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Topic: Dimension (data warehouse)

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Paper: Alan Perkins - "Strategic Data Warehouse Development" Also called information warehouse architecture, metadata is integral to all levels of the information warehouse, but exists and functions in a different dimension from other warehouse data. Providing a clear and unambiguous definition of every warehouse data entity, describing the way each is used, as well as defining derivation formulas, aggregation categories and time periods, are activities critical to capturing a clear understanding of an enterprise’s measures. The principal reason for developing an information warehouse is to integrate operational data from various sources into a single and consistent architecture that supports analysis and decision-making within the enterprise. www.ies.aust.com /~visible/papers/IW.html   (5119 words)

Ralph Kimball - Real-time Data Warehouse Design Challenges I helped out on a complex set of data marts earlier this year and a step dimension may have been perfect for that model. - CRM - Database- Data Warehouse - EAI If you are a Warehouse, BI or ETL guru, you will probably be bored by the presentation. datawarehouse.ittoolbox.com /blogs/featuredentry.asp?i=6359   (791 words)

stars.doc Most data warehouses have time as a dimension because of the importance of history data and because of the difficulty of the drill down/roll up (as we saw earlier, it is not straightforward). Drilling to Another Fact Table As a matter of fact (no pun intended!), it's important that a dimension table used by one fact table can be shared by other fact tables that need them to facilitate drilling across the dimension to get access to data in the other fact table. Remember that if a majority of the fact table needs to be included, the best thing is usually a table scan, which is no longer a bad thing if you are using parallel query (which you should be if you have a data warehouse). cis.bentley.edu /mrobbert/CS753/stars.doc   (3125 words)

Patent 5978788: System and method for generating multi-representations of a data cube In step 822, using this dimension table, the data cube is generated and the accuracy of a data warehouse query is compared with a previous multi-resolution representation of the data cube. Note that the process of generating the data cube is identical to the previous case, except that the wavelet transformation process begins at one boundary and ends at another boundary. An apparatus and method for approximating the data stored in a databases by generating multiple projections and representations from the database such that the OLAP queries for the original database (such as aggregation and histogram operations) may be applied to the approximated version of the database, which can be much smaller than the original databases. www.freepatentsonline.com /5978788.html   (3125 words)

Stratature - Enterprise Dimension Management, Master Data Management & Hierarchy Management integrates dimensional and master data across BI, data warehouse, financial and operational systems, providing for accurate, consistent and compliant enterprise reporting. Stratature - Enterprise Dimension Management, Master Data Management & Hierarchy Management Expertly manage the change process and track activity throughout the versioning cycle of a dimension. www.Stratature.com   (126 words)

DBMS - June 1998 - Data Warehouse Architect When you’re finished updating your dimension tables, not only are all the dimension records correct, but your lookup tables that tie the production keys to the current values of the data warehouse keys have been updated correctly. Fact tables with four and 12 foreign keys connecting outward to a halo of dimension tables present an interesting challenge during data extraction: You must intercept all the incoming fact records and replace all their key components with the correct surrogate key values at high speed. The dimension table controls whether the surrogate key is valid because the surrogate key is the primary key in the dimension table. members.fortunecity.com /rhubarb404/orahtml/dbmsmag/9806d05.html   (1996 words)

DBMS - August 1998 - Data Warehouse Architect Disqualify the Diagnosis dimension because it is multivalued Although the helper table clearly violates the classic star join design where all the dimension tables have a simple one-to-many relationship to the fact table, there is no avoiding the issue of what to do with multivalued dimensions that designers insist on attaching to a fact table. The dimension tables, on the other hand, usually represent textual attributes that are already known about such things as the product, the customer, or the calendar. www.fortunecity.com /skyscraper/oracle/699/orahtml/dbmsmag/9808d05.html   (1979 words)

Data Warehouse Design Considerations for SQL Server 2000 Dimension tables must be indexed on their primary keys, which are the surrogate keys created for the data warehouse tables. The date and time dimensions are created and maintained in the data warehouse independent of the other dimension tables or fact tables – updating date and time dimensions may involve only a simple annual task to mechanically add the records for the next year. Tables are implemented in the relational database after surrogate keys for dimension tables have been defined and primary and foreign keys and their relationships have been identified. www.microsoft.com /technet/prodtechnol/sql/2000/reskit/part5/c1761.mspx   (9842 words)

DBMS - September 1998 - Data Warehouse Architect If you are descending the tree from certain selected parents to various subsidiaries, you join the dimension table to the helper table and the helper table to the fact table with the joins as shown in Figure 3. The problem is that you cannot use the recursive pointer with SQL to join the dimension table with the fact table and add up the consulting revenues or hours for a set of organizations, as in Figure 2. The beauty of this design is that you can place any normal dimensional constraint against the Customer dimension table and the helper table will cause all the fact table records for the directly constrained customers plus all their subsidiaries to be correctly summarized. www.dbmsmag.com /9809d05.html   (1803 words)

DBMS - October 1995 - Data Warehouse Architect If the cardinality of the repeated dimension data element is high (in other words, there are just a few duplications), the outrigger table may be nearly as big as the main dimension table. The fields in dimension tables are typically textual and are used as the source of constraints and row headers in reports. Multitable join queries occur after a series of browses and involve constraints placed on several of the dimension tables that are all joined to the fact table simultaneously. www.dbmsmag.com /9510d05.html   (1613 words)

I have a small problem relating to the relationships that exist between a dimension table and a fact table. Using slowly changing dimensions type 2, during any subsequent load cycles to the data warehouse where a column value changes for the production/natural key (Mary Smith in Figure 1) a new record is added to the customer dimension table to capture this information (see Figure 1). You can even create a view that joins the two dimension tables to fool the BI tool's semantic layer into thinking that a particular fact is linked to the parent Customer Master dimension. As the dimensions change, the new facts must ensure that they always inherit the most current dimensional key of the slow varying dimension. www.dmreview.com /article_sub.cfm?articleId=6349   (2060 words)

MIT Data Warehouse: Creating Advanced Queries The All Data Warehouse Tables page is your reference for identifying which tables are Fact tables and which ones are Dimension tables (see the Type column). Dimension tables contain fields that can be used to limit your queries or group numbers in your report. The Fact table is the body of the star and the Dimension tables are the points of the star. www.mit.edu:8001 /afs/athena.mit.edu/project/warehouse/aquery.html   (838 words)

Data Warehouse General : Differences between star and snowflake schemas The snowflake schema is a schema in which the fact table is indirectly linked to a number of dimension tables. star schema uses denormalized dimension tables,but in case of snowflake schema it uses normalized dimensions to avoid redundancy... The dimension tables are normalized to remove redundant data and partitioned into a number of dimension tables for ease of maintenance. www.geekinterview.com /question_details/236   (838 words)

Mat Stephen's SQL Server WebLog : Microsoft SQL Server 2005 Analysis Services – Unified Dimensional Model (UDM). Can you get your head round it? If you do BI, you’ll need to! Whilst a UDM can gather data from numerous data sources, the need to cleanse data still requires a data warehouse. UDM has five new dimension types, Role Playing, Fact, Reference, Data Mining and Many to many. Hence the virtual dimension, as a term, is now gone and concept converted to a real, first class, dimension. blogs.technet.com /mat_stephen/archive/2005/03/10/391943.aspx   (914 words)

DBMS - June 1998 - Data Warehouse Architect Fact tables with four and 12 foreign keys connecting outward to a halo of dimension tables present an interesting challenge during data extraction: You must intercept all the incoming fact records and replace all their key components with the correct surrogate key values at high speed. When every surrogate key in the fact table is a proper foreign key connecting to its respective primary key in one of the dimension tables, the fact table and the dimension tables obey referential integrity. In this case, the surrogate key in the fact table is a foreign key, which means that the value of the surrogate key exists in the corresponding dimension table. www.dbmsmag.com /9806d05.html   (2000 words)

data capture Sybase and data warehousing software sap data management data warehouse dimension relationships Sybase and data warehousing software meaningful psychologica data-capture guaranteed data capture9 data warehouse alcan knowledge base jobs data protection act principles data warehouse dimension. UK law data protection act unreadable cod and cod raw disks and accuser software which UK law data protection act data-capture data management association international15 how data compression works teradata dell data warehouse distributed personal data management win. data mining and the privacy act of your disaster capture facility requires full third party sear data-capture data back up and capture recommendations19 data backup florida data mining concepts with examples what is data miningg data mining and the. www.datarecovery42.com /data-capture.html   (2000 words)

MOLAP - a Whatis.com definition - see also: multidimensional online analytical processing MOLAP is often used as part of a data warehouse application. MOLAP (multidimensional online analytical processing) is online analytical processing (OLAP) that indexes directly into a multidimensional database. The Panda Project provides an overview of MOLAP and multidimensional databases. searchsqlserver.techtarget.com /sDefinition/0,,sid87_gci882493,00.html   (236 words)

Definitions The snowflake schema is a variant of the star schema model, where some dimension tables are normalized, thereby further splitting the data The most common modeling paradigm is the star schema, in which a data warehouse contains a large fact table, with no redundancy, and Sophisticated applications may require multiple fact tables to share dimension tables. gaia.ecs.csus.edu /~torbertk/csc177/homework/definitions.html   (236 words)

Snowflake schema - Wikipedia, the free encyclopedia The snowflake schema is a more complex data warehouse model than a star schema, and is a type of star schema. For example, a product dimension table in a star schema might be normalized into a products table, a product_category table, and a product_manufacturer table in a snowflake schema. That is, the dimension data has been grouped into multiple tables instead of one large table. en.wikipedia.org /wiki/Snowflake_schema   (236 words)

Badger Software Guide to Business Intelligence Applications The cube is a conceptual container for our data, with key numerical data stored pre-aggregated for each dimension. An OLAP database design is what we use in the data warehouse or mart. Once the data has been loaded into the database, and pre-aggregated in the cube, it becomes very easy for the application users to analyse data at any level, over any combinations of dimensions: www.badger.co.uk /business.htm   (1348 words)

Lexicon After the computer simulation of dynamical systems along with the computer graphic representation of their trajectories was developed by John von Neumann and associates in the 1940s, scientists became interested in the inverse process: given an attractor-basin portrait (that is, experimental data) find a dynamical rule fitting the data. A geometric space of finite dimension, finite or infinite in extent, points of which correspond to the observable states of a natural system. Such a dynamical model would then be the basis for prediction of new data. www.visual-chaos.org /lex.html   (754 words)

Glossary That's why you're reading a glossary of "data warehousing terminology" instead of a glossary of "data warehouse terminology". The emphasis in the data-based knowledge business needs to be kept on the process. A Product dimension could have levels for Product Family, Product Category, Product Subcategory, and Product Name. www.sdgcomputing.com /glossary.htm   (5777 words)

Best Practices for Microsoft Business Intelligence : Star vs. Snowflake Schemas A star schema collapses the multiple tables for a single dimension such as Product into a single flat dimension. A snowflake schema is very appealing to database administrators, because it normalizes the dimensional relationship. In a relational data warehouse, you should present to users a star schema rather than a snowflake schema. blogs.msdn.com /bi_systems/articles/164525.aspx   (449 words)

Seeking Science in Art: Meta-Level Modeling One may notice immediately that the star schema model shows more information about the contents or meanings of the business: it explicitly shows which attributes belong to which entities, what the dimensions are (e.g., location, time, product, customer) and how they are related to the FACT entity. For example, a star schema entity is either a fact entity or a dimension. It clearly defines the structures of fact and dimension entities used by this particular data warehouse; these rules are implicit in the start schema model in Figure 6. www.dmreview.com /master.cfm?NavID=55&EdID=881   (3827 words)

Discussion Forums : Starschema Vs Snowflake ... Star vs. Snowflake schema is a classic question asked when putting together a dimensional datawarehouse. Given the difference in size between a typical fact table, and the dimensions surrounding it, fact tables are where you should concentrate your effort to improve performance and reduce storage, given that most fact tables comprise 80% to 90% of your data warehouse. Basically, snowflaking is the process of removing low cardinality attributes from a dimension table, and placing them in a separate dimension table connected by a snowflake key. forums.oracle.com /forums/thread.jsp?forum=57&thread=180583&message=508120   (3827 words)

DataWarehousing.com - documenting data replication and data transformation sites on the Net A snowflake schema is a more complex data warehouse model as it groups dimension data into multiple tables instead of one large table. Snowflake schemas are often used for large-sized data warehouses and are primarily used for loading and replenishing data marts. Before deciding on which schema to implement, companies must assess their needs, the current and projected size of the data warehouse, as well as the data architecture. www.datawarehousing.com /techtips/techtip10.asp   (3827 words)

Enterprise Systems From Star to Snowflake to ERD: Comparing Data Warehouse Design Approaches In the snowflake schema, dimension information is modeled with a normalized structure, much like it would be in a traditional relational model. The star dimensional model has the same fact table as the snowflake schema; each dimension, however, is captured in one table– resulting in some redundant data. For example, the tool we used can require considerably more temporary table storage space with a star than with a snowflake or relational structure, in cases where duplicate rows are generated. www.esj.com /article.aspx?ID=101399125802PM   (3827 words)

DBMS - August 1996 - Data Warehouse Architect Thus in a grocery store chain, if the "back door" purchase orders database is one data mart and the "front door" retail sales database is another data mart, the two data marts will form a coherent part of an overall enterprise data warehouse if their common dimensions (say, time and product) conform. Once the common dimensions have been identified, the development of separate data marts must be managed under this common dimensional framework. The data warehouse is "off the hook" if it makes the most granular data available. www.dbmsmag.com /9608d05.html   (1386 words)

Best Practices for Microsoft Business Intelligence : Use surrogate keys Surrogate keys are maintained by the data warehouse staging process. The primary key for dimension tables should be a numeric, usually integer, surrogate key. There are several problems with using the business key as the primary key in the dimension table: blogs.msdn.com /bi_systems/articles/141648.aspx   (242 words)

Free Training - Tutorial 48: MSAS - Introducing Dimension and Cube Processing Everything that happens in the server is recorded, but the end user sees a multidimensional cube created out of the data in the data warehouse and has very little taste of what really goes on behind the scenes. The Multidimensional Expressions (MDX) query retrieves data in the hierarchical order. In this section we will be examining this black box to understand how the Analysis server processes Dimensions and cubes. www.exforsys.com /content/view/1381   (1073 words)

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