Abstract: A visualization system, in the case where a prediction target is expressed by a sum of a plurality of partial prediction targets, includes: a reception unit 81 for receiving designation of a method of compiling the partial prediction targets into a plurality of groups; a learning unit 82 for learning, for at least one of the groups, a model having, as an objective variable, a sum of partial prediction targets included in the group; and a first display control unit 83 for causing a display device to display parameters constituting the model.

Abstract: A visualization system, in the case where a prediction target is expressed by a sum of a plurality of partial prediction targets, includes: a reception unit 81 for receiving designation of a method of compiling the partial prediction targets into a plurality of groups; a learning unit 82 for learning, for at least one of the groups, a model having, as an objective variable, a sum of partial prediction targets included in the group; and a first display control unit 83 for causing a display device to display parameters constituting the model.

Abstract: Error calculation means 81 calculates an error in a demand quantity predicted by a prediction model, and, from a predicted demand quantity during a covered time slot and a predicted demand quantity during a sales permitted period calculated for each product, calculates an error in the predicted demand quantity during the covered time slot and an error in the predicted demand quantity during the sales permitted period. Safety stock quantity calculation means 82 calculates an occurrence probability of the predicted demand quantity during the covered time slot, for each product, and an occurrence probability of the predicted demand quantity during the sales permitted period, for each product, from the errors and calculates a safety stock quantity. Order quantity calculation means 83 calculates an order quantity, from a stock quantity anticipated at a time point of delivery, the predicted demand quantity during the covered time slot, and the safety stock quantity.

Abstract: An information processing system 80 for predicting a prediction target using a predictive model including a variable that influences the prediction target includes a reception unit 81 and a specifying unit 82. The reception unit 81 receives designation of a plurality of prediction targets. The specifying unit 82 specifies, from among the designated plurality of prediction targets, a prediction target for which an element included in a corresponding predictive model shows a different tendency from other prediction targets.

Abstract: An information processing system 80 configured to predict a prediction target specified by a plurality of classifications using a prediction model including a variable that affects the prediction target, includes an accepting unit 81 and an aggregating unit 82. The accepting unit 81 accepts classifications that specify the prediction target. The aggregating unit 82 specifies the prediction target by the accepted classifications and aggregates, for each of the variables, a degree of contribution determined by the prediction model corresponding to that prediction target.

Abstract: Provided is a display system for displaying analytical information that allows a person to easily analyze which term in the estimation equation causes the estimation failure, when the measured value largely deviates from the estimated value. A calculation means (3) uses values of two or more types of attributes used in calculation of an estimated value, and a coefficient of an explanatory variable in an estimation equation used in calculation of the estimated value, to calculate a product of a value of the explanatory variable specified from each of the values of the attributes and the coefficient corresponding to the explanatory variable, for each estimated value. A display means (4) displays a stacked bar graph in which each product calculated by the calculation means (3) and a constant term in the estimation equation are stacked, for each estimated value, and respectively displays a change in the estimated value and a change in a measured value corresponding to the estimated value.

Abstract: Provided is a learning model generation system capable of preventing a decrease in prediction accuracy in a case where the trend of an actual value of a prediction target has changed. The learning model generation means 71 generates a learning model using, as learning data, time series data in which a value of each explanatory variable used in prediction of a prediction target is associated with an actual value of the prediction target. The prediction means 72 calculates a predicted value of the prediction target using the learning model once the value of each explanatory variable is given. The change point determination means 73 determines a change point which is a point in time when a trend of the actual value of the prediction target changed.

Abstract: Disclosed is an information processing apparatus, wherein, upon defining an judgment method of determining the timing for presenting information, an appropriate judgment rule can be defined without any need for specialized knowledge, and without putting in much effort in setup work thereof.

Abstract: In general information processing apparatuses, determination on what sort of operational interface is appropriate to be provided for which application (AP, hereinafter) is left up to the user. An information processing apparatus in an exemplary embodiment of the present invention comprises: an operation-definition storage means for storing a record including a function name, a function ontology and an operation type, with respect to each of a plurality of applications (APs); and an operation modification means for acquiring from the above-mentioned operation-definition storage means a set of records having an identical function ontology (identical ontology set) or a set of identifiers of the records belonging to the identical ontology set, and for replacing an operation type of a record including the above-mentioned identical function ontology of a designated AP with an operation type having a high appearance frequency (high frequency type) in the identical ontology set.

Abstract: Disclosed is an information processing apparatus, wherein, upon defining an judgment method of determining the timing for presenting information, an appropriate judgment rule can be defined without any need for specialized knowledge, and without putting in much effort in setup work thereof.

Abstract: In general information processing apparatuses, determination on what sort of operational interface is appropriate to be provided for which application (AP, hereinafter) is left up to the user. An information processing apparatus in an exemplary embodiment of the present invention comprises: an operation-definition storage means for storing a record including a function name, a function ontology and an operation type, with respect to each of a plurality of applications (APs); and an operation modification means for acquiring from the above-mentioned operation-definition storage means a set of records having an identical function ontology (identical ontology set) or a set of identifiers of the records belonging to the identical ontology set, and for replacing an operation type of a record including the above-mentioned identical function ontology of a designated AP with an operation type having a high appearance frequency (high frequency type) in the identical ontology set.

Abstract: A content storage means stores content information that correlates available pieces of content with tags that are assigned thereto and that represent contexts in advance. A usage log storage means stores information that represents a piece of content that a user used as a usage log. A user context determination means obtains a tag that is assigned to a piece of content and that is contained in the usage log from the content information and determines a context of the user based on the obtained tag.