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Although this may sound like a lot of work, managing one data cube is more efficient than writing a number of custom reports. Currently, some vendors provide administrative tools to get the data into the cubes in the first place, in the proper form, and on a regular basis. Hence, the job of managing data has been simplified for users.

A well-organized presentation of the concepts and applications of online analytical processing OLAP for those interested in decision support systems. Try our Search Tips. Topics Libraries Unlimited Librarianship: Available for Course Adoption. Other Titles of Interest.

What is the Definition of OLAP? OLAP Definition

These viewpoints are sometimes called dimensions such as looking at the same sales by salesperson or by date or by customer or by product or by region, etc. Databases configured for OLAP use a multidimensional data model, allowing for complex analytical and ad hoc queries with a rapid execution time.

OLAP is typically contrasted to OLTP online transaction processing , which is generally characterized by much less complex queries, in a larger volume, to process transactions rather than for the purpose of business intelligence or reporting. It consists of numeric facts called measures that are categorized by dimensions. The measures are placed at the intersections of the hypercube, which is spanned by the dimensions as a vector space. The usual interface to manipulate an OLAP cube is a matrix interface, like Pivot tables in a spreadsheet program, which performs projection operations along the dimensions, such as aggregation or averaging.

The cube metadata is typically created from a star schema or snowflake schema or fact constellation of tables in a relational database. Measures are derived from the records in the fact table and dimensions are derived from the dimension tables. Each measure can be thought of as having a set of labels , or meta-data associated with it. A dimension is what describes these labels ; it provides information about the measure.

Multidimensional structure is defined as "a variation of the relational model that uses multidimensional structures to organize data and express the relationships between data". The data still remains interrelated. Multidimensional structure is quite popular for analytical databases that use online analytical processing OLAP applications.


Data can be viewed from different angles, which gives a broader perspective of a problem unlike other models. It has been claimed that for complex queries OLAP cubes can produce an answer in around 0. Aggregations are built from the fact table by changing the granularity on specific dimensions and aggregating up data along these dimensions. The number of possible aggregations is determined by every possible combination of dimension granularities. The combination of all possible aggregations and the base data contains the answers to every query which can be answered from the data.

Because usually there are many aggregations that can be calculated, often only a predetermined number are fully calculated; the remainder are solved on demand.

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The problem of deciding which aggregations views to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time.

View selection is NP-Complete.

OLAP systems have been traditionally categorized using the following taxonomy. MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database. Some MOLAP tools require the pre-computation and storage of derived data, such as consolidations — the operation known as processing.

OLAP - Online Analytical Processing

The data cube contains all the possible answers to a given range of questions. As a result, they have a very fast response to queries. On the other hand, updating can take a long time depending on the degree of pre-computation. Pre-computation can also lead to what is known as data explosion. Other MOLAP tools, particularly those that implement the functional database model do not pre-compute derived data but make all calculations on demand other than those that were previously requested and stored in a cache. ROLAP works directly with relational databases and does not require pre-computation.

The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. It depends on a specialized schema design.

OLAP: faster analyzing data

This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality. ROLAP tools do not use pre-calculated data cubes but instead pose the query to the standard relational database and its tables in order to bring back the data required to answer the question. ROLAP tools feature the ability to ask any question because the methodology does not limit to the contents of a cube.

ROLAP also has the ability to drill down to the lowest level of detail in the database. However, since it is a database, a variety of technologies can be used to populate the database. However, as with any survey there are a number of subtle issues that must be taken into account when interpreting the results.