Abstract: A system and method for real-time operational predictive scoring of components and services of an information technology system (ITS) for forecasting and assessing performance of the components of the ITS are provided. A data pipeline is configured to collect and store, in real-time, multiple time series signals corresponding to health, performance, and functionality of each of the components of the ITS. An operational predictive score (OPS) engine of a scoring module calculates an OPS for each of the time series signals. An OPS roll-up module of the scoring module calculates an OPS for each of the components and services in the ITS by aggregating the OPS for the individual time series signals. An alerting engine to notify operational issues and provide root cause analysis using OPS score decomposition. A visualization layer for OPS based analytics.

Abstract: There is provision of an operation support system for supporting an operation of a demand-supply system including a resource supplying facility and a resource demander facility. The operation support system receives an input of a demand-supply system model of the demand-supply system, facility-capacity information of the resource supplying facility, and analysis condition information defining a plurality of variables and an external variable of the demand-supply system, to generate a correlation logical formula representing a relation among the plurality of variables and the external variable.

Abstract: A device may receive customer data, and may identify unique communication channels associated with the customer data. The device may determine, based on the customer data, an optimal order for a Markov chain model, and may determine a model accuracy of the Markov chain model based on the optimal order. The device may transform transitions in the Markov chain model, based on the customer data, to generate transformed transitions, and may process the customer data, with a multi-level indexing model and based on the unique communication channels and the transformed transitions, to generate sparse matrices. The device may determine removal effects and steady state values for the sparse matrices, and may determine attribution weights for the unique communication channels based on the Markov chain model with the optimal order, the removal effects, and the steady state values. The device may perform actions based on the attribution weights.

Abstract: Systems, methods and products for determining a consumer-facing price using a demand model that is generated based on historical transactional data. One embodiment comprises a method implemented in a pricing module of an automotive data processing system. Data that identifies a consumer (or consumer group) and a vehicle type are received and the demand model is accessed to generate a payment corresponding to the attributes of the consumer and the attributes of the vehicle type. The demand model may be implemented in a machine learning engine that maintains a set of weights ? used in a predictive demand function. The weights are adjusted by the machine learning engine to minimize a loss function which measures deviation of demand estimated by the predictive demand function from the demand indicated by a set of historical transaction data.

Abstract: A method identifies a plurality of product categories by applying a categorization model to product data that includes product names and product descriptions. The product data is obtained from internal sources and external sources. The method creates a knowledge graph for a plurality of business entities from inputs that include the plurality of product categories, published data that includes merchant categorizations, and geographical data. The method generates cohort clusters of similar business entities by applying a clustering algorithm to the knowledge graph. The method uses the cohort clusters to learn a first extrapolation model for predicting inventory. And the method provides access to the first extrapolation model to predict inventory level at a specific point in time. The access is provided via a merchant application that runs on an online merchant platform.

Abstract: A system and method for optimization of on-demand microtransit is provided. A list of possible stops of one or more vehicles is maintained. A plurality of requests for transportation are received, each of the requests associated with at least one traveler. Travelers to be transported by one of the vehicles are selected. A set of a minimal number of the stops is selected, the set comprising at least one stop that is within a predefined walking distance of the origin location of each of the selected travelers and at least one stop within the predefined walking distance of the destination location of each of the selected travelers. Potential routes are identified for the vehicle that include the stops in the set. The potential routes are evaluated using a plurality of constraints and one of the routes is selected for fulfilling the requests of the selected travelers.

Abstract: A system and method for measuring a customer experience across a plurality of business channels in an organization has been provided. The system is configured to provide insight into customer experience across business channels from data generated via social media. The social media data of the customer pertaining to the organization is acquired and cleansed to obtain the unstructured data. Using linguistic and behavioral analysis of unstructured social data, the system enables an organization to understand customer experience by a plurality of channel to market, including the relationship of the channels on customer experience. Finally, it was concluded that whether the customer expectations are matched, missed or exceeded with the organization.

Abstract: The invention includes various embodiments of apparatus and methods for human-machine visualization interfaces and processes for providing actionable information on elements of networks, networks, and systems of networks ability to support one or more organizational elements, missions associated with the one or more organizational elements, and capabilities of the organizational elements.

Abstract: The disclosed embodiments illustrate methods and systems for processing customer data to predict behavioral characteristics of a customer in a physical store. The method includes receiving customer data, pertaining to a shopping episode, of the customer from one or more sensing devices. The method further includes segmenting the shopping episode into one or more segments of time durations. The method further includes categorizing each of the one or more segments of time durations into one of an in-aisle category and a non-aisle category. The method further includes identifying hand-related actions of the customer based on the received customer data. The method further includes determining a likelihood of occurrence of an action-related category based on at least the identified one or more hand-related actions. The method further includes predicting the behavioral characteristics of the customer based on at least the determined likelihood of occurrence of the action-related category.

Abstract: An approach is provided for rearranging assignments of workers to teams in an agile environment. Project requirements are collected and skills of workers are determined. Based on the project requirements and the skills, an initial arrangement of assignments of the workers to the teams is generated. During project execution, data from sensors worker locations and content of communications among the workers are obtained. The data from the sensors identifies workers who are sitting on chairs. Based on the data from the sensors, worker locations, and the content of the communications, a weighted graph is generated that represents the workers, strengths of relationships among the workers, and strengths of relationships between the workers and the teams. Based on the weighted graph, an optimal rearrangement of the assignments of the workers to the teams is automatically and dynamically generated. The optimal rearrangement specifies a match between the skills and the project requirements.