Abstract: A method of providing video feeds from a plurality of cameras to a plurality of screens including determining a plurality of constraints on a centralized processor processing the video feeds, determining a camera semantic classification for each of the plurality of cameras, determining historical events captured by each of the plurality of cameras, and providing at least one video feed to at least one of the screens according to the plurality of constraints on the centralized processor, the camera semantic classifications and the historical events.

Abstract: A hierarchical multi-stage model of asset failure risk for complex heterogeneously distributed physical assets is built. The hierarchical multi-stage model considers heterogeneity of failure patterns for the assets. At least one data stream is analyzed to determine whether the hierarchical multi-stage model needs to be updated due to a change in the failure patterns. If the analysis indicates that the hierarchical multi-stage model needs to be updated, the hierarchical multi-stage model is dynamically updated to obtain an updated hierarchical multi-stage model.

Abstract: Electronic health records of a plurality of patients are received. A risk prediction model for a disease based on the electronic health records of the plurality of patients is created. An electronic health record of an original patient is received. A neighboring group of patients of the plurality of patients is identified, wherein the neighboring group of patients is two or more patients similar to the original patient. An ordering of the two or more patients of the neighboring group of patients is received, wherein the ordering of the two or more patients of the neighboring group of patients is based upon how similar each patient of the two or more patients is to the original patient. The risk prediction model is updated based on the ordering of the two or more patients of the neighboring group of patients.

Abstract: An approach for infrastructure asset management is provided. This approach comprises an end-to-end analytics driven maintenance approach that can take data about physical assets and additional external data, and apply advanced analytics to the data to generate business insight, foresight and planning information. Specifically, this approach uses a maintenance analysis tool, which is configured to: receive data about a set of physical assets of an infrastructure, and analyze the data about the set of physical assets to predict maintenance requirements for each of the set of physical assets. The maintenance analysis tool further comprises an output component configured to generate a maintenance plan based on the predicted maintenance requirements for each of the set of physical assets.

Abstract: An approach for infrastructure asset management is provided. This approach comprises an end-to-end analytics driven maintenance approach that can take data about physical assets and additional external data, and apply advanced analytics to the data to generate business insight, foresight and planning information. Specifically, this approach uses a maintenance analysis tool, which is configured to: receive data about a set of physical assets of an infrastructure, and analyze the data about the set of physical assets to predict maintenance requirements for each of the set of physical assets. The maintenance analysis tool further comprises an output component configured to generate a maintenance plan based on the predicted maintenance requirements for each of the set of physical assets.

Abstract: A hierarchical multi-stage model of asset failure risk for complex heterogeneously distributed physical assets is built. The hierarchical multi-stage model considers heterogeneity of failure patterns for the assets. At least one data stream is analyzed to determine whether the hierarchical multi-stage model needs to be updated due to a change in the failure patterns. If the analysis indicates that the hierarchical multi-stage model needs to be updated, the hierarchical multi-stage model is dynamically updated to obtain an updated hierarchical multi-stage model.

Abstract: A proton exchange membrane fuel cell that includes: a positive electrode; a negative electrode; a polyelectrolyte membrane; and platelet-shaped nanoparticles of gold, platinum, palladium, silver, copper or their alloys or mixtures thereof. The polyelectrolyte membrane includes a sulfonated tetrafluoroethylene based fluoropolymer-copolymer and is disposed between the positive electrode and the negative electrode. The nanoparticles contact the surface of the proton exchange membrane increase the efficiency of the fuel cell by at least 50%.

Abstract: Historical high-spatial-resolution temperature data and dynamic temperature sensor measurement data may be used to predict temperature. A first formulation may be derived based on the historical high-spatial-resolution temperature data for determining a temperature at any point in 3-dimensional space. The dynamic temperature sensor measurement data may be calibrated based on the historical high-spatial-resolution temperature data at a corresponding historical time. Sensor temperature data at a plurality of sensor locations may be predicted for a future time based on the calibrated dynamic temperature sensor measurement data. A three-dimensional temperature spatial distribution associated with the future time may be generated based on the forecasted sensor temperature data and the first formulation.

Abstract: A method and system to perform spatio-temporal prediction are described. The method includes obtaining, based on communication with one or more sources, multi-scale spatial datasets, each of the multi-scale spatial datasets providing a type of information at a corresponding granularity, at least two of the multi-scale spatial datasets providing at least two types of information at different corresponding granularities. The method also includes generating new features for each of the multi-scale spatial datasets, the new features being based on features of each of the multi-scale spatial datasets and spatial relationships between and within the multi-scale spatial datasets. The method further includes selecting, using the processor, features of interest from among the new features, training a predictive model based on the features of interest, and predicting an event based on the predictive model.

Abstract: A method of characterizing relationships among spatio-temporal events and a system to characterize the relationships are described. The method includes receiving information specifying the spatio-temporal events and associated categories from one or more sources. The method also includes building, using a processor, a directed acyclic graph (DAG) indicating a relationship among the categories for each of two or more space lag (SL) and time lag (TL) sets. Each of the two or more SL and TL sets defines a spatio-temporal boundary such that only the spatio-temporal events and the associated categories with (SL,TL)-neighborhoods inside the respective spatio-temporal boundary are considered in building the respective DAG. The respective (SL,TL)-neighborhood of each of the spatio-temporal events is a polygonal shape defined by the respective SL and the respective TL and the respective (SL,TL)-neighborhood of each of the categories is a union of the (SL,TL)-neighborhoods of the associated spatio-temporal events.

Abstract: A system, method and program product are provided for calculating a routing scheme for a set of correspondent banking (CB) transactions. The described system includes: a network modeling system that creates a model of a CB network for a set of banks based on existing relationships among and between the banks; and an optimization engine that generates a CB transaction routing scheme for an inputted set of CB transactions within the CB network, wherein the optimization engine provides an overall maximized economic benefit for executing each of the inputted set of CB transactions based on separately collected cost data, risk data and revenue data.

Abstract: A system, method and program product are provided for evaluating clients in a correspondent banking (CB) network with regard to CB product offerings. The disclosed system includes: a network modeling system that creates a model of a CB network, wherein the model is defined by a set of banks and a set of clients based on existing relationships among and between the banks and clients, and wherein the model is further defined with inputted risk data, expected return data, demand data, and cost data; and an evaluation engine that utilizes the model to generate a clientele evaluation for an inputted CB product offering within the CB network, wherein the evaluation engine determines a potential economic benefit of each client with regard to the inputted CB product offering.

Abstract: An aspect of an online learning system includes collecting data, via a computer processing device, from a plurality of data sources including multiple disparate detectors, the data including at least one of historical alarm data, failures, maintenance records, and environment observations. The data is stored in tables each corresponding to a subject of measurement. The online learning system also includes identifying common fields shared across the tables, merging at least a portion of the data across the tables having the common fields, and creating an integrated data model based on results of the merging.

Abstract: An aspect of product pricing includes classifying customers into groups based on shared, predefined characteristics and financial transactions, and identifying services rendered and available but not rendered. For each customer, a risk associated with a service is estimated; availability and prices of the service by third parties are determined; a price for the service set by the entity is compared with the prices set by the third parties; and a demand for the service of the entity is estimated as a function of the availability and prices of the service by the third parties. For each customer, a probability that the customer will purchase the service is estimated based on the demand, and a price for the service that is customized for the customer is calculated, as a function of the risk, the demand, and the probability of purchase, and in view of a target profit and/or target revenue.

Abstract: An aspect of customer selection processes includes classifying, by a computer processor, customers of an entity into groups based on commonly shared, predefined characteristics among the customers. For each of the groups: services rendered for corresponding customers are identified; for each of the services rendered, a risk relationship and a reward relationship between each of the corresponding customers and the service is determined; and for each of the services rendered, a score that defines a combination of the risk relationship and the reward relationship is calculated. For each of the services rendered by the entity, the corresponding score is applied to a candidate customer having a set of characteristics matching the characteristics of one of the groups, and the service is offered to the candidate customer as a function of the score.

Abstract: Embodiments relate to analytics driven assessment of transactional risk daily limits (TRDLs). Customer data that includes historical transaction data and customer profile data associated with a customer is accessed by a processor. Economic data from an external data source is accessed via a network. A TRDL assessment model is applied, by a processor, to the customer data and the economic data to generate a TRDL for the customer.

Abstract: Embodiments relate to a transactional risk daily limit (TRDL) update alarm. Customer data including historical transaction data and customer profile data is accessed, along with economic data from an external data source. A TRDL alarm analytics model is applied to the customer data and the economic data to predict a number of transactions in a specified time period that are expected to exceed a TRDL. The TRDL alarm analytics model takes into account a payment transaction pattern associated with the customer. A threshold value is compared to the number of transactions in the specified time period that are expected to exceed the TRDL. An increase in the TRDL is requested to be applied at least during the specified time period based on the number of transactions in the specified time period that are expected to exceed the TRDL being greater than the threshold value.

Abstract: An aspect of product pricing includes classifying customers into groups based on shared, predefined characteristics and financial transactions, and identifying services rendered and available but not rendered. For each customer, a risk associated with a service is estimated; availability and prices of the service by third parties are determined; a price for the service set by the entity is compared with the prices set by the third parties; and a demand for the service of the entity is estimated as a function of the availability and prices of the service by the third parties. For each customer, a probability that the customer will purchase the service is estimated based on the demand, and a price for the service that is customized for the customer is calculated, as a function of the risk, the demand, and the probability of purchase, and in view of a target profit and/or target revenue.

Abstract: Embodiments relate to privacy preserving content analysis. A recoverable hash operation is performed on text information to produce hashed text information in a business-to-business system. The business-to-business system includes a business-to-business transaction gateway coupled to a plurality of enterprise computer systems. A non-recoverable hash operation is performed on numerical information to produce hashed numerical information in the business-to-business system. The hashed text information and the hashed numerical information are provided from the business-to-business transaction gateway to an analytics engine to perform encrypted content analysis. The text information and the numerical information are provided from one of the enterprise computer systems as a producer system to another of the enterprise computer systems as a consumer system through the business-to-business transaction gateway.

Abstract: Embodiments relate to analytics-driven automated reconciliation of financial transactions. External information is correlated with a plurality of financial transaction reconciliation exceptions associated with a sequence of financial transactions over a period of time. A plurality of causal factors is identified from the external information associated with a pattern of the financial transaction reconciliation exceptions. A plurality of more recent financial transactions is monitored for the causal factors. An exception prediction alert is issued based on identifying the causal factors in the more recent financial transactions prior to detecting a new financial transaction reconciliation exception associated with the more recent financial transactions.