Abstract: A system for determining a preference index comprises a transactions database storing transaction data indicative of a plurality of transactions carried out over a payment network, the transaction data comprising, for respective transactions, payment card identifiers, merchant identifiers, merchant category codes, issuer identifiers, payment device types, geographical locations, transaction amounts, transaction times and transaction dates. The system also comprises a transaction indexing component in communication with the transactions database, the transaction indexing component being configured to generate a database query to query the transactions database to retrieve transactions for a plurality of payment cards within a predetermined time window; and a preference index component.

Abstract: The present disclosure discloses system and method for providing perceptually efficient visualization of rules and exceptions mined from dataset. Further, parsing is performed on data-attributes associated with the rules. The data-attributes may include antecedents, consequents, ranges of the antecedents, syntax and statistics of the rules and exceptions. The visualization scheme of present disclosure present an overview first, allows semantic zooming, and then shows details on demand. Further, data attributes of the rules are mapped with visual attributes of graphical elements such as shape, color, opacity to create the perceptually efficient visualization of the rules and exceptions. Initially, the visualization shows main rule highlighting the exceptions associated and properties of the exceptions. Further, a semantic zoom slider is provided for allowing a user to navigate through different exception levels of the exception.

Abstract: System and method for visual Bayesian data fusion are disclosed. In an example, a plurality of datasets associated with a topic are obtained from a data lake. Each of the plurality of datasets include information corresponding to various attributes of the topic. Further, the plurality of datasets are joined to obtain a joined dataset. Furthermore, distribution associated with a target attribute is predicted using Bayesian modeling by selecting a plurality of attributes (k) based on mutual information with the target attribute in the joined dataset, learning a minimum spanning tree based Bayesian structure using the selected attributes and the target attribute, learning conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; and predicting the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating visual Bayesian data fusion.

Abstract: A method and system is provided for analyzing temporal text data. Particularly, the present application provides a method and system for analyzing temporal text data, comprises taking temporal text data as an input from voluminous data sources; implementing text analysis on the input data for information extraction and determination of top concepts; sending the analyzed text to a shared file system for storage purpose; clustering the analyzed text as per frequency distribution of top users, concepts and tag clouds and presenting the results in the form of a visualization dashboard.

Abstract: This disclosure relates generally to multi-sensor visual analytics, and more particularly to method and system for multi-sensor visual analytics using machine-learning models. In one embodiment, a method for multi-sensor visual analytics includes acquiring sensor data associated with a plurality of sensors for a plurality of days of operation. A plurality of multi-dimensional histograms, having operational profiles of the plurality of sensors are computed from the sensor data. The plurality of multi-dimensional histograms are monitored, and a plurality of multi-sensor patterns are obtained from the plurality of multi-dimensional histograms. The plurality of multi-sensor patterns are indicative of one or more properties of a plurality of sensor-clusters of the plurality of sensors. One or more visual analytical tasks are performed by processing the plurality of multi-sensor patterns using at least one machine-learning model.

Abstract: A computer implemented method of operating a loyalty program using a computer system is provided. The method includes transmitting to communication devices associated with a corresponding plurality of consumers, who are members of the loyalty program, information describing at least one merchant, wherein the at least one merchant is a merchant that does not currently offer products in return for loyalty points of the loyalty program. The method further includes receiving, from the communication devices, selection data characterizing selections from among the at least one merchant. The method further includes collating the selection data to identify the at least one merchant, wherein the at least one merchant was selected by a number of the consumers that meets a criterion, the criterion indicating that the at least one merchant has been selected by a large number of the consumers.

Abstract: A consumer who is a member of a computer-implemented loyalty program and who is associated with a first currency, is permitted to redeem loyalty points in exchange for a payment in a second currency which is different from the first currency. The payment may be made in the form of pre-paid physical or virtual payment card, or by a credit to an account held by a mobile wallet server. The consumer is sent information about products which the consumer can purchase using the second currency.

Abstract: A computer-implemented method and computer system are proposed for assisting a consumer associated with a plurality of loyalty programs, and who wishes to obtain a reward using loyalty points. The consumer interacts with a computer system with access to a database containing information about the loyalty programs with which the consumer is associated and the number of points the consumer has accumulated in each program, and at least one reward database storing information relating to rewards on offer using the loyalty programs. The consumer specifies one or more parameters of the desired reward, and the computer system uses the databases to make at least one recommendation of which of the loyalty programs to use to obtain a reward with the specified parameters.

Abstract: System and method for visual Bayesian data fusion are disclosed. In an example, a plurality of datasets associated with a topic are obtained from a data lake. Each of the plurality of datasets include information corresponding to various attributes of the topic. Further, the plurality of datasets are joined to obtain a joined dataset. Furthermore, distribution associated with a target attribute is predicted using Bayesian modeling by selecting a plurality of attributes (k) based on mutual information with the target attribute in the joined dataset, learning a minimum spanning tree based Bayesian structure using the selected attributes and the target attribute, learning conditional probabilistic tables at each node of the minimum spanning tree based Bayesian structure; and predicting the distribution associated with the target attribute by querying the conditional probabilistic tables, thereby facilitating visual Bayesian data fusion.

Abstract: In an embodiment, an apparatus for generating account creation information is provided. The apparatus comprises: a computer processor and a data storage device, the data storage device having a look-up module; a value determination module; and an account creation information generation module comprising non-transitory instructions operative by the processor to: receive an indication of a travel document of a passenger; look up passenger details of the passenger, in a passenger information database coupled to the apparatus, using the indication of the travel document of the passenger; receive an indication of a currency deposit by the passenger; determine a value from the indication of the currency deposit; and generate account creation information, the account creation information comprising an indication of the passenger details of the passenger and an indication of the value.

Abstract: This disclosure relates generally to multi-sensor visual analytics, and more particularly to method and system for multi-sensor visual analytics using machine-learning models. In one embodiment, a method for multi-sensor visual analytics includes acquiring sensor data associated with a plurality of sensors for a plurality of days of operation. A plurality of multi-dimensional histograms, having operational profiles of the plurality of sensors are computed from the sensor data. The plurality of multi-dimensional histograms are monitored, and a plurality of multi-sensor patterns are obtained from the plurality of multi-dimensional histograms. The plurality of multi-sensor patterns are indicative of one or more properties of a plurality of sensor-clusters of the plurality of sensors. One or more visual analytical tasks are performed by processing the plurality of multi-sensor patterns using at least one machine-learning model.

Abstract: A computer implemented method of identifying customer segments from transaction data is provided. The method includes receiving transaction data that includes transaction indications. Each transaction indication includes an indication of a transaction date and a cardholder identifier. The method also includes classifying spending behavior of a cardholder in a first period into classifications from a plurality of classifications and further classifying spending behavior of the cardholder in a second period into classifications from a plurality of classifications. The method also includes comparing the classification of the spend behavior of the cardholder in the first period with the classification of the spend behavior of the cardholder in the second period and associating the cardholder with one of a plurality of customer segments based on a result of the comparison.

Abstract: A method and system is provided for analysing temporal text data. Particularly, the present application provides a method and system for analysing temporal text data, comprises taking temporal text data as an input from voluminous data sources; implementing text analysis on the input data for information extraction and determination of top concepts; sending the analysed text to a shared file system for storage purpose; clustering the analysed text as per frequency distribution of top users, concepts and tag clouds and presenting the results in the form of a visualisation dashboard.

Abstract: A system for determining a preference index comprises a transactions database storing transaction data indicative of a plurality of transactions carried out over a payment network, the transaction data comprising, for respective transactions, payment card identifiers, merchant identifiers, merchant category codes, issuer identifiers, payment device types, geographical locations, transaction amounts, transaction times and transaction dates. The system also comprises a transaction indexing component in communication with the transactions database, the transaction indexing component being configured to generate a database query to query the transactions database to retrieve transactions for a plurality of payment cards within a predetermined time window; and a preference index component.

Abstract: The present disclosure discloses system and method for providing perceptually efficient visualization of rules and exceptions mined from dataset. Further, parsing is performed on data-attributes associated with the rules. The data-attributes may include antecedents, consequents, ranges of the antecedents, syntax and statistics of the rules and exceptions. The visualization scheme of present disclosure present an overview first, allows semantic zooming, and then shows details on demand. Further, data attributes of the rules are mapped with visual attributes of graphical elements such as shape, color, opacity to create the perceptually efficient visualization of the rules and exceptions. Initially, the visualization shows main rule highlighting the exceptions associated and properties of the exceptions. Further, a semantic zoom slider is provided for allowing a user to navigate through different exception levels of the exception.