Abstract: A method, computer system, and a computer program product for labeling optimization is provided. The present invention may include receiving a plurality of labeled historical transaction timeline image clusters based on a plurality of historical transaction timeline images clustered using unsupervised machine learning. The present invention may further include training an image recognition model using supervised machine learning based on the received plurality of labeled historical transaction timeline image clusters. The present invention may also include receiving, by the trained image recognition model, a current transaction timeline image. The present invention may further include assigning a corresponding label to the received current transaction timeline image based on matching the received current transaction timeline image to one of the received plurality of labeled historical transaction timeline image clusters.

Abstract: A method of preparing a dataset may comprise calculating a pattern relevance for a first field in the dataset and modifying the first field based on the pattern relevance. The method may further comprise detecting a contextual cue in the first field. The method may further comprise retrieving contextual information for a value in the first field and adding that contextual information to the database. Finally, the method may further comprise identifying a numerical scheme for the first field and parsing the first field into a number according to that numerical scheme.

Abstract: A computer-implemented system, method and computer program product for preparing alert reports that includes: receiving an override request to override an insight used to prepare the alert reports; determining, determining one or more execution pipelines used to prepare the alert reports that used the insight that is the subject of the override request; determining one or more impacted pipeline runs that were executed to prepare the alert reports that used the insight that is the subject of the override request; updating the one or more execution pipelines used to prepare the alert reports; and generating new alert reports, wherein generating the new alert reports comprises re-running the one or more impacted pipeline runs using the one or more updated execution pipelines wherein the insight that is the subject of the override request is overridden.

Abstract: A computer-implemented system, platform, computer program product, tool, and/or method for re-running alert reports that includes: identifying an alert report to be rerun; collecting information on the alert report; gathering information on the configuration of a data analytics pipeline that generated the alert report; gathering data used to generate the alert report; recreating a regenerated data analytics pipeline based upon the information gathered on the configuration of the data analytics pipeline that generated the alert report; running the regenerated data analytics pipeline using the gathered data to create a rerun alert report; and optionally comparing the rerun to the original run including optionally generating a visual display of the results including in an aspect creating a side-by-side comparison of the tasks.

Abstract: A computer-implemented system, platform, computer program product, tool, and/or method for capturing data and metadata for providing traceability of data analytic pipeline runs that includes: triggering a first pipeline run; obtaining a pipeline configuration for the first pipeline run, including one or more tasks; capturing and storing the pipeline configuration and starting inputs for the first pipeline run; executing the first pipeline run; capturing, before each task in the first pipeline run is executed, input data and input metadata for each task; capturing, after each task in the first pipeline run is executed, output data and output metadata for each task; and capturing the alerts for the first pipeline run.

Abstract: A method and/or system of extracting a table having data in a plurality of rows from a Not Only Structured Query Language (NoSQL) database to a different type of database that includes: scanning all the rows in a desired table in the NoSQL database and producing a list of column families and associated column names; creating a schema for a new table having a table catalog of new column names using a Java Script Object Notation (JSON) structure to extract the columns names from the list of column families; reading and extracting at least a portion of the data from the desired table in the NoSQL database into the new table having the table catalog of new columns names; associating a creation timestamp with the new table; and saving the new table having the table catalog of new column names to the different database.

Abstract: A method and or system for processing a response message from an analytical application that includes receiving the response message; parsing the response message to facilitate selecting a semantic model to translate the response message; obtaining the semantic model to translate the response message; translating the response message using the semantic model; and converting the translated response message to non-rich text. The non-rich text can be annotated for semantic meaning that can be displayed for example on a “dumb” display that does not support rich-text formats.

Abstract: An approach is provided that automatically computes a number of feature combinations based on a set of subject matter fields that are selected from a set of subject matter fields and further based on qualifiers that correspond to the selected set of subject matter fields. A model in an artificial intelligence (AI) system is then trained using the computed plurality of feature combinations.

Abstract: A system, platform, program product, and/or method for generating new composite insight templates that includes: running a machine learning model on a data set to obtain for each of a plurality of entities a risk score and feature-based insights; generating a list of top “n” features input to the machine learning model that contributes to the risk score for each entity; grouping entities based upon similar features input to the machine learning model that contributes to the risk score for each entity; generating a decision tree for at least one of the group of entities; extracting, from the decision tree generated for the at least one of the group of entities, one or more feature-based insights; generating, by applying subject matter input, a new composite insight based upon the one or more feature-based insights; and adding the new composite insight to insight templates.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for performing testing on a portion of an application. The method includes one or more processors identifying a test configuration for testing an application. The application comprises a plurality of components. The test configuration includes an indication to test at least one component of the application. The method further includes one or more processors testing the indicated at least one component of the application. The method further includes one or more processors determining a validation result of testing the indicated at least one component of the application.

Abstract: A computer-implemented system, platform, computer program product, and/or method for reducing data processing that includes identifying data properties used to generate features used as input to data analytic models; associating the data properties used to generate the features to corresponding features; determining whether an incoming data record is a previously processed data record; determining, in response to an incoming data record being a previously processed data record, whether the incoming data record matches the previously processed data record; identifying data properties in the incoming data record that have changed; determining features associated with the data properties in the incoming data record that have changed; and generating the features associated with the data properties in the incoming data record that have changed.

Abstract: A computer-implemented system, platform, computer program product, tool, and/or method for re-running alert reports that includes: identifying an alert report to be rerun; collecting information on the alert report; gathering information on the configuration of a data analytics pipeline that generated the alert report; gathering data used to generate the alert report; recreating a regenerated data analytics pipeline based upon the information gathered on the configuration of the data analytics pipeline that generated the alert report; running the regenerated data analytics pipeline using the gathered data to create a rerun alert report; and optionally comparing the rerun to the original run including optionally generating a visual display of the results including in an aspect creating a side-by-side comparison of the tasks.

Abstract: A computer-implemented system, platform, computer program product, and/or method for optimizing a data analytics suspicious activity detection pipeline that includes identifying a data analytics suspicious activity detection (SAD) pipeline for optimization; inputting desired key performance indicators for the data analytics suspicious activity detection (SAD) pipeline; gathering key performance indicators from previous runs of the data analytics suspicious activity detection (SAD) pipeline; identifying candidate pipeline configurations for simulation runs; running simulations of the candidate pipeline configurations; analyzing the simulations of the candidate pipeline configurations; and identifying the optimal pipeline configuration for the desired key performance indicators.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include receiving data, mapping a plurality of features to a plurality of attributes of the data, and generating a feature-to-attribute map based on said mapping. The operations may further include submitting new data to a feature calculation loop and implementing the feature calculation loop. The operations may also include tracking the features in the feature calculation loop, identifying a changed feature in the features, and recalculating for the changed feature using the feature calculation loop.

Abstract: Embodiments relate to a system, program product, and method for automatically executing an impact analysis of a data analytics pipeline to determine impacts to the pipeline subject to changes to input data and the pipeline. The method includes determining, automatically, components of the pipeline that are impacted by the implemented changes. The method also includes identifying datasets to rescore through the pipeline. Each of the datasets to rescore have been scored through the pipeline prior to the changes such that previous scores of each of the respective datasets have been determined by the pipeline prior to the changes. The method further includes rerunning, through only the determined impacted components, the datasets, thereby generating rescores of the datasets. The method also includes retrieving each of the previous scores of the datasets, comparing the rescores with the respective previous scores, and transmitting, subject to the comparing, alerts to an output device.

Abstract: A system, program product, and/or method for evaluating the condition of data for using data analytics options that includes: collecting data to evaluate its condition for supporting a plurality of data analytics options; determining, for each data analytics option, a plurality of a group of data indices, the group consisting of: a volume index measuring the amount of data, a history index for measuring the amount of historical data, a variety index for measuring the variety and type of data, a veracity index for measuring the quality of the data, a value index for measuring the information gain provided by the data; and determining a data readiness score that encompasses scaling, for each of the data analytics options, the plurality of the data indices group. Utilizing a data requirements matrix, providing domain-specific business objectives, and calculating for each of the data analytics options the information gain is also disclosed.

Abstract: A processor may receive target data regarding initial targets. The initial targets may relate to specific values for a first set of factors regarding performance of tasks for a first time period. The processor may receive task data regarding the performance of the tasks. The task data may be associated with values for a second set of factors over a second time period. The processor may analyze attributes of the tasks. The processor may generate feature data regarding features of the task. The features may relate to the attributes of the tasks that can be varied to perform the tasks over the second time period. The processor may generate a simulation of the performance of the tasks using the task data and the feature data.

Abstract: In some embodiments, a processor may receive a claim, where the claim relates to an event. The processor may identify characteristics of the claim. The processor may determine, using an AI model, an alert reason based on a similarity of the characteristics of the claim to characteristics of one or more other claims. The processor may generate an alert notification, and the processor may output the alert notification to a user.

Abstract: A computer-implemented system, method and computer program product for preparing alert reports that includes: receiving an override request to override an insight used to prepare the alert reports; determining, determining one or more execution pipelines used to prepare the alert reports that used the insight that is the subject of the override request; determining one or more impacted pipeline runs that were executed to prepare the alert reports that used the insight that is the subject of the override request; updating the one or more execution pipelines used to prepare the alert reports; and generating new alert reports, wherein generating the new alert reports comprises re-running the one or more impacted pipeline runs using the one or more updated execution pipelines wherein the insight that is the subject of the override request is overridden.

Abstract: A method, computer system, and a computer program product for labeling optimization is provided. The present invention may include receiving a plurality of labeled historical transaction timeline image clusters based on a plurality of historical transaction timeline images clustered using unsupervised machine learning. The present invention may further include training an image recognition model using supervised machine learning based on the received plurality of labeled historical transaction timeline image clusters. The present invention may also include receiving, by the trained image recognition model, a current transaction timeline image. The present invention may further include assigning a corresponding label to the received current transaction timeline image based on matching the received current transaction timeline image to one of the received plurality of labeled historical transaction timeline image clusters.