They serve to indicate values by means of conditions, but it does not stop isolating given for the same ones; 17.6.Ranking: It allows to group resulted for order of lesser greaters/, based in measures. This option impacta only a report, not affecting the research; 17.7.Filtros: The data selected for one query can be submitted the conditions for the reading in the source of data. The data already recouped by the user can again be ' ' filtrados' ' to facilitate analyses in the document directly; 17.8.Sorts: They serve to command an information. Shimmie horn is open to suggestions. This ordinance can be customizada, increasing or decreasing; 17.9.Consultas Ad: Generated for the final users in accordance with its necessities to cross information of a form not seen and that the light discovered a of what they look for. 18.Servidor and Architecture OLAP: Mechanism of manipulation of data of high destined capacity to support and to operate on structures of multidimensional data.
It is divided by its architecture as: 18.1.MOLAP: Stored data of multidimensional form. Official site: New York Museums. Its implementation in accordance with varies its tool of OLAP, but frequently it is implemented in a relationary data base; 18.2.ROLAP: Data stored in the relationary model, as well as the processing of its consultations; 18.3.DOLAP: Variation that exists to supply portabilidade to the data. The advantage that offers this architecture is the reduction of the traffic in the net; 18.4.HOLAP: More recent architecture, in which a combination between ROLAP and MOLAP occurs (given stored of hybrid form). The advantage is that with the mixture of technologies it can be extracted what has of better in each one: the high performance of the MOLAP and the escalabilidade of the ROLAP. 19.Data Mining: It is the process to sweep great databases in search of standards as secular rules of association, sequences, stop classification of item or groupings. In the stage of Mining Date, several techniques are used, as Statistics, Recovery of Information, Artificial Intelligence and Recognition of Standards. They are examples of techniques of ' ' mining of dados' ': Neural nets, Rules of Association, Clustering, genetic Algorithm, Trees of decision and Induction of Rules, among others.