Considering various disadvantages of relational model EF Codd founded the
12 basic principles of a multidimensional data presentation as :-*1
1. Multi-Dimensional Conceptual View: Business-analyst "sees the company world" multivariate and multi-dimensional, accordingly and conceptual data model representation in OLAP product should be multivariate and multi-dimensional on a nature, that allow analysts to fulfil intuitive operations: "slice and dice", rotate and pivot directions of consolidation.
2. Transparency: The user should not know what concrete resources are used for storage and data processing and how the data are organized. Without dependence from that, the OLAP-product a part of resources of the user is whether or not, this fact should be transparent for the user. If OLAP it is granted by client - server calculations this fact also, whenever possible, should be imperceptible for the user. OLAP should be granted in a context of open architecture, allowing the user where he was to communicate through the analytical tool with the server. In addition transparency should be achieved in interaction of the analytical tool with homogeneous / heterogeneous databases.
3. Accessibility: Business analyst should have a possibility to analyze within the framework of the common conceptual scheme, thus the data may remain under the control of old, "inherited" DBMS, being thus pegged to common analytical model. So OLAP tool kit should superimpose its own logic scheme on physical data arrays, fulfilling all conversions required for support of an uniform, agreed and complete "user sight" on the information.
4. Consistent Reporting Performance: With increasing of numbers of measures and database size analysts should not face with any decrease of productivity. Stable productivity is necessary for maintaining an usage simplicity which is required for finishing OLAP up to the end user. If the user - analyst will test essential distinctions in productivity according to number of measures then he will try to compensate these distinctions the strategy of development that will call data representation other ways, but not with what it is really necessary to present the data. Costs of time to bypass the system for compensation of its inadequacy is not what analytical products are intended for.
5. Client-Server Architecture: Large data volumes, required operating analytical processing stored on mainframes, but extracted from PC. Therefore one of requests - ability of OLAP products to operate in client - server environment. Main idea here is that OLAP tool server component should be intelligent enough and can build the common conceptual scheme based on generalization and consolidations of various logical and physical schemes of corporate databases.
6. Generic Dimensionality: All measures should be equivalent. Additional performances may be given to separate measures, but as all of them are symmetric, the given additional functionality may be given to any measure. Base data structure, formulas and report formats should not base on any one measurement and should not be displaced aside to any measure. Each measure should be applied irrespectively to its structure and operational abilities. Additional operational abilities may be granted to any selected measure, and as measures are symmetric, any function may be given to any measure.
7. Dynamic Sparse Matrix Handling: OLAP tool should guarantee optimal processing of the sparse matrixes. Access speed should be saved without dependence from data cells layout and to be a constant for the models having different number of measures and different data sparse.
8. Multi-User Support: Frequently some analysts have the necessity to work simultaneously with one analytical model or to create various models based on the same data. OLAP tool should grant them competitive access, guarantee integrity and data protection.
9. Unrestricted Cross-dimensional operations: Data calculation and manipulation on any number of measures should not prohibit or limit any ratios among data cells. The conversions requiring arbitrary definition, should be set in functionally complete formula language.
10. Intuitive Data Manipulation: Directions consolidation, detailing data in columns and rows, aggregation and other data manipulations inherent to hierarchy structure , should be executed in maximum convenient, natural and comfortable user interface.
11. Flexible Reporting: Various data visualization methods should be supported, other word reports should be presented in any possible orientation.
12. Unlimited Dimensions and Aggregation Levels: Strongly recommended, that each serious OLAP tool should have a minimum of 15 (better more than 20 measures in analytical model. Moreover, each of these measures should admit practically unlimited amount of aggregation levels, defined by user, on any direction of consolidation.
Basic OLAP
functionality
1)
Selection of Dimension – slice and dice.
2)
View from multiple perspective - Multiple views
in data, allow pivoting and in figurative formats.
3)
Allow filtration of data based on selection.
4)
Drill-Down to lower level and Roll-Up to higher level,
i.e. range all aggregation level.
Basic Terms and definition:-
1)
Cube:
Is a multiple dimensional structure (can be more than 3) that stores
pre-computed values of data (measures) to provide faster retrieval of results
when aggregated across various dimension.
2)
Measures:
Is a numeric value collected from facts which can be aggregated across multiple
dimensions.
3)
Dimension:
are a broader group of descriptive data, which is like an index to measure
something, it can be broader section of a business aspect.
4)
Levels:
are the hierarchy within dimensions, which can be drilled down to get details
of below levels.
5)
Scope:
Are the way to create arbitrary, predefined subsets of a dimension at a grain
level.
6)
Member:
are the data values of a business aspect.
References:-
