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Nabisco has created the online analytical processing (OLAP) data mart
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∙ 13y ago
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What does the OLAP do?
The OLAP allows Nabisco to accurately track sales and consumer preferences
What is the difference between OLAP and data mining?
difference between Data Mining and OLAP
What is olap?
OLAP stands for Online Analytical Processing. An OLAP system can be considered as a category of applications and technologies that are used for collecting, managing, processing and presenting multi-dimensional data for analysis and management purposes.
What does OLAP allow?
OLAP allows Nabisco to accurately track sales and consumer preferences, with the largest data mart holding sales, price discount, spoilage, transportation, and promotional information for two years
What are the names of some major companies offering business intelligence OLAP software?
Some major companies that offer business intelligence OLAP software include Hexaware, Adatis, Amosca, Timberlake, Interactive Reporting, and Tech Target.
Explain the OLAP and OLTP system with example?
OLTP vs. OLAPWe can divide IT systems into transactional (OLTP) and analytical (OLAP). In general we can assume that OLTP systems provide source data to data warehouses, whereas OLAP systems help to analyze it.- OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).- OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).
Distinguish between DBMS and OLAP systems?
The issue is, what distinguishes relational database systems and multidimensional data base systems. It is certainly possible to have an OLAP DMBS, and indeed a number of them have been on the market in the past. The defining difference is how the data is stored. An OLAP system has specialized data structures for optimizing performance with multidimensional data. A relational system uses data tables and SQL to store data. An native OLAP system (a.k.a MOLAP) does not store the data in relational tables. ...At least not directly. For example Oracle embeds their MOLAP system into relational tables. That can make it confusion, but for simplicities sake, just consider, a conventional, relational DBMS stores data in tables and uses SQL, an OLAP system uses something else and a different language, depending on the vendor. Examples are store data in variables, use Oracle OLAP DML, store data in Microsoft Analysis Services, use MDX, Store data in Essbase, use MDX, etc. For detailed information on using a native OLAP system see "The Multidimensional Data Modeling Tool Kit" on Amazon.
What is the disadvantage of olap?
OLAP, and its reliance on the data warehousing environment, are two of the most significant new technology areas. Moreover, the use of relational design and relational database technology are not feasible implementations to support OLAP design because of the complexity of the queries. The business problem is that OLAP queries are not real-time queries because of the refresh cycle of data into the OLAP data repository. Conventional designs call for integration of data into an operational data store where it can be cleansed, transformed, extracted, & then loaded into the OLAP data repository. This is accomplished through the use of (ETL) tools. The ETL process is generally complicated because data must be integrated and transformed for loading into the nonnormalized relational schema usually associated with OLAP environments. As such, the process can be complicated and time consuming, and with large amounts of data may only occur at monthly or quarterly time intervals. This creates the problem of not having real-time data in the OLAP repository. Real-time data exists in the OLTP environment where the time horizon of data within the OLTP environment is much shorter because performance decreases can occur with growing amounts of data. This is opposite of the nature and goals of the OLAP environment where data is aggregated and the time horizon of data grows to some large amount as determined by the information life cycle policy of the organization. The main problems you have to face using OLAP as a source is that OLAP engines, in general, are designed to return small result sets from highly aggregated data, whereas data mining, in general, is designed to perform operations on large sets of raw (or preprocessed) data. The implementation of OLAP in Analysis Services, requires that all of the result set be materialized in memory before returning to the client. This generally isn't a big deal for typical OLAP queries, but if you are, for instance, trying to mine all of your transaction data for the past 10 years, you will run into difficulties, in short the data gathered may not be (relatively) recent enough to qualify as real-time data for business intelligence purposes.
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