Abstract: A computer-implemented method processes a query on instances of an object of an object-oriented environment. The object has a root object and member fields which are sub-objects of the root object. The root object and each sub-object correspond to an entity represented by and stored in form of a table. The method includes analyzing the query to identify those objects which are necessary to execute the query. For each of the primary key values of a table corresponding to a root object, the method includes: (i) generating an instance of the object by populating the member fields of those objects which have been identified as necessary in said analyzing step with data identified by the respective primary key in their corresponding tables; (ii) executing the query on the generated object instance; and (iii) adding the query result to a result accumulator storage.

Abstract: A computer-implemented method processes a query on instances of an object of an object-oriented environment. The object has a root object and member fields which are sub-objects of the root object. The root object and each sub-object correspond to an entity represented by and stored in form of a table. The method includes analyzing the query to identify those objects which are necessary to execute the query. For each of the primary key values of a table corresponding to a root object, the method includes: (i) generating an instance of the object by populating the member fields of those objects which have been identified as necessary in said analyzing step with data identified by the respective primary key in their corresponding tables; (ii) executing the query on the generated object instance; and (iii) adding the query result to a result accumulator storage.

Abstract: A benchmarking computing application is configured to create a benchmark run for a segment of customer profiles. Customer profiles from the segment include a parameter, termed an adjustment parameter, that may affect energy consumption by the associated customer. The results of the benchmark run include energy consumption values for individual customer profiles in the segment, an average energy consumption value for the entire segment of customer profiles, and also adjustment energy consumption value. The adjustment energy consumption value reflects the portion of energy consumption by a particular customer attributed to the difference between the average value of the adjustment parameter for the segment and the value of the adjustment parameter for the particular customer profile.

Abstract: A method and system to determine customer segmentation based on energy consumption patterns is provided. An example system includes a communications module, a clustering module, and a matching module. The communications module obtains energy consumption data in the form of a plurality of value days. The clustering module groups the value days associated with a certain period of time into a set of clusters. The matching module identifies a customer profile as associated with a cluster from the set of clusters based on results of examining value days associated with the customer profile.

Abstract: A benchmarking computing application is configured to create a benchmark run for a segment of customer profiles. Customer profiles from the segment include a parameter, termed an adjustment parameter, that may affect energy consumption by the associated customer. The results of the benchmark run include energy consumption values for individual customer profiles in the segment, an average energy consumption value for the entire segment of customer profiles, and also adjustment energy consumption value. The adjustment energy consumption value reflects the portion of energy consumption by a particular customer attributed to the difference between the average value of the adjustment parameter for the segment and the value of the adjustment parameter for the particular customer profile.

Abstract: The invention relates to a data bank interrogation system, wherein two or more data bank tables are linked by means of a common key or several keys which are respectively common to at least two data bank tables. In an analysis query and a selection of data sets in the first data bank, a selection of data sets is determined in the second data bank corresponding to the selection according to the common key and the analysis query is answered using the thus selected data sets in the second data bank.

Abstract: The invention relates to a data base query system which is characterized in that once the database query is drawn up, a compressed image of the database to be queried is queried in accordance with the database query. Depending on the result of the query of the compressed image an inspection is made whether the result is sufficient and if the result is not sufficient, the database itself is queried in accordance with the database query.

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.

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.

Abstract: The invention relates to analysis of user data organized according to a database structure, such as customer data or product data in a company.

Abstract: The invention relates to a method and a system for detecting at least one partial model of a model pertaining to a system. A state of the system is described by state variables. At least on e of the state variables is a discrete state variable. Several value sets of the state variables are detected. A probability distribution for the state variables is detected by using the sets. The partial model of the system is detected using the sets and the probability distribution of the state variables and a statistical learning method. The partial model describes the system under the condition of the probability distribution for the state variables.

Abstract: 09 A first statistical image is formed for the first database whereby representing statistical correlations of the data elements contained in the first database. Afterwards, the first statistical image is stored in a server computer and transmitted from this server computer over a communications network to a client computer. The received first statistical image is processed by the client computer.

Abstract: For inference in a statistical model, or in a clustering model the formation of the result bitches formed from the terms of the association function or a conditional probability tables, of using the normal procedures, but as soon as the first zero occurs in the associated factors or a weight of zero has been determined for a cluster in the first steps, enabling the further calculation of the a posteriori weight to be aborted. In the case in which in an iterative learning process (e.g. an EM learning process) a cluster for a specific data point is assigned a weight of zero, this cluster will also be given the weight of zero for this data point for all further learning steps and therefore must also no longer be taken into consideration in all further learning steps. Useful data structures for buffering clusters or states of a variable which are still allowed from one learning step to the next are specified. This guarantees a meaningful removal of processing of irrelevant parameters and data.

Abstract: The invention relates to a method for the automatic, software-driven statistical evaluation of large amounts of data that is to be assigned to statistical variables in a database. Said method is characterised in that a statistical model, which approximately describes the relative frequencies of the states of the variables and the statistical dependencies between said states, is learnt and is used to determine the approximate relative frequencies of states of the variables, in addition to the approximate relative frequencies belonging to predeterminable relative frequencies of states of the variables and expectation values of the states of variables dependent thereon.

Abstract: The invention relates to a method and an arrangement for detecting at least one partial model of a model pertaining to a system. A state of the system is described by state variables. At least one of said state variables is a discrete state variable. Several value sets of the state variables are detected. A probability distribution for the state variables is detected by means of said sets. The partial model of the system is detected using the sets and the probability distribution of the state variables and a statistical learning method. The partial model describes the system under the condition of the probability distribution for the state variables.

Abstract: The photoreceptor arrangement has at least two photoreceptors (P) with which light rays are registered and converted into electrical signals. The photoreceptors (P) are coupled to one another, whereby the couplings (K) of the photoreceptors (P) are couplings.