enterprise data

This enterprise data model is known under a multitude of names, the most common seems to be Common Information Model (CIM).

Careful consideration must be given to what is exposed and with which binding when it comes to enterprise data.

Similarly, mashups used in an enterprise are often associated with data aggregation of both enterprise data and data from public sources.

Many people that work with database developers just end up calling it a logical data model or and enterprise data dictionary.

Skill transfer is not the only reason enterprise data makes sense as a target market.

This Appliance arrives tuned and configured out of the box, providing extreme analytics for overburdened enterprise data warehouses.

We ll do this with a particular focus on how JDBC compares with Java object serialization as an enterprise data management strategy.

When building a maturity model for your data, it makes sense to target a subset of your enterprise data first.

Existing host-based applications and enterprise data are accessible from the ESB through a set of access services.

Entity beans are a bit less mature than JDBC, but still offer a trusted solution for enterprise data persistence.

These applications might be running in the cloud, and they might be running in a more traditional enterprise data center.

As a single entry point to all enterprise data stores, the implementation of a data service layer has many benefits.

In the end, physical data models are mapped to an Enterprise Data Model -- a common logical data model shared by an enterprise.

Manufacturing companies and stagnated enterprise data management deals can be a place to apply these technologies.

This enables Mobile Users to have access to latest enterprise data and applications at all times.

If not architected correctly from the very beginning, enterprise data access can become a major problem down the road.

This approach usually incorporates one of the most important assets of the modern enterprise - enterprise data as an afterthought.

In particular, it brings Enterprise Data Management at the core of your Service Oriented Architecture.

It uses enterprise data service to retrieve individual parts of the organization's address, and creates an OrgAddress from them.

Enterprise data as an enabler; opening up enterprise data sources is a long and complicated process, involving many stakeholders.

This is because it is not always feasible to consolidate enterprise data into a data mart or warehouse.

Together, Netezza and IBM will continue to turbocharge enterprise data, helping deliver actionable insights to the entire organization.

Service module support is a primary requirement for positioning of the VSS in enterprise campus and enterprise data center market.

Users will be able to access their enterprise data by tunneling into the app.

In the process, semantics help describe the underlying business meaning of the enterprise data in a unified manner.

The stability and predictability of stateless session beans make them an excellent candidate for managing persistent enterprise data.

In a lot of ways this design pattern is similar to the enterprise data access as a service pattern, described above.

Paired with IBM software, Guardium database security offerings help customers better manage and protect their enterprise data.

Instead, he suggests splitting enterprise data into several logical domains and defining dictionaries for these domains.

PwC lists a number of Semantic technology vendors that focus on enterprise data integration techniques.

This can significantly simplify consumption of the enterprise data by business services.

How does the enterprise data architecture help in analytical processing of data to deliver information to business users on demand?

Due to the amount of accesses (synchronous) performance becomes one the most important characteristics of the enterprise data bus.

Given what Google has done with search, what could it do for enterprise data collection, database design, analytics and data storage?

In this pattern enterprise data access is incorporated in the business service implementation.

In addition, the rich presentation and document control capabilities delivered by PDF put a user-friendly face on enterprise data.

The data gurus are struggling with reconciling [Enterprise] Data Management with the loose coupling of SOA.

The future enterprise data center main concerns virtualization technology, green energy-saving technologies and automation technology.

It's almost a gratuitous exercise to catalog the slings and arrows a data architect is likely to face advocating enterprise data models.

Today's enterprise data is typically scattered between multiple siloed applications.

Access to enterprise data sources through automated role-based entitlement systems.

It provides a programmatic way of accessing data stores and maintaining enterprise data using the Java platform.

Integration services that give access to enterprise data, applications, newsfeeds and Web services are also provided.

In particular, it examined access to enterprise data, privacy and security concerns, and some lightweight development tools.

Enterprise data is stored in multiple systems and require multiple interfaces or mechanisms to interact with them.

Reuse: Data services serve as reusable building blocks of operations on enterprise data.

This SA shows how you can mix enterprise data and user-defined data to increase employee collaboration.

With this activity, you can front end enterprise data in your Domino applications without having to copy the data over.

The IBM Redbook explores automated generation of JSP pages which can access enterprise data transactionally.

Do the organization's boundary services follow industry standards like ACCORD, HiPAA for enterprise data sharing and accessing?

UltraLite also provides synchronization with enterprise data stores.

Only the keys from your enterprise data are stored in Notes documents.

As a result, aligning of the enterprise data with enterprise services becomes a daunting task.

Using the built-in feed generators, you can access active enterprise data and combine this with archive data when the need arises.

So the remaining 80% of enterprise data to more effective management of enterprise has become an another problem.

It is the central point of data integration for business management, delivering a common view of enterprise data.

In particular, you examine access to enterprise data, the life cycle of SAs, and their influence on traditional application development.

the other is enterprise data, such as enterprise organizational structure, position setting, wage grade and management system, etc.

Ownership of enterprise data by services.

Enterprise data sources are created over stretches of time and may contain fields that are obscure and not used.

They all know that utility computing will power both the Web-based apps of cloud computing and tomorrow's adaptive enterprise data center.

And, we build enterprise data warehouses and transactional master data hubs to store the end results.

These scenarios are quite plausible, and it is reasonable to expect enterprise data systems to cope with them.

You'll also learn why and how to develop multiple file schemas, and about using external schemas to design your enterprise data.

For example, on distributed platforms, map-reduce coupled with in-memory databases; on System Z, co-deploy for access to enterprise data.

The arrival of DB2 9 breathes new life into the 90% of enterprise data that is unstructured and not storable in an RDB.

These are called conforming dimensions, and they are an early step to building an enterprise data warehouse.

This can lead to misinterpretation and misrepresentation of the data contained in the enterprise data source.

Cloud Computing; Virtualization; Intrusion Tolerance; Network Security; Virtual Adversary Structure; New Enterprise Data Center;

Physical infrastructure; Data Center; Enterprise Data Center; Centralized storage; Business Migration;

Automatic detection of online duplication documents and its application in enterprise data warehouse

Entity beans offer five essential advantages as an enterprise data persistence mechanism

Enterprise Data warehousing with Teradata - Get a Single, Integrated View of your Business

On the System Structure and Construction of Enterprise Data Warehouse

Oracle Data Guard ensures high availability, data protection, and disaster recovery for enterprise data. Oracle Data Guard

Building concept of safety system of enterprise data

Financial - Current cloud computing offerings are not cost effective compared to large enterprise data centers

Create enterprise data standards, verify compliance, and generate compliant models

Research and Implementation of Constructing Tool for Enterprise Data Warehouse

On Relations of Enterprise Data, Information, Knowledge and Intelligence

Aligning enterprise data support with the business services

Design and Implementation of Enterprise Data Integration Platform

The Application of Database Replication Technique in Enterprise Data Sharing

A Web Service- Based Integrated- Type Enterprise Data Warehouse Architecture

Serialization is a bad choice for storing enterprise data because

Integrated Modeling: An Approach to Enterprise Data Integration

Commercial Enterprise Data Management System

Research of Enterprise Data Warehouse in Mobile Communications

Building-up of an enterprise data model supported by ontology

Service oriented semantic navigation of enterprise data

Establishment and policy for electric power enterprise data center

Design Scheme of Enterprise Data Replication Platform

Initial Analysis on Enterprise Data Storage Solutions

Research on the Enterprise Data Integration Technology of Based-EAI

Enterprise data access as a business service

Input data from either Microsoft Office Excel or enterprise data sources

On Constitution of Enterprise Data Information System

Theory and practice in enterprise data warehousing

Building the online-search framework of enterprise data on ERP

Custom reports and mash-ups using enterprise data from any source

Research on the Model of Enterprise Data Integration

ODS-based Integration of E-commerce Data and Enterprise Data

Framework of multi-layer enterprise data warehouse based on manufacturing industry ERP

The Architecture of Knowledge Management Based on Enterprise Data Resources

Enterprise Data Integration and Interoperability Based on Grid

Integration and interchange of enterprise data based on unified data model

The Study of Enterprise Data Grid Platform

Study on enterprise data mining solution based on extension set

Semantic Search of Enterprise Data Based on Semantic Model

Enterprise Data-report System Based on Web Services

Design of enterprise data warehouse based on ERP

Design and Application of Enterprise Data Warehouse Based on SQL Server 2000