Abstract: The present disclosure provides methods and systems for providing machine learning model service. The method may comprise: (a) generating, by a first computing system, a first output data using a first machine learning model, wherein the first machine learning model is trained on a first training dataset; (b) transmitting the first output data to a second computing system, wherein the first training dataset and the first machine learning model are inaccessible to the second computing system; (c) creating an input data by joining the first output data with a selected set of input features accessible to the second computing system; and (d) generating a second output data using a second machine learning model to process the input data.

Abstract: The present disclosure provides methods and systems for providing machine learning model service. The method may comprise: (a) generating, by a first computing system, a first output data using a first machine learning model, wherein the first machine learning model is trained on a first training dataset; (b) transmitting the first output data to a second computing system, wherein the first training dataset and the first machine learning model are inaccessible to the second computing system; (c) creating an input data by joining the first output data with a selected set of input features accessible to the second computing system; and (d) generating a second output data using a second machine learning model to process the input data.

Abstract: Techniques facilitating interpretable rule generation using loss-preserving transformation are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, an input data set that comprises three data categories. The computer-implemented method can also comprise transforming, by the system, the input data set into a transformed data set. The transformed data set can comprise two data categories determined based on the three data categories. Transforming the input data set can comprise determining a first cost associated with the transformed data set is no greater than a second cost associated with the input data set.

Abstract: Techniques facilitating interpretable rule generation using loss-preserving transformation are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, an input data set that comprises three data categories. The computer-implemented method can also comprise transforming, by the system, the input data set into a transformed data set. The transformed data set can comprise two data categories determined based on the three data categories. Transforming the input data set can comprise determining a first cost associated with the transformed data set is no greater than a second cost associated with the input data set.

Abstract: A system, method and computer program product for determining a target population to be subject to a promotion or offer of goods/services. The target population is determined that strongly prefers a given promotion, while also making sure the target population represents a sizable number of consumers such that profits may be maximized. The system provides an output solution listing available promotion options and one or more corresponding target groups based on solving an optimization problem that incorporates, prior obtained most important customer features for each promotion using historical promotion data and statistics measures. The system may automatically initiate a promotion offering to each of said customers by communicating the promotion to the members of the targeted group of people, wherein a percentage of future transactions to which the promotion is offered is expected to exceed a threshold level.

Abstract: User information may be received and a market segment associated with the user may be received. A personalized or individualized offer may be determined based on the user information, the personalized offer determined based on a product offeror's goal with respect to the user at a given time. The market segment offer and the personalized offer may be blended to determine a recommended personalized offering for the user at the given time, e.g., given a company's tactical, strategic and lifetime goals and values for that user.

Abstract: Techniques facilitating interpretable rule generation using loss-preserving transformation are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, an input data set that comprises three data categories. The computer-implemented method can also comprise transforming, by the system, the input data set into a transformed data set. The transformed data set can comprise two data categories determined based on the three data categories. Transforming the input data set can comprise determining a first cost associated with the transformed data set is no greater than a second cost associated with the input data set.

Abstract: Techniques facilitating interpretable rule generation using loss-preserving transformation are provided. In one example, a computer-implemented method can comprise evaluating, by a system operatively coupled to a processor, an input data set that comprises three data categories. The computer-implemented method can also comprise transforming, by the system, the input data set into a transformed data set. The transformed data set can comprise two data categories determined based on the three data categories. Transforming the input data set can comprise determining a first cost associated with the transformed data set is no greater than a second cost associated with the input data set.

Abstract: A hardware processor coupled to a transaction data database and a customer data database receives transaction data and customer data, and executes a predictive modeling algorithm that determines customer features that characterize purchasing behavior from the customer data and the transaction data. The hardware processor executes a clustering algorithm that segments customers into multiple groups based on the customer features. A likelihood function is constructed based on a selected demand model, the transaction data and customer segment information determined from the multiple groups, the likelihood function determined based on probability that each sales transaction belongs to a segment conditioned on a paid price. A model estimator computes parameters that maximize the likelihood function.

Abstract: User information may be received and a market segment associated with the user may be received. A personalized or individualized offer may be determined based on the user information, the personalized offer determined based on a product offeror's goal with respect to the user at a given time. The market segment offer and the personalized offer may be blended to determine a recommended personalized offering for the user at the given time, e.g., given a company's tactical, strategic and lifetime goals and values for that user.

Abstract: Training a machine to learn to offer personalized promotions over a network is provided. A promotion optimization engine may take logit models and their confidence measures, and compute the acceptance probability of each promotion based on the customer and product features. A target promotion may be determined based on an objective function, which jointly considers the acceptance probability and the logit model's confidence level. A cognitive engine receives a user response to the promotion and based on the user response, updates parameters of the logit model and confidence level associated with the logit model. In one aspect, a signal to offer the promotion is transmitted via a communication channel to a user's device, wherein the signal causes the user's device to automatically connect to one or more of the processors to receive the promotion, e.g., when the user's device is online.

Abstract: A system, method and computer program product for determining a target population to be subject to a promotion or offer of goods/services. The target population is determined that strongly prefers a given promotion, while also making sure the target population represents a sizable number of consumers such that profits may be maximized. The system provides an output solution listing available promotion options and one or more corresponding target groups based on solving an optimization problem that incorporates, prior obtained most important customer features for each promotion using historical promotion data and statistics measures. The system may automatically initiate a promotion offering to each of said customers by communicating the promotion to the members of the targeted group of people, wherein a percentage of future transactions to which the promotion is offered is expected to exceed a threshold level.

Abstract: One or more computer processors generate a probability model for a cycle time of a complexity category of a completed project. One or more computer processors determine an overdue risk probability of an active project using the generated probability model. The completed project has a start date and an end date. In addition, the cycle time reflects the time difference between the start date and the end date.

Abstract: Methods, systems, and articles of manufacture for prioritizing proposal development under resource constraints are provided herein.

Abstract: Disclosed are methods and systems for identifying and visualizing the patterns of work transfers for service delivery using financial data. Within a service firm, work is often transferred from one delivery center to another, which results in additional costs and delays in service delivery. To control unnecessary work transfers, sustained patterns of work transfer are identified so that steps can be taken to limit their occurrence. The disclosed methods and systems analyze the relations among the costs transferred from one location to another.

Abstract: An approach for visualizing versions of a hierarchically organized object is provided. A visualization is generated to include first and second versions including information from multiple hierarchical levels of trees representing the versions. The visualization is generated so that the second version highlights a value of a dimension for a labeled path of the tree representing the second version. The highlighted value differs from a corresponding value of a dimension for a labeled path of the tree representing the first version based on a vector space constructed to have multiple dimensions for each labeled path of the trees. A similarity measure between the first and second versions is computed by normalizing a first vector associated with the first version, normalizing a second vector associated with the second version, and determining a product of the normalized first vector and the normalized second vector.

Abstract: Disclosed are methods and systems for identifying and visualizing the patterns of work transfers for service delivery using financial data. Within a service firm, work is often transferred from one delivery center to another, which results in additional costs and delays in service delivery. To control unnecessary work transfers, sustained patterns of work transfer are identified so that steps can be taken to limit their occurrence. The disclosed methods and systems analyze the relations among the costs transferred from one location to another.

Abstract: An approach for visualizing versions of a hierarchically organized object is provided. A visualization is generated to include first and second versions including information from multiple hierarchical levels of trees representing the versions. The visualization is generated so that the second version highlights a value of a dimension for a labeled path of the tree representing the second version. The highlighted value differs from a corresponding value of a dimension for a labeled path of the tree representing the first version based on a vector space constructed to have multiple dimensions for each labeled path of the trees. A similarity measure between the first and second versions is computed by normalizing a first vector associated with the first version, normalizing a second vector associated with the second version, and determining a product of the normalized first vector and the normalized second vector.

Abstract: An approach for visualizing versions of a hierarchically organized object is presented. A measure of similarity is computed between each version and a standard version. Versions having identical hierarchies are clustered into sets of versions. Versions within each of the sets of versions are organized according to the computed similarity measures. A visualization is generated as a plot having first and second dimensions. The first dimension represents similarity between a set of versions and the standard version. The second dimension represents similarity between a version and the standard version. In one embodiment, the visualization includes, in a first area, information from multiple hierarchical levels of the standard version and includes, in a second area, information from only the root node level of the other versions. After receiving an approval of a version in the second area, the approved version may replace the standard version in the first area.

Abstract: An approach for visualizing versions of a hierarchically organized object is presented. A measure of similarity is computed between each version and a standard version. Versions having identical hierarchies are clustered into sets of versions. Versions within each of the sets of versions are organized according to the computed similarity measures. A visualization is generated as a plot having first and second dimensions. The first dimension represents similarity between a set of versions and the standard version. The second dimension represents similarity between a version and the standard version. In one embodiment, the visualization includes, in a first area, information from multiple hierarchical levels of the standard version and includes, in a second area, information from only the root node level of the other versions. After receiving an approval of a version in the second area, the approved version may replace the standard version in the first area.