Abstract: A computer-implemented method, computer system, and computer program product for measuring a quality of a chatbot response. The present invention may include receiving one or more classifications, receiving a set of questions in a chatbot to be analyzed, filtering any question from the received set of questions that is not related to an area of expertise of the chatbot, matching at least two questions from the received set of questions to each other, and applying at least one of the one or more classifications to the at least two matched questions. The one or more classifications may be based on a similarity of words and synonyms used in the at least two matched questions. The one or more classifications may be based on a similarity of intents of the at least two matched questions.

Abstract: The invention relates to a sensor system. The sensor system comprises sensor devices. The sensor devices are configured for exchanging data via a network connection. Each of the sensor devices is configured to measure sensor data wherein each of the sensor devices comprises a local processor and a local memory coupled to the local processor. Each local memory comprises a local data repository. The local memory comprises local instructions which, when executed by the local processor, cause the local processor to: repeatedly store the sensor data in the local data repository as a data file, wherein each data file stored has a unique name; and repeatedly synchronize with the local data repository of the other of the sensor devices, wherein each local data repository maintains a complete copy of the data file of each of the sensor devices after synchronization.

Abstract: A method for calculation of at least one software usage metric is disclosed, the software usage metric describing a plurality of software products installed in a computing infrastructure including a plurality of computing machines organized into a plurality of computing groups. The method includes installing a first set of software products in each computing machine in a first computing group of a plurality of computing groups, scanning one computing machine in the first computing group to discover that the first set of software products are installed thereon; and calculating an overall usage metric for the first computing group based on a number of computing machines belonging to the first computing group and the discovered first set of software products.

Abstract: Assigning a given computer to a computer group of a set of computer groups. Software components installed on the given computer are scanned, resulting in a list of discovered software components of the given computer. In addition, for each computer group of the set of computer groups, a list of software components most frequently installed on the computers of the computer group is obtained. For each computer group, the list is compared with the list of discovered software components and, based on the comparison, a likelihood that the given computer belongs to the computer group is computed. In case only one of the likelihoods exceeds a threshold, the given computer is assigned to the respective computer group for which the likelihood exceeds the threshold.

Abstract: A method for calculation of at least one software usage metric is disclosed, the software usage metric describing a plurality of software products installed in a computing infrastructure including a plurality of computing machines organized into a plurality of computing groups. The method includes installing a first set of software products in each computing machine in a first computing group of a plurality of computing groups, scanning one computing machine in the first computing group to discover that the first set of software products are installed thereon; and calculating an overall usage metric for the first computing group based on a number of computing machines belonging to the first computing group and the discovered first set of software products.

Abstract: The invention relates to a sensor system. The sensor system comprises sensor devices. The sensor devices are configured for exchanging data via a network connection. Each of the sensor devices is configured to measure sensor data wherein each of the sensor devices comprises a local processor and a local memory coupled to the local processor. Each local memory comprises a local data repository. The local memory comprises local instructions which, when executed by the local processor, cause the local processor to: repeatedly store the sensor data in the local data repository as a data file, wherein each data file stored has a unique name; and repeatedly synchronize with the local data repository of the other of the sensor devices, wherein each local data repository maintains a complete copy of the data file of each of the sensor devices after synchronization.

Abstract: A method and system are provided for performing an extract-transform-load (ETL) process. The method includes collecting load information about a volume and a complexity of raw data to be processed during the ETL process. The method further includes receiving an expected completion time of the ETL process and execution information about (i) hardware resources and (ii) an influence of the hardware resource on an execution time of the ETL process. The method also includes calculating resources for a distributed processing software infrastructure to be used to perform the ETL process, by applying a statistical method to the load information, expected completion time, and execution information. The method additionally includes dynamically assigning cloud resources corresponding to and based on the calculated resources, in accordance with the expected completion time. The method further includes performing the ETL process on the raw data using the assigned cloud resources and storing ETL process results.

Abstract: An approach for updating of a repetition frequency of a system scan operation. The approach calculates values of asset management factors based on results of the asset management factors from a current and a previously performed system scan operation. Groups of the computer systems are treated as node equivalents, and the asset management factors are treated as synapse equivalents of the node equivalents. The approach also feeds values of the factors and weighing values as input for determining an update value for the repetition frequency as output. The weighing value is adaptable via the learning capability of the neural network equivalent. Finally, the repetition frequency is updated using the update value by an activation function.

Abstract: Assigning a given computer to a computer group of a set of computer groups. Software components installed on the given computer are scanned, resulting in a list of discovered software components of the given computer. In addition, for each computer group of the set of computer groups, a list of software components most frequently installed on the computers of the computer group is obtained. For each computer group, the list is compared with the list of discovered software components and, based on the comparison, a likelihood that the given computer belongs to the computer group is computed. In case only one of the likelihoods exceeds a threshold, the given computer is assigned to the respective computer group for which the likelihood exceeds the threshold.

Abstract: Assigning a given computer to a computer group of a set of computer groups. Software components installed on the given computer are scanned, resulting in a list of discovered software components of the given computer. In addition, for each computer group of the set of computer groups, a list of software components most frequently installed on the computers of the computer group is obtained. For each computer group, the list is compared with the list of discovered software components and, based on the comparison, a likelihood that the given computer belongs to the computer group is computed. In case only one of the likelihoods exceeds a threshold, the given computer is assigned to the respective computer group for which the likelihood exceeds the threshold.

Abstract: A method and system are provided for performing an extract-transform-load (ETL) process. The method includes collecting load information about a volume and a complexity of raw data to be processed during the ETL process. The method further includes receiving an expected completion time of the ETL process and execution information about (i) hardware resources and (ii) an influence of the hardware resource on an execution time of the ETL process. The method also includes calculating resources for a distributed processing software infrastructure to be used to perform the ETL process, by applying a statistical method to the load information, expected completion time, and execution information. The method additionally includes dynamically assigning cloud resources corresponding to and based on the calculated resources, in accordance with the expected completion time. The method further includes performing the ETL process on the raw data using the assigned cloud resources and storing ETL process results.

Abstract: An approach for updating of a repetition frequency of a system scan operation. The approach calculates values of asset management factors based on results of the asset management factors from a current and a previously performed system scan operation. Groups of the computer systems are treated as node equivalents, and the asset management factors are treated as synapse equivalents of the node equivalents. The approach also feeds values of the factors and weighing values as input for determining an update value for the repetition frequency as output. The weighing value is adaptable via the learning capability of the neural network equivalent. Finally, the repetition frequency is updated using the update value by an activation function.

Abstract: Assigning a given computer to a computer group of a set of computer groups. Software components installed on the given computer are scanned, resulting in a list of discovered software components of the given computer. In addition, for each computer group of the set of computer groups, a list of software components most frequently installed on the computers of the computer group is obtained. For each computer group, the list is compared with the list of discovered software components and, based on the comparison, a likelihood that the given computer belongs to the computer group is computed. In case only one of the likelihoods exceeds a threshold, the given computer is assigned to the respective computer group for which the likelihood exceeds the threshold.