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What are the OLAP tools in data mining?
There are three main categories of OLAP tools which are as follows −
MOLAP − MOLAP represents Multidimensional OLAP. It supports tuples as the data storage unit. The MOLAP applies a dedicated n-dimensional array storage engine and OLAP middleware to handle data. Hence, OLAP queries are completed through direct addressing to the associated multidimensional views (data cubes).
This structure focuses on the pre-computation of the transactional information into the aggregations, which results in fast query execution performance. Particularly, MOLAP pre-computes and stores aggregated measures at each hierarchy level at load time, and stores and indexes these values for immediate retrieval.
The full pre-computation needed a large amount of overhead, both in processing time and in the storage area. For sparse data, MOLAP needs sparse matrix compression algorithms to enhance storage uses, and thus in general is featured by the smaller on-disk size of data in comparison with data saved in RDBMS.
MOLAP-based products arrange, navigate and analyze data generally in an aggregated form. They needed tight coupling with the software and they were based upon a multidimensional database (MDDB) system. An effective implementations save the data in a way similar to the form in which it is used by using improved storage methods to minimize storage.
ROLAP − ROLAP stands for Relational OLAP. It can store the data based on the already familiar relational DBMS technology. In this case, data and the related aggregations are saved in RDBMS, and OLAP middleware can implement managing and exploration of data cubes.
This architecture targets the optimization of the RDBMS back end and supports additional tools and services including data cube navigation logic. Because of the use of the RDBMS back end, the main benefit of ROLAP is scalability in managing large data volumes.
ROLAP systems work frequently from the data that occupy a relational database, where the base data and dimension tables are stored as relational tables. This model allows the multidimensional analysis of records.
It is the newest and quickest-growing OLAP technology segment in the industry. This method enables several multidimensional views of two-dimensional relational tables to be generated, preventing structuring records around the desired view.
MQE − MQE stands for Managed Query Environment. Some products have been able to provide ad-hoc queries such as data cube and slice and dice analysis capabilities. It is done by developing a query to select data from the DBMS, which delivers the requested data to the system where it is placed into a data cube.
This data cube can be locally stored in the desktop and also manipulated there to reduce the overhead, it is required to create the structure each time the query is executed. After storing the data in the data cube, multidimensional analysis and operations can be applied to it.
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