File Name: olap and multidimensional data analysis .zip
- Multidimensional Data Analysis
- Query Optimization and Execution for Multi-Dimensional OLAP
- OLAP and Multidimensional Model
- Data Warehousing - OLAP
Online analytical processing of OLAP is een applicatie-architectuur die door een bedrijf wordt gebruikt ter ondersteuning van de analytische applicaties. Het is geen datawarehouse of databasemanagementsysteem. De belangrijkste toepassingen van OLAP zijn bedrijfsmatige problemen waarbij records uit gigantische gegevensverzamelingen gehaald moeten worden.
It allows managers, and analysts to get an insight of the information through fast, consistent, and interactive access to information. ROLAP servers are placed between relational back-end server and client front-end tools. MOLAP uses array-based multidimensional storage engines for multidimensional views of data.
Multidimensional Data Analysis
OLAP tools enable users to analyze multidimensional data interactively from multiple perspectives. OLAP consists of three basic analytical operations: consolidation roll-up , drill-down, and slicing and dicing. For example, all sales offices are rolled up to the sales department or sales division to anticipate sales trends. By contrast, the drill-down is a technique that allows users to navigate through the details.
For instance, users can view the sales by individual products that make up a region's sales. Slicing and dicing is a feature whereby users can take out slicing a specific set of data of the OLAP cube and view dicing the slices from different viewpoints. 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, using an aggregate function or aggregation function. 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.
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. Some aggregation functions can be computed for the entire OLAP cube by precomputing values for each cell, and then computing the aggregation for a roll-up of cells by aggregating these aggregates, applying a divide and conquer algorithm to the multidimensional problem to compute them efficiently.
Functions that can be decomposed in this way are called decomposable aggregation functions , and include COUNT, MAX, MIN, and SUM , which can be computed for each cell and then directly aggregated; these are known as self-decomposable aggregation functions.
These latter are difficult to implement efficiently in OLAP, as they require computing the aggregate function on the base data, either computing them online slow or precomputing them for possible rollouts large space.
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. 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.
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 is not limited 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. The superior flexibility of ROLAP tools allows this less than optimal design to work, but performance suffers. There is no clear agreement across the industry as to what constitutes "Hybrid OLAP", except that a database will divide data between relational and specialized storage.
HOLAP tools can utilize both pre-calculated cubes and relational data sources. Each type has certain benefits, although there is disagreement about the specifics of the benefits between providers. The following acronyms are also sometimes used, although they are not as widespread as the ones above:.
Several OLAP vendors — both server and client — adopted it. Codd , who has been described as "the father of the relational database". Codd's paper  resulted from a short consulting assignment which Codd undertook for former Arbor Software later Hyperion Solutions , and in acquired by Oracle , as a sort of marketing coup.
As a result, Codd's "twelve laws of online analytical processing" were explicit in their reference to Essbase. There was some ensuing controversy and when Computerworld learned that Codd was paid by Arbor, it retracted the article. OLAP market experienced strong growth in late s with dozens of commercial products going into market. Many clients support interactive data exploration where users select dimensions and measures of interest. Some dimensions are used as filters for slicing and dicing the data while others are selected as the axes of a pivot table or pivot chart.
Users can also vary aggregation level for drilling-down or rolling-up the displayed view. Clients can also offer a variety of graphical widgets such as sliders, geographic maps, heatmaps and more which can be grouped and coordinated as dashboards. An extensive list of clients appears in the visualization column of the comparison of OLAP servers table.
From Wikipedia, the free encyclopedia. Retrieved Business Intelligence for Telecommunications. CRC Press. VDM Verlag Dr. Computers and Electronics in Agriculture. OLAP Council. Management information systems 9th ed. Data Warehousing Review. Multidimensional models boost viewing options. Data Mining and Knowledge Discovery. OLAP Report. Archived from the original on January 24, Jensen, Christian December Distributed Systems Online.
Database Trends and Applications. American Journal of Physiology. Heart and Circulatory Physiology. December TopCells: Keyword-based search of top-k aggregated documents in text cube. SBP Lecture Notes in Computer Science". In Greenberg, A. Berlin, Heidelberg: Springer. Archived from the original on May 28, Archived from the original on December 21, Retrieved November 27, Zhang, Chao Data warehouses.
Fact table Early-arriving fact Measure. Dimension table Degenerate Slowly changing. Business intelligence software Reporting software Spreadsheet. Bill Inmon Ralph Kimball. Categories : Online analytical processing Data management Information technology management.
Query Optimization and Execution for Multi-Dimensional OLAP
Most times used interchangeably, the terms Online Analytical Processing OLAP and data warehousing apply to decision support and business intelligence systems. OLAP systems help data warehouses to analyze the data effectively. The dimensional modeling in data warehousing primarily supports OLAP, which encompasses a greater category of business intelligence like relational database, data mining and report writing. Many of the OLAP applications include sales reporting, marketing, business process management BPM , forecasting, budgeting , creating finance reports and others. Each OLAP cube is presented through measures and dimensions. Measures refers to the numeric value categorized by dimensions.
An OLAP cube is a multi-dimensional array of data. The term cube here refers to a multi-dimensional dataset, which is also sometimes called a hypercube if the number of dimensions is greater than 3. A cube can be considered a multi-dimensional generalization of a two- or three-dimensional spreadsheet. For example, a company might wish to summarize financial data by product, by time-period, and by city to compare actual and budget expenses. Product, time, city and scenario actual and budget are the data's dimensions.
Multidimensional data analysis is also possible if a relational database is used. By that would require querying data from multiple tables. On the contrary, MOLAP has all possible combinations of data already stored in a multidimensional array. MOLAP can access this data directly. MOLAP tools process information with the same amount of response time irrespective of the level of summarizing. MOLAP tools remove complexities of designing a relational database to store data for analysis. MOLAP server implements two level of storage representation to manage dense and sparse data sets.
PDF | In this paper we want to introduce the basic concepts of OLAP systems. I continue to describe systems architecture namely ROLAP systems, MOLAP.
OLAP and Multidimensional Model
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Data Warehousing - OLAP
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What is MOLAP?
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