Main OLAP rules discussed:
OLAP Rule 1: Multidimensional Conceptual View
Multi-dimensional data models enable more straightforward and intuitive manipulation of data.
OLAP Rule 6: Generic Dimensionality
Every data dimension should be equivalent in its structure and operational capabilities
OLAP Rule 9: Unrestricted Cross-Dimensional Operations
Computational facilities must allow calculation and data manipulation across any number of data dimensions, and must not restrict any relationship between data cells.
OLAP Rule 10:Intuitive Data Manipulation
Data manipulation inherent in the consolidation path, such as drilling down or zooming out (i.e. drilling up), should be accomplished via direct action on the analytical model's cells, and not require use of a menu or multiple trips across the user interface.
Design Contraints:
There are two major constraint when designing your multi-dimensional database::
Business - the report requirements, and the representation of those reports, and moving from a spreadsheet to a multidimensional view
Data - the format, quality, source, and availability of data.
Important Definitions: (Microsoft BI: Making better decisions faster)
business intelligence (BI)
An approach to management that allows an organization to
define what information is useful and relevant to its corporate decision making.
Business intelligence is a multifaceted concept that empowers organizations to
make better decisions faster, convert data into information, and use a rational
approach to management.
Dimensions:
A dimension is a structural attribute of a cube that is a list of members, all of which are of a similar type in the user's perception of the data.
Measures:
Measures are the numeric values
that can be aggregated to give meaning to your dimensions.
calculated measure A measure that is calculated or derived from a combination of
base measures.
Grain:
The lowest level of drill-down is called 'grain', and is usually at the transaction level.
cube A
multidimensional data structure that represents the intersections of each unique
combination of dimensions. At each intersection there is a cell that contains a
data value.
database A
collection of related data that is organized in a useful manner for easy
retrieval. There are different applications of databases depending on the type
of data to be stored and how the data is to be used.
data warehouse A repository for data. Many experts define the data warehouse as a centralized data store that feeds data into a series of subject-specific data stores—called data marts. Others accept a broader definition of the data warehouse as a collection of integrated data marts.
desktop online analytical processing (DOLAP) An OLAP storage mode that keeps data on a client's machine and provides local multidimensional analysis.
hierarchy The organization of levels within a dimension that (1) reflects how data is aggregated from detailed levels to summarized levels and (2) serves as the drill-down path for top-down business analysis.
ragged hierarchy A hierarchy that has an inconsistent number of drill-down levels.
hybrid online analytical processing (HOLAP) An OLAP tool that can store data in both multidimensional databases and relational databases.
multidimensional online analytical processing (MOLAP) An OLAP storage mode in which data is placed into special structures that are stored on a central server(s).
online analytical processing (OLAP) Multidimensional analysis that is supported by interface tools and database structures that allow instantaneous access and easy user manipulation. Online analytical processing got its name because this name contrasts well with OLTP, a term that was already in widespread use when the term OLAP was created. There are fundamental differences between transaction processing and analytical processing. OLAP systems support multidimensional analysis at the speed of thought. OLAP typically follows the client/server paradigm, where an OLAP database server is accessed by many users who use multidimensional client tools to analyze data.
online transaction processing (OLTP) A data processing system designed to record all the business transactions of an organization as they occur. OLTP systems are structured for the purposes of running the day-to-day raw data of business, which requires efficiency and minute processing of transactions at the lowest level of detail. An OLTP system processes a transaction, performs all the elements of the transaction in real time, and processes many transactions on a continuous basis. OLTP systems usually offer little or no analytical capabilities.
data warehouse A repository for data. Many experts define the data warehouse as a centralized data store that feeds data into a series of subject-specific data stores—called data marts. Others accept a broader definition of the data warehouse as a collection of integrated data marts.
desktop online analytical processing (DOLAP) An OLAP storage mode that keeps data on a client's machine and provides local multidimensional analysis.
hierarchy The organization of levels within a dimension that (1) reflects how data is aggregated from detailed levels to summarized levels and (2) serves as the drill-down path for top-down business analysis.
ragged hierarchy A hierarchy that has an inconsistent number of drill-down levels.
hybrid online analytical processing (HOLAP) An OLAP tool that can store data in both multidimensional databases and relational databases.
multidimensional online analytical processing (MOLAP) An OLAP storage mode in which data is placed into special structures that are stored on a central server(s).
online analytical processing (OLAP) Multidimensional analysis that is supported by interface tools and database structures that allow instantaneous access and easy user manipulation. Online analytical processing got its name because this name contrasts well with OLTP, a term that was already in widespread use when the term OLAP was created. There are fundamental differences between transaction processing and analytical processing. OLAP systems support multidimensional analysis at the speed of thought. OLAP typically follows the client/server paradigm, where an OLAP database server is accessed by many users who use multidimensional client tools to analyze data.
online transaction processing (OLTP) A data processing system designed to record all the business transactions of an organization as they occur. OLTP systems are structured for the purposes of running the day-to-day raw data of business, which requires efficiency and minute processing of transactions at the lowest level of detail. An OLTP system processes a transaction, performs all the elements of the transaction in real time, and processes many transactions on a continuous basis. OLTP systems usually offer little or no analytical capabilities.
relational online analytical processing
(ROLAP) An OLAP storage mode where data is
stored in relational databases.
uniform aggregation A method of summarizing data from its lowest level of detail to
its highest level of detail, where data can be aggregated the same way across
all dimensions.
semiadditive aggregation A method of summarizing data from its lowest level of detail to its highest level of detail in which measures are not aggregated uniformly across all dimensions.
slice and dice Two complementary methods for interacting with data. Slicing means isolating a specific member of a dimension for analysis. Dicing means breaking a data set into smaller pieces by examining how measures intersect with multiple dimensions.
***Memorize all of these!
semiadditive aggregation A method of summarizing data from its lowest level of detail to its highest level of detail in which measures are not aggregated uniformly across all dimensions.
slice and dice Two complementary methods for interacting with data. Slicing means isolating a specific member of a dimension for analysis. Dicing means breaking a data set into smaller pieces by examining how measures intersect with multiple dimensions.
***Memorize all of these!
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