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  Encyclopedia of Keywords > Information > Olap   Michael Charnine

Keywords and Sections
OLAP SERVICES
DATA WAREHOUSE
FACT TABLE
BI
BUSINESS INTELLIGENCE
DIMENSIONS
PRODUCTS
ANALYSIS
CODD
ANALYTICAL
AGGREGATIONS
PIVOTTABLE SERVICE
OLAP CUBE
ABILITY
MULTIDIMENSIONAL
OLAP TECHNOLOGY
DATA MODEL
DATA ANALYSIS
SCHEMAS
MULTIDIMENSIONAL OLAP
ANALYZE
FORECASTING
OLAP TOOLS
CHALLENGE
Review of Short Phrases and Links

    This Review contains major "Olap"- related terms, short phrases and links grouped together in the form of Encyclopedia article.

Definitions

  1. OLAP is a technology that can be distributed to many users using a variety of platforms.
  2. OLAP is a platform upon which decisions are made.
  3. OLAP is a specific way of financial and statistical data representation for executives, specialists and analysts. (Web site)
  4. OLAP is part of the broader category business intelligence, which also encompasses relational reporting and data mining. (Web site)
  5. OLAP is part of the broader category business intelligence, which also includes Extract transform load (ETL), relational reporting and data mining.

Olap Services

  1. OLAP Services supports a full MOLAP implementation, full ROLAP implementation, or a HOLAP solution.
  2. OLAP Services does not store empty values, and as a result, even sparsely populated cubes will not balloon in size.
  3. The result is that OLAP Services derives nonaggregated data from a few existing aggregate values rather than having to scan the entire data warehouse.

Data Warehouse

  1. An OLAP database does not need to be as large as a data warehouse, since not all transactional data is needed for trend analysis.
  2. Furthermore, OLAP Services is optimized for development of data warehouses where star or snowflake schemas have been designed.
  3. Normally, OLAP databases extract information from several OLTP databases called data warehouses.

Fact Table

  1. Combining data from multiple fact tables is a difficult problem in the pure relational world, but can be made easy and intuitive in certain OLAP servers. (Web site)
  2. It has been claimed that for complex queries OLAP cubes can produce an answer in around 0.1% of the time for the same query on OLTP relational data. (Web site)
  3. Distinguish between data mining and OLAP. Explain the relation between data warehousing and data mining.
  4. A numeric value stored in a fact table and in an OLAP cube.

Bi

  1. The findings of The OLAP Survey 6 are a valuable guide to the product capabilities and after-sale support you can expect from the top BI vendors.
  2. While OLAP systems have the ability to answer "who" and "what" questions, it's their ability to answer "what if" that sets them apart from other BI tools. (Web site)

Business Intelligence

  1. The authors did a really good job showing how data mining and OLAP can work together to create a BI (Business Intelligence) system. (Web site)
  2. We found one for ETL, Jetstream, one OLAP engine, Mondrian, and jPivot, which gave a front end to Mondrian.
  3. This paper explains the application of business intelligence tools like dataware housing, OLAP, and data mining in insurance.

Dimensions

  1. OLAP organizes facts into dimensions which are ways that the facts can be broken down.
  2. Logi OLAP Reporting lets users move dimensions, select different calculations, create interactive charts and graphs and easily drill down into cube data.
  3. Filtering of hierarchy members with or without applying these filters to the OLAP calculations. (Web site)
  4. The key difference between OLAP dimensions and simple relational dimensions is the central role played by hierarchies in OLAP implementations. (Web site)

Products

  1. Dimensional model is the underlying data model used by many of the commercial OLAP products available today in the market.
  2. Few OLAP products, however, have taken advantage of this trend.
  3. Many of these vendors already are delivering beta products based on the specification to OLAP Services users.
  4. The obvious benefit is that the OLAP database administrator is better able to translate skills from SQL Server and other Microsoft products.

Analysis

  1. In traditional OLAP systems, data that has not been preaggregated is not available for reporting and analysis purposes unless calculated at run time.
  2. The Analysis Services task processes OLAP cubes that are based on the data. (Web site)
  3. The term used in Microsoft's Analysis Services (OLAP Services) for a cube that is created from portions of one or more base cubes.

Codd

  1. This hindered large-scale deployment to many clients, and the use of OLAP data from other vendors. (Web site)
  2. Explain Codd’s guidelines for an OLAP system.
  3. We agree with Dr Codd, but also note that many OLAP applications are still read-only.

Analytical

  1. Warehouse management operates to manage the ETL and data quality stage, the Relational OLAP databases and Data Warehouse and the analytical applications. (Web site)
  2. The OLAP + CHART ModelKit components package is intended for the creation of analytical applications for decision support systems.
  3. OLAP common analytical operations Consolidation - involves the aggregation of data i.e.

Aggregations

  1. These precomputed values, or aggregations, are the basis of the OLAP performance gains.
  2. These logs can then be used to fine-tune the set of aggregations that OLAP Services maintains.
  3. The number of aggregations in an OLAP model is a function of the number of dimensions, the number of levels in the hierarchies, and the parent-child ratios.
  4. Around about V8.5, Holos Server implemented a hierarchical lock manager, allowing nesting of fine and coarse-grain OLAP locks, and full transaction control. (Web site)
  5. Datamation maintains a comprehensive web site with information on OLAP and data warehouses. (Web site)

Pivottable Service

  1. PivotTable Service is the facility that connects OLAP client applications to the OLAP Services server.
  2. Built on the OLE DB for OLAP interfaces, these new capabilities will support live access to OLAP Services servers, disconnected usage, and Web-based access.
  3. All access to data managed by OLAP Services, by custom programs or client tools, is through the OLE DB for OLAP interface provided by PivotTable Service.

Olap Cube

  1. An OLAP cube is a mechanism to define (and optionally store) aggregations.
  2. Included in OLAP Services is a full tutorial on OLAP concepts and a step-by-step guide to building an OLAP cube.
  3. Also called a data mart or OLAP cube.

Ability

  1. The ability to create virtual cubes means that many unnecessary values can be eliminated from the OLAP data storage altogether.
  2. An ability to edit OLAP data directly in the grid. (Web site)

Multidimensional

  1. Multidimensional Expressions is a Microsoft -owned query language for OLAP databases.
  2. As described earlier, OLAP data explosion is the result of multidimensional preaggregation.
  3. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. (Web site)
  4. Used to link OLAP clients and servers using a multidimensional language, MDX.
  5. Multidimensional OLAP ("MOLAP") utilizes a proprietary multidimensional database ("MDDB") to provide OLAP analyses.

Olap Technology

  1. Executives and business analysts can view OLAP data by various dimensions, measures, and filters.
  2. HOLAP can use varying combinations of ROLAP and OLAP technology. (Web site)
  3. In its simplest definition, OLAP is a queriable repository against which Microsoft and third-party technologies provide the ability to ask questions.
  4. An overview of data warehousing and OLAP technology. (Web site)
  5. The most common example of HOLAP architecture is OLAP services in Microsoft SQL Server 7.0. (Web site)

Data Model

  1. Missing or invalid data values create sparsity in the OLAP data model.
  2. Within each dimension of an OLAP data model, data can be organized into a hierarchy that represents levels of detail on the data.
  3. Star and snowflake schemas are relational approximations of the OLAP data model and can be an excellent starting point for building OLAP cube definitions.

Data Analysis

  1. OLAP ModelKit is designed for multidimensional data analysis. (Web site)
  2. OLAP services provide for fast analysis of multi-dimensional information.
  3. It usually includes one or more data marts or OLAP systems for end-user data analysis.

Schemas

  1. Pentaho has also updated the Pentaho Cube Designer to streamline the process of creating OLAP schemas, and to enhance support for "snowflake" schemas. (Web site)
  2. OLAP Services can easily accommodate other source schemas should they be encountered.

Multidimensional Olap

  1. SWREG reporter uses SWREG's CSV sales data reports, imports them to a relational database and then processes as a multidimensional (OLAP) cube.
  2. Dimensionalization - Organizing the data into the multidimensional (OLAP) structure of a star schema.
  3. MOLAP(Multidimensional OLAP), provides the analysis of data stored in a multi-dimensional data cube.

Analyze

  1. The process-driven approach helps Finance & IT Managers analyze ERP, OLAP & relational data systems & Web services simultaneously.
  2. Speed and response time are important attributes of OLAP services that allow users to browse and analyze data on-line in an efficient manner.

Forecasting

  1. Open Source OLAP Server for analysis, reporting, budgeting, forecasting, and planning. (Web site)
  2. Contact: Gert Jan de Nooij OLAP marketing, sales, marketing research, and sales forecasting consultants with Oracle Express products and Essbase products. (Web site)

Olap Tools

  1. Analytical applications carry out OLAP analysis using OLAP tools. (Web site)
  2. This is accomplished by connecting the data to fast and easy-to-use tools known as Online Analytical Processing (OLAP) tools.
  3. The SAP business information warehouse enables OLAP for processing of information from large amounts of operative and historical data.
  4. Data warehouses consolidate data into a central repository and give you the OLAP tools necessary to retrieve data pertinent to the solution.

Challenge

  1. First and foremost, OLAP presents data to end users through a natural, intuitive data model.
  2. Data sparsity is a challenge for OLAP vendors that has been met with varying degrees of success.
  3. A fundamental challenge in OLAP implementation is mapping the initial database schema to the multidimensional model.
  4. He could also be interpreted as saying that data changes should not be allowed in what are normally regarded as calculated cells within the OLAP database.
  5. In fact, in the interests of storing sparse data more compactly, a few OLAP tools such as TM1 do break this rule, without great loss of function.

Categories

  1. Encyclopedia of Keywords > Information
  2. Encyclopedia of Keywords > Nature > Systems
  3. Encyclopedia of Keywords > Information > Data
  4. Glossaries > Glossary of Data Management /
  5. Books about "Olap" in Amazon.com

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  Short phrases about "Olap"
  Originally created: February 19, 2008.
  Links checked: July 31, 2013.
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