Abstract: A computer-implemented method includes: generating a plurality of candidate groups based on transaction data associated with each user of a plurality of users, each candidate group of the plurality of candidate groups including a subset of users that conducted transactions with a common merchant within a threshold time; generating a plurality of actual groups based on the plurality of candidate groups and the transaction data associated with each user of the plurality of users, each actual group of the plurality of actual groups including a subset of users grouped together in at least a threshold plurality of candidate groups; and for an actual group of the plurality of actual groups, determining a classification associated with a merchant for the actual group based on transaction data for transactions conducted between the merchant and each user of the actual group.

Abstract: Methods for detecting insider threats are disclosed. A method includes collecting server access data and application access data, based on the server access data and the application access data, determining nearest neighbors of an employee, and based on the nearest neighbors of the employee, determining a peer group of the employee, determining an average rank distance (ARD) of the nearest neighbors based on a ranking of the nearest neighbors in a plurality of time periods, identifying ARD gaps between the nearest neighbors, and generating scores corresponding to the ARD gaps between the nearest neighbors. One or more employees are identified that represent an internal threat to an organization based on the scores corresponding to the ARD gaps.

Abstract: Provided is a method that includes determining cuisine type classifiers based on merchant data, receiving transaction data associated with each payment transaction of a plurality of payment transactions involving a plurality of merchants and a plurality of consumers; determining, with at least one processor, a plurality of cuisine type labels based on a plurality of merchant names associated with the plurality of payment transactions or a portion of the plurality of payment transactions, wherein a cuisine type label identifies a cuisine type provided by a respective merchant of the plurality of merchants; generating a merchant cuisine type model from the transaction data to determine a cuisine type label of a merchant, the merchant cuisine type model based on the transaction data, and makes an inference based on a combination of merchant or consumer features associated with a payment transaction of the plurality of payment transactions; and automatically predicting one or more cuisine type classifications usi

Abstract: Provided is a method that includes determining cuisine type classifiers based on merchant data, receiving transaction data associated with each payment transaction of a plurality of payment transactions involving a plurality of merchants and a plurality of consumers; determining, with at least one processor, a plurality of cuisine type labels based on a plurality of merchant names associated with the plurality of payment transactions or a portion of the plurality of payment transactions, wherein a cuisine type label identifies a cuisine type provided by a respective merchant of the plurality of merchants; generating a merchant cuisine type model from the transaction data to determine a cuisine type label of a merchant, the merchant cuisine type model based on the transaction data, and makes an inference based on a combination of merchant or consumer features associated with a payment transaction of the plurality of payment transactions; and automatically predicting one or more cuisine type classifications usi

Abstract: Methods for detecting insider threats are disclosed. A method includes collecting server access data and application access data, based on the server access data and the application access data, determining nearest neighbors of an employee, and based on the nearest neighbors of the employee, determining a peer group of the employee, determining an average rank distance (ARD) of the nearest neighbors based on a ranking of the nearest neighbors in a plurality of time periods, identifying ARD gaps between the nearest neighbors, and generating scores corresponding to the ARD gaps between the nearest neighbors. One or more employees are identified that represent an internal threat to an organization based on the scores corresponding to the ARD gaps.

Abstract: Systems and methods are disclosed for parsing logs from arbitrary or unknown systems or applications by capturing heterogeneous logs from the arbitrary or unknown systems or applications; generating one pattern for every unique log message; building a pattern hierarchy tree by grouping patterns based on similarity metrics, and for every group it generates one pattern by combing all constituting patterns of that group; and selecting a set of patterns from the pattern hierarchy tree.

Abstract: This disclosure describes technologies for pattern matching in data streams. Given one or more patterns of data, and one or more data streams, e.g., streams of measurements made by one or more sensors, this disclosure provides apparatus and techniques for identifying occurrences of the patterns in the data streams. Techniques according to this disclosure generally make use of distribution strategies, as disclosed herein, to distribute the work of pattern identification among multiple processors. Data streams may be divided into multiple segments, and patterns for identification as well as data stream segments may be distributed among the multiple processors.

Abstract: Provided is a method that includes determining cuisine type classifiers based on merchant data, receiving transaction data associated with each payment transaction of a plurality of payment transactions involving a plurality of merchants and a plurality of consumers; determining, with at least one processor, a plurality of cuisine type labels based on a plurality of merchant names associated with the plurality of payment transactions or a portion of the plurality of payment transactions, wherein a cuisine type label identifies a cuisine type provided by a respective merchant of the plurality of merchants; generating a merchant cuisine type model from the transaction data to determine a cuisine type label of a merchant, the merchant cuisine type model based on the transaction data, and makes an inference based on a combination of merchant or consumer features associated with a payment transaction of the plurality of payment transactions; and automatically predicting one or more cuisine type classifications usi

Abstract: Systems and methods are disclosed for parsing logs from arbitrary or unknown systems or applications by capturing heterogeneous logs from the arbitrary or unknown systems or applications; generating one pattern for every unique log message; building a pattern hierarchy tree by grouping patterns based on similarity metrics, and for every group it generates one pattern by combing all constituting patterns of that group; and selecting a set of patterns from the pattern hierarchy tree.