Abstract: Assistance apparatus for automatically identifying failure types of a technical system is provided including at least one processor configured to determine for each sensor data a set of specific temporal courses of first time series of the sensor data of the sensor and assign a symbolic representation to each of the different specific temporal courses, provide at least one failure pattern, obtain more than one monitored time series of sensor data of the technical system, each of them divided into a sequence of time segments, and automatically assign to each time segment a symbolic representations according to the temporal course of the sensor data in the time segment, calculate a similarity measure for the set of symbolic representations of a selected time interval, determine a ranking of the failure pattern depending on decreasing values of the calculated similarity measure, and output the ranking.

Abstract: A method for checking samples for defects is provided, in which image data of the samples are recorded and classified into predeterminable defect categories by a defect detection algorithm, and the samples classified into a defect category are represented in a multi-dimensional confusion matrix as a classification result of the defect detection algorithm, characterized in that—miniature images which reproduce the image data are assigned according to the classified defect categories of the image data to segments of the confusion matrix which represent the defect categories, and these miniature images are displayed visually, —the miniature image is assigned by an interaction with a user or a software robot to a different segment from the assigned segment of the confusion matrix, and is either provided as training image data for the defect detection algorithm or is output as training image data for the defect detection algorithm.

Abstract: A method for the computer-aided processing of operating data relating to a technical system, wherein the operating data have been captured during operation of the technical system in a predefined operating interval and are stored as digital data in a memory is provided. A pair of first state variables and one or more second state variables with corresponding state values of the technical system at the particular operating time are extracted from the operating data by the method. A two-dimensional representation is generated on a display of a user interface by these state variables. Bar charts for different state ranges of the pairs of first state variables are generated in this representation on the basis of the evaluation of a threshold value criterion, wherein the bar charts represent, inter alia, how many states of the technical system within the particular state range can be classified as critical.

Abstract: A method for parameterizing an anomaly detection method, which takes a multiplicity of sensor data points as a basis for performing a density-based cluster method, including a) mapping each sensor data point in a data space into a pixel data point in a pixel space, b) reproducing at least one operation of the density-based cluster method in the data space by means of at least one pixel operation in the pixel space, c) receiving at least one parameter value for each parameter of the density-based cluster method, d) applying the at least one pixel operation in accordance with the parameter values to the pixel data points e) outputting a cluster result in visual form in the pixel space, and f) providing the received parameter values for the anomaly detection method, and an assistance apparatus for parameterizing an anomaly detection apparatus that performs the anomaly detection method.

Abstract: A method for the computer-aided processing of operating data relating to a technical system, wherein the operating data have been captured during operation of the technical system in a predefined operating interval and are stored as digital data in a memoryis provided. A pair of first state variables and one or more second state variables with corresponding state values of the technical system at the particular operating time are extracted from the operating data by the method. A two-dimensional representation is generated on a display of a user interface by these state variables. Bar charts for different state ranges of the pairs of first state variables are generated in this representation on the basis of the evaluation of a threshold value criterion, wherein the bar charts represent, inter alia, how many states of the technical system within the particular state range can be classified as critical.

Abstract: Provided is a method for the computer-assisted operation of an electric motor which is exposed to a thermal load as a result of the rotational motion of its rotor during operation. In this method, measured data is received during the operation of the electric motor. One or more temperature values are derived from the measured data. A number of temperature characteristics curves are then forecast with differing restart times for defining a cooling period for reducing the thermal load on the electric motor, wherein the approximated temperature value, which results from the approximated temperature value is used as the specific starting value for a restart in the temperature characteristics curve to be forecast.

Abstract: Provided is a method for the computer-assisted operation of an electric motor which is exposed to a thermal load as a result of the rotational motion of its rotor during operation. In this method, measured data is received during the operation of the electric motor. One or more temperature values are derived from the measured data. A number of temperature characteristics curves are then forecast with differing restart times for defining a cooling period for reducing the thermal load on the electric motor, wherein the approximated temperature value, which results from the approximated temperature value is used as the specific starting value for a restart in the temperature characteristics curve to be forecast.

Abstract: At least one hit in a large volume of data is found using a graphical search pattern. The graphical search pattern is created afresh or modified by a user using a graphical interface. The user may implement complex searches and use a graphical representation of properties and/or correlations for the search in a directed manner. The method may be used, for example, in data mining, in the monitoring of states, or in automated alerting.

Abstract: A method executed by a computer is provided, including the steps of capturing a plurality of datasets, each dataset at least including a time value and an angular value, and placing pixels representing the datasets in a polar coordinate system at a radial distance from a center according to the time value and at an angle from a reference direction according to the angular value.

Abstract: Processing geographical information includes storing geographical information including a map dataset and associated context dataset in a memory. A central window subset based on the map data set and the associated context dataset is extracted from the memory. A frame window subset based on an associated context dataset that is adjacent to the central window subset is extracted from the memory. The central window subset and the frame window subset are transferred to a graphics memory.

Abstract: A method executed by a computer is provided, including the steps of capturing a plurality of datasets, each dataset at least including a time value and an angular value, and placing pixels representing the datasets in a polar coordinate system at a radial distance from a center according to the time value and at an angle from a reference direction according to the angular value.

Abstract: Processing geographical information includes storing geographical information including a map dataset and associated context dataset in a memory. A central window subset based on the map data set and the associated context dataset is extracted from the memory. A frame window subset based on an associated context dataset that is adjacent to the central window subset is extracted from the memory. The central window subset and the frame window subset are transferred to a graphics memory.

Abstract: A device is provided for determining an investment solution. The device includes a global unit being configured to: (1) send an investment indication on an investment amount to a plurality of local units, each local unit being associated with an investment area and being configured to determine a plurality of local investment solutions for the associated investment area based on the investment amount, (2) receive the plurality of local investment solutions from each local unit, and (3) determine a plurality of global solutions based on the received local investment solutions. The device also includes an optimization unit being configured to: (1) display the plurality of global solutions in a diagram together with associated investment effects, (2) receive a user input for adapting the diagram illustrating the plurality of global solutions, and (3) determine an investment solution from the plurality of global solutions based on the adapted diagram.

Abstract: At least one hit in a large volume of data is found using a graphical search pattern. The graphical search pattern is created afresh or modified by a user using a graphical interface. The user may implement complex searches and use a graphical representation of properties and/or correlations for the search in a directed manner. The method may be used, for example, in data mining, in the monitoring of states, or in automated alerting.

Abstract: The invention relates to a method for the computer-assisted identification of a class of VoIP calls of a first type (spam) in a communication network (internet). Said communication network has a plurality (N) of first subscribers (Tn1-1, . . . , Tn1-5) and a plurality (M) of second subscribers (Tn2-1, . . . , Tn2-7), the first and the second subscribers being allocated a definite characteristic (IP address, telephone number, e-mail address) wherein, at least some of the first subscribers (Tn1-1, . . . , Tn1-5) are allocated, respectively, with at least one list (white list, black list) which contains at least one definite characteristic of the second subscriber.