Adding Member Mappings 6-21 Using Explicit Mappings 6-24 Using Between Mappings 6-25 Using In Mappings 6-26 Using Like Mappings 6-27 Using Special Characters in the Source Value Expression for Like Mappings 6-28 Using Special Characters in the Target Value Expression 6-30 Using Multi-Dimensional Mappings 6-31 Using Special Characters in Multi-Dimensional Mapping 6-32 Format Mask Mapping for Target Values 6-33 #FORMAT Mapping Type Components 6-33 #FORMAT Mapping Example 6-34 Replacing Segments 6-35 Replacing Segments with String Operations 6-35 Replace Segments with String Operations and Using a Prefix or Suffix 6-35 Selecting Members 6-36 Filtering Members 6-37 Import Member Mappings 6-37 Importing Excel Mappings 6-38 Downloading an Excel Template (Mapping Template) 6-40 Restoring Member Mappings 6-41 Exporting Member Mappings 6-42 Exporting the Current Dimension or All Dimensions Map 6-42 Exporting the Map to Excel 6-43 Setting Data Integration Options 6-44 Defining File-Based Options 6-44 Defining Direct Integration Options 6-46 Defining Target Options 6-48 Creating a Custom Target Application 6-54 Defining Target Options for Budgetary Control Applications 6-59 Defining Application Options for Financial Consolidation and Close 6-59 Defining Filters 6-66 Defining Planning Filters 6-66 Defining Oracle NetSuite Filters 6-67 Defining Oracle General Ledger Filters 6-68 Defining Budgetary Control Filters 6-69 Defining Oracle HCM Cloud Filters 6-69 Defining A Clear Region 6-71 Using Business Rules 6-73 Registering a Business Rule in Independent Mode 6-73 v Business Rules Supported Events 6-75 Adding Run Time Prompts to Business Rules 6-75 Registering a Business Rule in Embedded Mode 6-77 Adding a Custom View to the Drill Through Landing Page 6-78 7 Running an Integration Viewing the Integration Job 7-4 8 Reviewing a Data Integration Status and Viewing Results Viewing Process Details 8-1 Viewing an Error Messages Output File 8-2 Using the Workbench 8-4 Using the Workbench Workflow 8-5 Importing Source Data 8-6 Validating Source Data 8-7 Exporting Data to Target Applications 8-7 Checking the Data 8-8 Filtering Workbench Data 8-8 Using Drilling Through to Source 8-9 Defining a Custom View in the Workbench 8-11 Validating Source Data 8-13 Validation Mapping Errors 8-13 Fixing Mapping Errors 8-14 9 Using Drilling Through to Source Adding a Custom View to the Drill Through Landing Page 9-2 10 Integrating Data Integrating EPM Planning Projects and Oracle Fusion Cloud Project Management (Project Management) 10-1 About Integrating EPM Planning Projects and Project Management 10-1 Process Description for Integrating EPM Planning Projects and Project Management 10-2 Security Role Requirements for EPM Cloud to Project Management Integrations 10-4 BI Publisher Security 10-4 Projects and Budgets File-Based Data Import Process Security 10-4 Registering the Project Management Source 10-5 Defining the Integration 10-6 Registering the EPM Planning Projects Application 10-7 vi Classifying Project Dimensions in the EPM Planning Projects Application 10-8 Register the Reporting Type Application
Load more

Recommended publications
  • Scientific Data Mining in Astronomy
    SCIENTIFIC DATA MINING IN ASTRONOMY Kirk D. Borne Department of Computational and Data Sciences, George Mason University, Fairfax, VA 22030, USA [email protected] Abstract We describe the application of data mining algorithms to research prob- lems in astronomy. We posit that data mining has always been fundamen- tal to astronomical research, since data mining is the basis of evidence- based discovery, including classification, clustering, and novelty discov- ery. These algorithms represent a major set of computational tools for discovery in large databases, which will be increasingly essential in the era of data-intensive astronomy. Historical examples of data mining in astronomy are reviewed, followed by a discussion of one of the largest data-producing projects anticipated for the coming decade: the Large Synoptic Survey Telescope (LSST). To facilitate data-driven discoveries in astronomy, we envision a new data-oriented research paradigm for astron- omy and astrophysics – astroinformatics. Astroinformatics is described as both a research approach and an educational imperative for modern data-intensive astronomy. An important application area for large time- domain sky surveys (such as LSST) is the rapid identification, charac- terization, and classification of real-time sky events (including moving objects, photometrically variable objects, and the appearance of tran- sients). We describe one possible implementation of a classification broker for such events, which incorporates several astroinformatics techniques: user annotation, semantic tagging, metadata markup, heterogeneous data integration, and distributed data mining. Examples of these types of collaborative classification and discovery approaches within other science disciplines are presented. arXiv:0911.0505v1 [astro-ph.IM] 3 Nov 2009 1 Introduction It has been said that astronomers have been doing data mining for centuries: “the data are mine, and you cannot have them!”.
    [Show full text]
  • Data Quality Through Data Integration: How Integrating Your IDEA Data Will Help Improve Data Quality
    center for the integration of IDEA Data Data Quality Through Data Integration: How Integrating Your IDEA Data Will Help Improve Data Quality July 2018 Authors: Sara Sinani & Fred Edora High-quality data is essential when looking at student-level data, including data specifically focused on students with disabilities. For state education agencies (SEAs), it is critical to have a solid foundation for how data is collected and stored to achieve high-quality data. The process of integrating the Individuals with Disabilities Education Act (IDEA) data into a statewide longitudinal data system (SLDS) or other general education data system not only provides SEAs with more complete data, but also helps SEAs improve accuracy of federal reporting, increase the quality of and access to data within and across data systems, and make better informed policy decisions related to students with disabilities. Through the data integration process, including mapping data elements, reviewing data governance processes, and documenting business rules, SEAs will have developed documented processes and policies that result in more integral data that can be used with more confidence. In this brief, the Center for the Integration of IDEA Data (CIID) provides scenarios based on the continuum of data integration by focusing on three specific scenarios along the integration continuum to illustrate how a robust integrated data system improves the quality of data. Scenario A: Siloed Data Systems Data systems where the data is housed in separate systems and State Education Agency databases are often referred to as “data silos.”1 These data silos are isolated from other data systems and databases.
    [Show full text]
  • Managing Data in Motion This Page Intentionally Left Blank Managing Data in Motion Data Integration Best Practice Techniques and Technologies
    Managing Data in Motion This page intentionally left blank Managing Data in Motion Data Integration Best Practice Techniques and Technologies April Reeve AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann is an imprint of Elsevier Acquiring Editor: Andrea Dierna Development Editor: Heather Scherer Project Manager: Mohanambal Natarajan Designer: Russell Purdy Morgan Kaufmann is an imprint of Elsevier 225 Wyman Street, Waltham, MA 02451, USA Copyright r 2013 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods or professional practices, may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information or methods described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.
    [Show full text]
  • Informatica Cloud Data Integration
    Informatica® Cloud Data Integration Microsoft SQL Server Connector Guide Informatica Cloud Data Integration Microsoft SQL Server Connector Guide March 2019 © Copyright Informatica LLC 2017, 2019 This software and documentation are provided only under a separate license agreement containing restrictions on use and disclosure. No part of this document may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording or otherwise) without prior consent of Informatica LLC. U.S. GOVERNMENT RIGHTS Programs, software, databases, and related documentation and technical data delivered to U.S. Government customers are "commercial computer software" or "commercial technical data" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, duplication, disclosure, modification, and adaptation is subject to the restrictions and license terms set forth in the applicable Government contract, and, to the extent applicable by the terms of the Government contract, the additional rights set forth in FAR 52.227-19, Commercial Computer Software License. Informatica, the Informatica logo, Informatica Cloud, and PowerCenter are trademarks or registered trademarks of Informatica LLC in the United States and many jurisdictions throughout the world. A current list of Informatica trademarks is available on the web at https://www.informatica.com/trademarks.html. Other company and product names may be trade names or trademarks of their respective owners. Portions of this software and/or documentation are subject to copyright held by third parties. Required third party notices are included with the product. See patents at https://www.informatica.com/legal/patents.html. DISCLAIMER: Informatica LLC provides this documentation "as is" without warranty of any kind, either express or implied, including, but not limited to, the implied warranties of noninfringement, merchantability, or use for a particular purpose.
    [Show full text]
  • What to Look for When Selecting a Master Data Management Solution What to Look for When Selecting a Master Data Management Solution
    What to Look for When Selecting a Master Data Management Solution What to Look for When Selecting a Master Data Management Solution Table of Contents Business Drivers of MDM .......................................................................................................... 3 Next-Generation MDM .............................................................................................................. 5 Keys to a Successful MDM Deployment .............................................................................. 6 Choosing the Right Solution for MDM ................................................................................ 7 Talend’s MDM Solution .............................................................................................................. 7 Conclusion ..................................................................................................................................... 8 Master data management (MDM) exists to enable the business success of an orga- nization. With the right MDM solution, organizations can successfully manage and leverage their most valuable shared information assets. Because of the strategic importance of MDM, organizations can’t afford to make any mistakes when choosing their MDM solution. This article will discuss what to look for in an MDM solution and why the right choice will help to drive strategic business initiatives, from cloud computing to big data to next-generation business agility. 2 It is critical for CIOs and business decision-makers to understand the value
    [Show full text]
  • Using ETL, EAI, and EII Tools to Create an Integrated Enterprise
    Data Integration: Using ETL, EAI, and EII Tools to Create an Integrated Enterprise Colin White Founder, BI Research TDWI Webcast October 2005 TDWI Data Integration Study Copyright © BI Research 2005 2 Data Integration: Barrier to Application Development Copyright © BI Research 2005 3 Top Three Data Integration Inhibitors Copyright © BI Research 2005 4 Staffing and Budget for Data Integration Copyright © BI Research 2005 5 Data Integration: A Definition A framework of applications, products, techniques and technologies for providing a unified and consistent view of enterprise-wide business data Copyright © BI Research 2005 6 Enterprise Business Data Copyright © BI Research 2005 7 Data Integration Architecture Source Target Data integration Master data applications Business domain dispersed management (MDM) MDM applications integrated internal data & external Data integration techniques data Data Data Data propagation consolidation federation Changed data Data transformation (restructure, capture (CDC) cleanse, reconcile, aggregate) Data integration technologies Enterprise data Extract transformation Enterprise content replication (EDR) load (ETL) management (ECM) Enterprise application Right-time ETL Enterprise information integration (EAI) (RT-ETL) integration (EII) Web services (services-oriented architecture, SOA) Data integration management Data quality Metadata Systems management management management Copyright © BI Research 2005 8 Data Integration Techniques and Technologies Data Consolidation centralized data Extract, transformation
    [Show full text]
  • Introduction to Data Integration Driven by a Common Data Model
    Introduction to Data Integration Driven by a Common Data Model Michal Džmuráň Senior Business Consultant Progress Software Czech Republic Index Data Integration Driven by a Common Data Model 3 Data Integration Engine 3 What Is Semantic Integration? 3 What Is Common Data Model? Common Data Model Examples 4 What Is Data Integration Driven by a Common Data Model? 5 The Position of Integration Driven by a Common Data Model in the Overall Application Integration Architecture 6 Which Organisations Would Benefit from Data Integration Driven by a Common Data Model? 8 Key Elements of Data Integration Driven by a Common Data Model 9 Common Data Model, Data Services and Data Sources 9 Mapping and Computed Attributes 10 Rules 11 Glossary of Terms 13 Information Resources 15 Web 15 Articles and Analytical Reports 15 Literature 15 Industry Standards for Common Data Models 15 Contact 16 References 16 Data Integration Driven by a Common Data Model Data Integration Engine Today, not even small organisations can make do with a single application. The majority of business processes in an organisation is nowadays already supported by some kind of implemented application, and the organisation must focus on making its operations more efficient. One of the ways of achieving this goal is the information exchange optimization across applications, assisted by the data integration from various applications; it is summarily known as Enterprise Application Integration (EAI). Without effective Enterprise Application Integration, a modern organisation cannot run its business processes to meet the ever increasing customer demands and have an up-to-date knowledge of its operations. Data integration is an important part of EAI.
    [Show full text]
  • The Progress Datadirect for ODBC for SQL Server Wire Protocol User's Guide and Reference
    The Progress DataDirect® for ODBC for SQL Server™ Wire Protocol User©s Guide and Reference Release 8.0.2 Copyright © 2020 Progress Software Corporation and/or one of its subsidiaries or affiliates. All rights reserved. These materials and all Progress® software products are copyrighted and all rights are reserved by Progress Software Corporation. The information in these materials is subject to change without notice, and Progress Software Corporation assumes no responsibility for any errors that may appear therein. The references in these materials to specific platforms supported are subject to change. Corticon, DataDirect (and design), DataDirect Cloud, DataDirect Connect, DataDirect Connect64, DataDirect XML Converters, DataDirect XQuery, DataRPM, Defrag This, Deliver More Than Expected, Icenium, Ipswitch, iMacros, Kendo UI, Kinvey, MessageWay, MOVEit, NativeChat, NativeScript, OpenEdge, Powered by Progress, Progress, Progress Software Developers Network, SequeLink, Sitefinity (and Design), Sitefinity, SpeedScript, Stylus Studio, TeamPulse, Telerik, Telerik (and Design), Test Studio, WebSpeed, WhatsConfigured, WhatsConnected, WhatsUp, and WS_FTP are registered trademarks of Progress Software Corporation or one of its affiliates or subsidiaries in the U.S. and/or other countries. Analytics360, AppServer, BusinessEdge, DataDirect Autonomous REST Connector, DataDirect Spy, SupportLink, DevCraft, Fiddler, iMail, JustAssembly, JustDecompile, JustMock, NativeScript Sidekick, OpenAccess, ProDataSet, Progress Results, Progress Software, ProVision, PSE Pro, SmartBrowser, SmartComponent, SmartDataBrowser, SmartDataObjects, SmartDataView, SmartDialog, SmartFolder, SmartFrame, SmartObjects, SmartPanel, SmartQuery, SmartViewer, SmartWindow, and WebClient are trademarks or service marks of Progress Software Corporation and/or its subsidiaries or affiliates in the U.S. and other countries. Java is a registered trademark of Oracle and/or its affiliates. Any other marks contained herein may be trademarks of their respective owners.
    [Show full text]
  • Using the Qlik Data Integration Platform for Better Analytics
    DATA SHEET DATA INTEGRATION Modern DataOps Using the Qlik Data Integration Platform for Better Analytics QLIK.COM INTRODUCTION Enabling DataOps for Analytics-Ready Data Qlik’s Data Integration Platform (formerly Attunity solutions) automates real-time data streaming, cataloging, and publishing, so you can quickly find and free analytics-ready data — and take action on it. In today’s hyper-competitive business climate, real-time insights are critical. Users need robust data integration and agile analytics solutions, to make decisions fast. Unlike traditional batch data movement and ETL scripting — which are slow, inflexible and labor intensive — Qlik’s data integration platform automates the creation of data streams from core transactional systems. It efficiently moves data to applications, warehouses, and lakes — on premise and in the cloud — and makes data immediately available via an Amazon-like catalog marketplace experience. With Qlik, users get the frictionless data agility they need to drive greater business value. Data consumers need better access to their data in real time. Many existing processess and technologies simply can’t keep up with this increasing demand, creating more complexity and tighter bottlenecks within IT. DataOps seeks to bring improvements to data integration. Borrowing methods from DevOps — which combines softward development (Dev) and IT operations (Ops) to improve the velocity, quality, predictability, and scale of software development and deployment — DataOps focuses on the practices and technologies for building and enhancing data pipelines to rapidly meet business analytics needs. Modern DataOps 2 Data Warehouse Automation Business needs are in a continual state of flux. Data sets are increasingly diverse. To meet the growing need for analytics-ready data delivery at the speed of now, Qlik’s Data Integration Platform automates the entire data warehouse lifecycle.
    [Show full text]
  • 2.4 Data Integration
    Data Mining 2.4 Data Integration Fall 2008 Instructor: Dr. Masoud Yaghini Data Integration Data Integration Data integration : – Combines data from multiple databases into a coherent store – Denormalization tables (often done to improve performance by avoiding joins) Integration of the data from multiple sources may produces redundancies and inconsistencies in the resulting data set. Tasks of data integration : – Detecting and resolving data value and schema conflicts – Handling Redundancy in Data Integration Data Integration Outline Detecting and Resolving Data Value and Schema Conflicts Handling Redundancy in Data Integration References Data Integration Detecting and Resolving Data Value and Schema Conflicts Data Integration Schema Integration Schema Integration : – Integrate metadata from different sources – The same attribute or object may have different names in different databases – e.g. customer_id in one database and cust_number in another The metadata include: – the name, meaning, data type, and range of values permitted for the attribute, and etc. Data Integration Detecting and resolving data value conflicts For the same real world entity, attribute values from different sources are different This may be due to differences in representation, scaling, or encoding. Examples: – the data codes for pay_type in one database may be “H” and “S”, and 1 and 2 in another . – a weight attribute may be stored in metric units in one system and British imperial units in another. – For a hotel chain, the price of rooms in different cities may involve not only different currencies but also different services (such as free breakfast) and taxes. Data Integration Detecting and resolving data value conflicts This step also relates to data cleaning, as described earlier.
    [Show full text]
  • DATA INTEGRATION GLOSSARY Data Integration Glossary
    glossary DATA INTEGRATION GLOSSARY Data Integration Glossary August 2001 U.S. Department of Transportation Federal Highway Administration Office of Asset Management NOTE FROM THE DIRECTOR Office of Asset Management, Infrastructure Core Business Unit, Federal Highway Administration his glossary is one of a series of documents on data integration being published by the Federal Highway Administration’s Office of Asset T Management. It defines in simple and understandable language a broad set of the terminologies used in information management, particularly in regard to database management and integration. Our objective is to provide convenient reference material that can be used by individuals involved in data integration activities. The glossary is limited to the more fundamental concepts and taxonomies applied in transportation database management and is not intended to be comprehensive. The importance of data integration in implementing Asset Management processes cannot be overstated. My office will continue to provide information and support to all transportation agencies as they work to integrate their data. Madeleine Bloom Director, Office of Asset Management DATA INTEGRATION GLOSSARY 3 LIST OF TERMS Aggregate data . .7 Dynamic segmentation . .12 Application . .7 Enterprise . .12 Application integration . .7 Enterprise application integration (EAI) . .12 Application program interface (API) . .7 Enterprise resource planning (ERP) . .12 Archive . .7 Entity . .12 Asset Management . .7 Entity relationship (ER) diagram . .12 Atomic data . .7 Executive information system (EIS) . .13 Authorization request . .7 Extensibility . .13 Bulk data transfer . .7 Geographic information system (GIS) . .13 Business process . .7 Graphical user interface (GUI) . .13 Business process reengineering (BPR) . .7 Information systems architecture . .13 Communications protocol . .7 Interoperable database . .13 Computer Aided Software Engineering (CASE) tools .
    [Show full text]
  • Oracle Essbase Spreadsheet Add-In User's Guide
    ORACLE® ESSBASE SPREADSHEET ADD-IN RELEASE 11.1.1 USER’S GUIDE Spreadsheet Add-in User’s Guide, 11.1.1 Copyright © 1991, 2008, Oracle and/or its affiliates. All rights reserved. Authors: EPM Information Development Team This software and related documentation are provided under a license agreement containing restrictions on use and disclosure and are protected by intellectual property laws. Except as expressly permitted in your license agreement or allowed by law, you may not use, copy, reproduce, translate, broadcast, modify, license, transmit, distribute, exhibit, perform, publish or display any part, in any form, or by any means. Reverse engineering, disassembly, or decompilation of this software, unless required by law for interoperability, is prohibited. The information contained herein is subject to change without notice and is not warranted to be error-free. If you find any errors, please report them to us in writing. If this software or related documentation is delivered to the U.S. Government or anyone licensing it on behalf of the U.S. Government, the following notice is applicable: U.S. GOVERNMENT RIGHTS: Programs, software, databases, and related documentation and technical data delivered to U.S. Government customers are "commercial computer software" or "commercial technical data" pursuant to the applicable Federal Acquisition Regulation and agency-specific supplemental regulations. As such, the use, duplication, disclosure, modification, and adaptation shall be subject to the restrictions and license terms set forth in the applicable Government contract, and, to the extent applicable by the terms of the Government contract, the additional rights set forth in FAR 52.227-19, Commercial Computer Software License (December 2007).
    [Show full text]