On-Line Analytical Processing or abbreviated OLAP is basically a special method to perform analysis of data contained in data storage media in the form of database and then proceed with making analysis report in accordance with the request of the user or user. For that purpose the data in the form of information is made into a special format by giving groups or groups to the data, this is called the cube models. OLAP is a technology that allows analysts, managers and executives to simultaneously access data quickly, consistently and interactively with a variety of visualization and visualization of information where each line of data can be transformed to reflect the company or organizational dimension so that it is easily understood by users or users.
Here are the main characteristics found on On-Line Analytical Processing that includes:
The data on the OLAP is stored in a multidimensional model. If the relational database consists of two dimensions, then the multidimensional bases consist of many dimensions that can be separated by OLAP into several sub attributes. OLAP can be used for data mining or finding relationships between an undiscovered item.
In OLAP databases do not necessarily have a large size such as a data warehouse, because not all transactions require trend analysis, since OLAP objectives display data in a dynamic table, which will automatically summarize data into multiple slices of different data and allow the user or user to interactively perform calculations and as well as create a report format.
OLAP is a computer process that allows users to easily and selectively select and view data from different perspectives. Some of the activities OLAP performs include generating queries, requesting adhoc reports, supporting statistical analysis, interactive analysis, building multimedia applications. To facilitate this OLAP DW is required with a set of tools that have multidimensional capabilities. These tools can be querytools, spreadsheets, data mining tools, and data visualization.
View is closely related to OLAP and data warehouse. OLAP queries are usually queryagregat. An analyst usually wants a quick answer to queries on a very large dataset, and naturally takes into account the initial calculation of the view. In particular, cube operators give rise to several closely related queryagregats. The relationship that exists between many queryaggggs arising from a single cube operation can be exploited to develop a very effective early computational strategy.
Tools to create the report is the table itself, namely by dragging the columns and rows. Users or users can change the form of reports and categorize them according to the wishes and needs of the user or user and the OLAP engine will automatically calculate the new data.
The OLAP query is affected by two things: query language (SQL) and spreadsheet structure. A common operation in an OLAP query is to aggregate one or more dimensions. The following types of OLAP queries are:
1. Roll-Up is to aggregate at different levels of the dimension hierarchy. For example, for each city given total sales, then for the total sales of each province can be obtained by adding total sales to all cities within a province.
2. Drill-Down is the opposite of Roll-Up. For example, for every province can be given total sales, then total sales of each city can be in Drill-Down.
3. Pivoting is to aggregate on selected dimensions. For example if done pivoting on location and time obtained cross-tabulation. Cross-tabulation is a collection of the following queries:
SELECT SUM (S.Sales)
FROM Sales S, Times T
WHERE S.timeid = T.timeid
GROUP BY T.year
and
SELECT SUM (S.Sales)
FROM Sales S, Location L
WHERE S. timeid = L.timeid
GROUP BY L.state
So the new query as follows:
SELECT SUM (S.Sales)
FROM Sales S, Times T, Location L
WHERE S.timeid = T.timeid AND S.timeid = L.timeid
GROUP BY T.year, L.state
4. Slicing and Dicing ie looking for similarity and selection range on one or more dimensions.
Here are the main characteristics found on On-Line Analytical Processing that includes:
Advertisement
- Support the use and use of data warehouses that have multidimensional data.
- Provide interactive query facility and complex analysis.
- Provide drill-down facility to obtain detailed information, and roll-up to obtain aggregate in multidimensional.
- Able to produce calculations and comparisons.
- Be able to present results in the form of numbers that are easy to understand and presentation in graphical form.
The data on the OLAP is stored in a multidimensional model. If the relational database consists of two dimensions, then the multidimensional bases consist of many dimensions that can be separated by OLAP into several sub attributes. OLAP can be used for data mining or finding relationships between an undiscovered item.
In OLAP databases do not necessarily have a large size such as a data warehouse, because not all transactions require trend analysis, since OLAP objectives display data in a dynamic table, which will automatically summarize data into multiple slices of different data and allow the user or user to interactively perform calculations and as well as create a report format.
OLAP is a computer process that allows users to easily and selectively select and view data from different perspectives. Some of the activities OLAP performs include generating queries, requesting adhoc reports, supporting statistical analysis, interactive analysis, building multimedia applications. To facilitate this OLAP DW is required with a set of tools that have multidimensional capabilities. These tools can be querytools, spreadsheets, data mining tools, and data visualization.
View is closely related to OLAP and data warehouse. OLAP queries are usually queryagregat. An analyst usually wants a quick answer to queries on a very large dataset, and naturally takes into account the initial calculation of the view. In particular, cube operators give rise to several closely related queryagregats. The relationship that exists between many queryaggggs arising from a single cube operation can be exploited to develop a very effective early computational strategy.
Tools to create the report is the table itself, namely by dragging the columns and rows. Users or users can change the form of reports and categorize them according to the wishes and needs of the user or user and the OLAP engine will automatically calculate the new data.
The OLAP query is affected by two things: query language (SQL) and spreadsheet structure. A common operation in an OLAP query is to aggregate one or more dimensions. The following types of OLAP queries are:
1. Roll-Up is to aggregate at different levels of the dimension hierarchy. For example, for each city given total sales, then for the total sales of each province can be obtained by adding total sales to all cities within a province.
2. Drill-Down is the opposite of Roll-Up. For example, for every province can be given total sales, then total sales of each city can be in Drill-Down.
3. Pivoting is to aggregate on selected dimensions. For example if done pivoting on location and time obtained cross-tabulation. Cross-tabulation is a collection of the following queries:
SELECT SUM (S.Sales)
FROM Sales S, Times T
WHERE S.timeid = T.timeid
GROUP BY T.year
and
SELECT SUM (S.Sales)
FROM Sales S, Location L
WHERE S. timeid = L.timeid
GROUP BY L.state
So the new query as follows:
SELECT SUM (S.Sales)
FROM Sales S, Times T, Location L
WHERE S.timeid = T.timeid AND S.timeid = L.timeid
GROUP BY T.year, L.state
4. Slicing and Dicing ie looking for similarity and selection range on one or more dimensions.
No comments