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Discrete Manufacturing (ERP)
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Documents related to » olap data warehousing analysis


Thinking Radically about Data Warehousing and Big Data: Interview with Roger Gaskell, CTO of Kognitio
Managing data—particularly in large numbers—still is and probably will be the number one priority for many organizations in the upcoming years. Many of the

olap data warehousing analysis  create and populate physical OLAP cubes. The underlying data is held as low-level transactional data in standard relational structures, but to the application layer it appears as a “virtual cube.” By virtualizing the cube structures, organizations can still get a holistic view of their data but with zero latency given that large virtual cubes can be created within just seconds. Moreover, users can benefit from one single version of the truth; instead of contending with multiple physical cubes that Read More...
A Definition of Data Warehousing
There is a great deal of confusion over the meaning of data warehousing. Simply defined, a data warehouse is a place for data, whereas data warehousing

olap data warehousing analysis  of OLAP is that OLAP can only answer questions you know to ask, data mining answers questions you didn''t necessarily know to ask. Data Visualization Tools: Tools that show graphical representations of data, including complex three-dimensional data pictures. The theory is that the user can see trends more effectively in this manner than when looking at complex statistical graphs. Some vendors are making progress in this area using the Virtual Reality Modeling Language (VRML). Metadata Management: Read More...
Microsoft Goes Their Own Way with Data Warehousing Alliance 2000
Microsoft Corp. (Nasdaq: MSFT) today announced that 47 applications and tools from 39 vendors throughout the industry have qualified for Microsoft« Data

olap data warehousing analysis  Goes Their Own Way with Data Warehousing Alliance 2000 Event Summary REDMOND, Wash., Nov. 30 /PRNewswire/ -- Microsoft Corp. (Nasdaq: MSFT) today announced that 47 applications and tools from 39 top vendors throughout the industry have qualified for Microsoft Data Warehousing Alliance 2000. Alliance members and partners are committed to delivering tools and applications based on the Microsoft Data Warehousing Framework 2000, an open architecture for building business intelligence and analytical Read More...
Best Practices for a Data Warehouse on Oracle Database 11g
Companies are recognizing the value of an enterprise data warehouse (EDW) that provides a single 360-degree view of the business. But to ensure that your EDW

olap data warehousing analysis  Warehousing , Data Warehousing OLAP , Resource Data Warehouse , Land and Resource Data Warehouse , Data Warehouse Web Site , Data Mining , Data Mart , Data Warehouse Architecture , Data Warehouse Concepts , Data Warehouse Tutorial , Data Warehouse Definition , OLAP , Business Intelligence , Huge Data Warehouse , Data Warehouse Appliance . NOTE: The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. Read More...
Customer Relationship Analysis Firm Extends Reach
thinkAnalytics signs a partnering agreement with one of the largest information technology services companies in North America. Why does CGI expect

olap data warehousing analysis  by Gentia Software, an OLAP (On-Line Analytical Processing) specialist, in 1998; thinkAnalytics was recently launched as a separate company. The company''s product suite comprises a variety of analytic tools that sit on top of common middleware and analysis functions. The middleware functions do not replace traditional ETL (Extract/Transform/Load) functions but rather augments them to provide intelligent data cleanup. Data cleanup encompasses such functions as null replacement, data scaling and deduping. Read More...
Data Center Automation
With the increasing complexity of the data center and its dependent systems, data center automation (DCA) is becoming a necessity. To replace the costly and

olap data warehousing analysis  Center Automation With the increasing complexity of the data center and its dependent systems, data center automation (DCA) is becoming a necessity. To replace the costly and inefficient human aspect of managing the data center, IT departments must adopt DCA solutions. Combined with utility-based computing architectures, these solutions can provide greater dynamics in the environment and facilitate speed of response to market demands. Read More...
Securing Data in the Cloud
When considering adopting cloud computing or software-as-a-service (SaaS), a question most potential customers ask vendors is “How secure will our data be in

olap data warehousing analysis  Data in the Cloud When considering adopting cloud computing or software-as-a-service (SaaS), a question most potential customers ask vendors is “How secure will our data be in your hands?” Customers are right to ask this question and should closely examine any vendor’s security credentials as part of their cloud/SaaS evaluations. This document is intended to give a broad overview of one vendor’s security policies, processes, and practices. Read More...
Data Loss Prevention Best Practices: Managing Sensitive Data in the Enterprise
While a great deal of attention has been given to protecting companies’ electronic assets from outside threats, organizations must now turn their attention to

olap data warehousing analysis  Loss Prevention Best Practices: Managing Sensitive Data in the Enterprise While a great deal of attention has been given to protecting companies’ electronic assets from outside threats, organizations must now turn their attention to an equally dangerous situation: data loss from the inside. Given today’s strict regulatory standards, data loss prevention (DLP) has become one of the most critical issues facing executives. Fortunately, effective technical solutions are now available that can help. Read More...
Data Quality Trends and Adoption
While much of the interest in data quality (DQ) solutions had focused on avoiding failure of data management-related initiatives, organizations now look to DQ

olap data warehousing analysis  Quality Trends and Adoption While much of the interest in data quality (DQ) solutions had focused on avoiding failure of data management-related initiatives, organizations now look to DQ efforts to improve operational efficiencies, reduce wasted costs, optimize critical business processes, provide data transparency, and improve customer experiences. Read what DQ purchase and usage trends across UK and US companies reveal about DQ goals and drivers. Read More...
TCO Analysis of a Traditional Data Center vs. a Scalable, Containerized Data Center
Standardized, scalable, pre-assembled, and integrated data center facility power and cooling modules provide a total cost of ownership (TCO) savings of 30

olap data warehousing analysis  Analysis of a Traditional Data Center vs. a Scalable, Containerized Data Center Standardized, scalable, pre-assembled, and integrated data center facility power and cooling modules provide a total cost of ownership (TCO) savings of 30% compared with traditional, built-out data center power and cooling infrastructure. Avoiding overbuilt capacity and scaling the design over time contributes to a significant percentage of the overall savings. This white paper provides a quantitative TCO analysis of the Read More...
Beware of Legacy Data - It Can Be Lethal
Legacy data can be lethal to your expensive new application – two case studies and some practical recommendations.

olap data warehousing analysis  of Legacy Data - It Can Be Lethal Beware of Legacy Data It Can Be Lethal Featured Author - Jan Mulder - August 23, 2002 Introduction The term legacy is mostly used for applications. For example, according to the Foldoc dictionary, legacy is: A computer system or application program which continues to be used because of the cost of replacing or redesigning it and often despite its poor competitiveness and compatibility with modern equivalents. The implication is that the system is large, monolithic Read More...
Data Quality Basics
Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at

olap data warehousing analysis  Quality Basics Bad data threatens the usefulness of the information you have about your customers. Poor data quality undermines customer communication and whittles away at profit margins. It can also create useless information in the form of inaccurate reports and market analyses. As companies come to rely more and more on their automated systems, data quality becomes an increasingly serious business issue. Read More...
Sentiment Analysis: Movie Reviews
Sentiment analysis is the method of extracting subjective information from any written content. It is being widely used in product benchmarking, market

olap data warehousing analysis  sentiment analysis,natural language processing Read More...
Data Security Is Less Expensive than Your Next Liability Lawsuit: Best Practices in Application Data Security
Insecure data. Heavy fines due to non-compliance. Loss of customers and reputation. It adds up to a nightmare scenario that businesses want to avoid at all

olap data warehousing analysis  Security Is Less Expensive than Your Next Liability Lawsuit: Best Practices in Application Data Security Insecure data. Heavy fines due to non-compliance. Loss of customers and reputation. It adds up to a nightmare scenario that businesses want to avoid at all costs. However, this nightmare is preventable: knowledge base-driven data security solutions can be critical tools for enterprises wanting to secure not only their data—but also their status in the marketplace. Read More...

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