Abstract:
PROBLEM TO BE SOLVED: To provide a prediction device, a prediction method, and a prediction program, which enable highly accurate prediction of a temporally changing prediction target.SOLUTION: In prediction of a prediction target, a change in the prediction target is predicted on the basis of data on plural items related to the prediction target. That is, for each of the items, a causal effect value representing an SN ratio of the prediction target to each piece of data including the data on the items to an SN ratio of the prediction target to each piece of data other than the data on the items is derived; the intensity of the SN ratio of an integral estimation value to the data on plural items selected sequentially from the item with the largest of the derived causal effect values is calculated for each of the number of items; the number of items is determined based on the SN ratio of the calculated integral estimation value for each of the number of items; items equivalent to the determined number of items is selected sequentially from the item with the largest of the derived causal effect values; and a change in the prediction target is predicted on the basis of the data on the selected items by using a method such as a T method.
Abstract:
Provided are a prediction device, prediction method and prediction program with high prediction accuracy when predicting a prediction target that changes over time. When predicting a prediction target, change of the prediction target is predicted on the basis of data of multiple elements relating to the prediction target. Specifically, this prediction device derives, for each element, a factor effect value which indicates the S/N ratio of the prediction target to the data including the data of said element relative to the S/N ratio of the prediction target to the data excluding the data of said element, calculates for each number of elements, the strength of the S/N ratio of a combined estimate to the data of the selected multiple elements in order from that having the greatest derived factor effect value, determines a number of elements on the basis of the S/N ratios of the combined estimate value for each calculated number of elements, selects the determined number elements in order from that having the largest derived factor effect value, and predicts the change of the prediction target using a method (such as the Taguchi method) on the basis of the data of the selected elements.
Abstract:
PROBLEM TO BE SOLVED: To provide a computer-implemented hierarchical revenue model for managing revenue allocations among derived product developers in a networked system.SOLUTION: The hierarchical revenue model comprises: providing a first revenue value associated with a first digital component; providing a second revenue value associated with a second digital component; and combining the first revenue value with the second revenue value to generate a third revenue value. The third revenue value is associated with the second digital component including at least a portion of the first digital component.
Abstract:
PROBLEM TO BE SOLVED: To provide a means for comparing utility and warranty of services in an information technology (IT) stack. SOLUTION: The method includes steps of: (a) determining, at each layer of the IT stack, a required utility and warranty (RUW) value for each of a set of services, wherein the RUW value represents a desired solution for implementing a business process; (b) determining, at each layer of the IT stack, an available utility and warranty (AUW) value for each of the set of services, wherein the AUW value is a measure of an ability of each of the set of services to satisfy the RUW at each layer of the IT stack; and (c) comparing the RUW value with the AUW value at each layer of the IT stack to thereby determine whether each of the set of services satisfies the desired solution for implementing the business process. COPYRIGHT: (C)2011,JPO&INPIT
Abstract:
PROBLEM TO BE SOLVED: To perform a simulation for an efficient allocation of resources, such as funds and manpower, in a plurality of concurrent projects in order to minimize a delay period and excess of cost. SOLUTION: The simulation is practiced with each project regarded as an agent and the optimized solution is output by using a genetic algorithm. Specifically, a risk factor of each project is input by an input means 120, and the optimal allocation of facilities and labor is determined by an arithmetic means 110 with a delay period and a delay event probability using a delay risk model, time development of production, and minimization of an objective function. COPYRIGHT: (C)2010,JPO&INPIT