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What is 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.
Some efficient techniques are used as spare data storage administration on a disk to enhance the response time. There are various OLAP tools, as Pilot products (Software Analysis Server) introduce ‘time’ also as an additional dimension for analysis, thereby allowing time ‘series’ analysis. Some products as Oracle Express Server introduce strong analytical effectiveness into the database itself.
It is software that is used to index data into a multidimensional database. As one form of online analytical processing, the MOLAP techniques enable the viewer of the data to arrange the records in several types of the sequence by defining sorting protocols and another form of data retrieval. This defines that rather than having to make do with one or two possible views of data, the end-user can view the records from several different angles, and with varying perimeters defined.
Using MOLAP, a sales manager can generate a document sorted by an account manager, arranged according to user names, and identify all generated revenue related to the salesperson’s accounts within a given period. By a similar token, MOLAP can make a list of clients based in a given location, which can be beneficial in setting appointments when a salesperson plans to be in the area. Essentially, MOLAP can capture and sort by any area included in the database that is not limited to that type of activity.
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