Abstract: Indexes having local features are automatically selected from sensor data of a plurality of sensors. Sensor data of the plurality of sensors, each associated with the plurality of indexes, is partitioned into a plurality of blocks. A principal component analysis is applied to the sensor data of each of the partitioned blocks and a plurality of principal components are extracted from each of the blocks. A migration distance evaluation unit extracts, from two different blocks, two principal components that form a principal component pair, and calculates a migration distance between each of the principal components regarding the extracted principal component pair. A migration factor index detection unit detects, as a migration factor index, an index among the plurality of indexes configuring the principal components having a large migration distance among the migration distances between each of the principal components calculated by the migration distance evaluation unit.

Abstract: An analysis support apparatus includes: a feature interpreting section extracting a feature from a target analysis model generated from vibration data and classifying the feature into an abnormality determination feature, or a feature representing a vibration and enabling determining whether abnormality occurs; an abnormality determination range creating section identifying a range being determined to be abnormal of the abnormality determination feature of the target analysis model as an abnormality determination range; a similar model selecting section calculating an overlapping degree indicating how much the abnormality determination ranges of the target analysis model and one or more predetermined reference analysis models overlap and selecting, based on the overlapping degree, a similar analysis model similar to the target analysis model from the reference analysis models; and an abnormality range difference calculating section extracting a difference between the abnormality determination ranges of the ta

Abstract: The transferability determination apparatus includes: a data input unit which receives the input of first static feature data and first observation data related to a transfer source task; a static feature information modeling unit configured to generate a static feature model using the first static feature data as an objective variable and the feature values related to the first observation data as an explanatory variable; a transfer source data selecting unit configured to receive second static feature data of the transfer destination task and select first static feature data; a data extension unit configured to receive second observation data of the transfer destination task and calculate extended observation data on the basis of the second observation data and the static feature model; and a transfer source model evaluation unit configured to calculate a generalization error of a prediction result obtained by inputting the extended observation data to the analysis model.

Abstract: A CPU of a data integration server detects first rare words whose number existing as words relating to configurations of tables of a factory data model is equal to or smaller than a predetermined number, detects second rare words whose number existing as words relating to configurations of tables in a common data model is equal to or smaller than a predetermined number, determines whether or not determination conditions for determining that a second column included in the common data model is a synonymous column candidate of a first column of the factory data model are satisfied, and, in the case where the determination conditions are satisfied, selects the second column as the synonymous column candidate of the first column. The determination conditions include a condition that one of the first rare words around the first column matches one of the second rare words around the second column.

Abstract: An analysis support apparatus includes: a feature interpreting section extracting a feature from a target analysis model generated from vibration data and classifying the feature into an abnormality determination feature, or a feature representing a vibration and enabling determining whether abnormality occurs; an abnormality determination range creating section identifying a range being determined to be abnormal of the abnormality determination feature of the target analysis model as an abnormality determination range; a similar model selecting section calculating an overlapping degree indicating how much the abnormality determination ranges of the target analysis model and one or more predetermined reference analysis models overlap and selecting, based on the overlapping degree, a similar analysis model similar to the target analysis model from the reference analysis models; and an abnormality range difference calculating section extracting a difference between the abnormality determination ranges of the ta

Abstract: Indexes having local features are automatically selected from sensor data of a plurality of sensors. Sensor data of the plurality of sensors, each associated with the plurality of indexes, is partitioned into a plurality of blocks. A principal component analysis is applied to the sensor data of each of the partitioned blocks and a plurality of principal components are extracted from each of the blocks. A migration distance evaluation unit extracts, from two different blocks, two principal components that form a principal component pair, and calculates a migration distance between each of the principal components regarding the extracted principal component pair. A migration factor index detection unit detects, as a migration factor index, an index among the plurality of indexes configuring the principal components having a large migration distance among the migration distances between each of the principal components calculated by the migration distance evaluation unit.

Abstract: A CPU of a data integration server detects first rare words whose number existing as words relating to configurations of tables of a factory data model is equal to or smaller than a predetermined number, detects second rare words whose number existing as words relating to configurations of tables in a common data model is equal to or smaller than a predetermined number, determines whether or not determination conditions for determining that a second column included in the common data model is a synonymous column candidate of a first column of the factory data model are satisfied, and, in the case where the determination conditions are satisfied, selects the second column as the synonymous column candidate of the first column. The determination conditions include a condition that one of the first rare words around the first column matches one of the second rare words around the second column.