Statistical models for improving the performance of database operations

Abstract

A method for performing an automatic software-driven statistical evaluation of a large amount of data to be assigned to statistical variables in a database contained in at least one cluster. The method is characterized by using a statistical model to model an approximate description of a relative frequency of the state or states of the statistical variables and a statistical dependencies between the state or states, and then determining the approximate relative frequency of the state or states of the statistical variables and the approximate relative frequency belonging to a predetermined relative frequency of the state or states of the statistical variables and an expected value of the state or states of the statistical variables dependent thereon.

1 . Method for the automatic, software-driven statistical evaluation of large amounts of data that is to be assigned to statistical variables in a database, in particular, contained in one or several clusters which is characterized in that 
 a statistical model for the approximate description of the relative frequencies of the states of the variables and the statistical dependencies between said states, is learnt and    by means of the data stored in the database and is used to determine, on the basis of the statistical model, the approximate relative frequencies of states of the variables, in addition to the approximate relative frequencies belonging to the pre-determinable relative frequencies of states of the variables and expected values of the states of variables dependent thereon.    
   
   
       2 . Method according to  claim 1 , characterized in that as the statistical model, a graphical probabilistic model, in particular a Bayesian network, is used.  
   
   
       3 . Method according to  claim 1 , characterized in that a statistical clustering model, in particular a Bayesian clustering model, is used by means of which the data is subdivided into many clusters.  
   
   
       4 . Method according to  claim 1 , characterized in that likewise a clustering model based on a distance measurement is used by means of which the data is likewise subdivided into a plurality of clusters.  
   
   
       5 . Method according to  claim 3  or  4 , characterized in that the considered data is restricted to the data contained in one cluster or a number of clusters.  
   
   
       6 . Method according to  claim 5 , characterized in that it is possible that such clusters are restricted in which the data belonging to the specific states of variables contains at least one specific relative frequency.  
   
   
       7 . Method according to one of the  claims 4  to  6 , characterized in that the data belonging to a cluster is stored on a data carrier in a way appropriate to the cluster affiliation.  
   
   
       8 . Method according to one of the previous claims, characterized in that database reporting methods or OLAF methods are further used to determine the relative frequencies and expected values of the states of variables.  
   
   
       9 . Method according to  claim 8 , characterized in that database reporting methods or OLAP methods are used if a test variable assumes or exceeds a predetermined value.  
   
   
       10 . Method for the automatic, software-driven statistical evaluation of large amounts of data that is to be assigned to statistical variables in a database, in particular, contained in one or several clusters which is characterized in that, 
 the data is subdivided into many clusters by a clustering model based on distance measurement and, if required, the considered data is restricted to the data contained in one cluster or several clusters, and    database reporting methods or the OLAF methods are used to determine the relative frequencies and expected values of the states of variables.    
   
   
       11 . Application of the method according to one of the previous claims for the statistical evaluation of customer data, in particular, in the Web reporting/Web mining area and in customer relationship management systems.  
   
   
       12 . Application of the method according to one of the previous claims for the statistical evaluation of environmental databases, medical databases or genome databases.