Abstract: A crash prediction computing system includes a machine learning module capable of analyzing data logs associated with each of a plurality of services or applications to identify and categorize every error, exception, and/or crash, such as those resulting from client system interactions based on crash type, customer profile type, customer screen navigation flow, time or crash. The machine learning algorithms continuously train the crash prediction models for each crash category with associated client computing system navigation flow. The crash prediction computing system applies each model before each screen/activity navigation to predict whether the next move will result in an error, exception or crash, and for each predicted error, exception, or crash, automatically implement alternate route functionality to arrive at a desired target.

Abstract: Aspects of the disclosure relate to impact analysis in a next-release quality assurance environment for a software application. First log data associated with user navigation of user interface components in a production environment may be retrieved. A production navigational graph may be generated, where a node represents a user interface component visited by a user, and an edge representing a navigational link traversed by the user. Then, second log data associated with release notes for a next-release version of the software application maybe retrieved. Then, the computing platform may identify a change in a portion of a software code in the next-release version, and may identify, based on the production navigational graph, a user interface component and/or a link potentially impacted by the change. The production navigational graph may be provided, via an interactive graphical user interface, where the user interface component and/or the link is visually highlighted.

Abstract: Embodiments of the present invention provide a system for facilitating a secure way to allow primary users and secondary users to perform interactions. In particular, the system may be configured to receive an interaction request from a primary user, where the interaction request comprises location of an automated machine, initiate and lock a session between the automated machine and the entity application located on the user device of the primary user, identify a trigger and display a code on the automated machine, wherein the code is scannable by the user device of the primary user or a user device of an authorized secondary user, receive a scanned code from the user device of the primary user or the user device of the authorized secondary user, determine a match between the scanned code and the code displayed on the automated machine, and complete the interaction.

Abstract: Embodiments of the present invention provide systems and methods for using machine learning to analyze and infer the contextual significance of a conversational language in order to proactively engage with one or more users in a familiar manner via a virtual voice assistant. As such, the systems and methods reduce redundancy of process steps for the user in accessing relevant information or initiating certain resource activities via disparate channels of communication by creating a continuity of conversational tone and substance.

Abstract: A crash test simulator device for re-creating a software crash scenario within a virtual environment using artificial intelligence processes to consider a large group of variables that may be relevant to the crash incident. The crash test simulator device includes a production environment monitoring engine configured to monitor a user's interaction with an application implemented within a production environment, and generate information used to re-create a crash incident within a virtual environment.

Abstract: A data aggregator includes a memory, a network interface, a vectorization engine, and an aggregator. The memory stores a plurality of aggregation rules. The network interface retrieves a plurality of transactions that includes a first transaction and a second transaction. The vectorization engine determines a first transaction vector for the first transaction and a second transaction vector for the second transaction. The aggregator determines that the first transaction occurred during a first life event and that the second transaction occurred during a second life event. The aggregator also determines that the first life event and the second life event are the same and in response to that determination, assigns the first transaction and the second transaction to a cluster for the first life event. The aggregator further stores the first life event, the second life event, and an identifier for the cluster to the database.

Abstract: A system is provided for mobile log heatmap-based auto testcase generation. In particular, the system may continuously track and log user actions and data flows for applications within the production environment. Based on the logs, the system may generate a navigation network graph through which the system may identify all possible navigation paths that may be taken by the user to access certain functions or screens of the application. Once the paths have been identified, the system may collect and sanitize testing data based on user session and system interaction data in the production environment. The testing data may then be used to drive the development of the next release or version of the application.

Abstract: Aspects of the disclosure relate to machine learning based test coverage in a production environment. A computing platform may retrieve, for a software application, first log data associated with a production environment, and generate a production navigational graph where a node represents a webpage visited by a user, and an edge represents a navigational link between two webpages. Likewise, second log data associated with a testing environment may be retrieved, and a testing navigational graph may be generated, where a node represents a webpage tested by a developer, and an edge represents a navigational link between two webpages. Then, the computing platform may generate a coverage graph indicative of a gap between the navigation activity and the testing activity. Subsequently, the computing platform may provide, via an interactive graphical user interface, the coverage graph, where nodes and edges of the coverage graph are associated with selectable visual objects.

Abstract: Aspects of the disclosure relate to machine learning based test coverage in a production environment. A computing platform may retrieve, for a software application, first log data associated with a production environment, and generate a production navigational graph where a node represents a webpage visited by a user, and an edge represents a navigational link between two webpages. Likewise, second log data associated with a testing environment may be retrieved, and a testing navigational graph may be generated, where a node represents a webpage tested by a developer, and an edge represents a navigational link between two webpages. Then, the computing platform may generate a coverage graph indicative of a gap between the navigation activity and the testing activity. Subsequently, the computing platform may provide, via an interactive graphical user interface, the coverage graph, where nodes and edges of the coverage graph are associated with selectable visual objects.

Abstract: Aspects of the disclosure relate to impact analysis in a next-release quality assurance environment for a software application. First log data associated with user navigation of user interface components in a production environment may be retrieved. A production navigational graph may be generated, where a node represents a user interface component visited by a user, and an edge representing a navigational link traversed by the user. Then, second log data associated with release notes for a next-release version of the software application maybe retrieved. Then, the computing platform may identify a change in a portion of a software code in the next-release version, and may identify, based on the production navigational graph, a user interface component and/or a link potentially impacted by the change. The production navigational graph may be provided, via an interactive graphical user interface, where the user interface component and/or the link is visually highlighted.

Abstract: A crash prediction computing system includes a machine learning module capable of analyzing data logs associated with each of a plurality of services or applications to identify and categorize every error, exception, and/or crash, such as those resulting from client system interactions based on crash type, customer profile type, customer screen navigation flow, time or crash. The machine learning algorithms continuously train the crash prediction models for each crash category with associated client computing system navigation flow. The crash prediction computing system applies each model before each screen/activity navigation to predict whether the next move will result in an error, exception or crash, and for each predicted error, exception, or crash, automatically implement alternate route functionality to arrive at a desired target.

Abstract: A crash test simulator device for re-creating a software crash scenario within a virtual environment using artificial intelligence processes to consider a large group of variables that may be relevant to the crash incident. The crash test simulator device includes a production environment monitoring engine configured to monitor a user's interaction with an application implemented within a production environment, and generate information used to re-create a crash incident within a virtual environment.

Abstract: In one embodiment, a system includes one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including sequencing a plurality of rows into a first sequence based on a first criteria and determining to store a first set of the plurality of rows in a first block of a first storage unit in accordance with the first sequence. The operations further include determining to store, in a first block of the second storage unit, a block identification of the first block of the first storage unit and a row identification for each row of the first set of the plurality of rows. The operations further include re-creating the first set of the plurality of rows of the first block of the first storage unit using information stored in the second storage unit.

Abstract: Systems, computer program products, and methods are described herein for multi-release and parallel development of a database. The present invention is configured to determine a first version of a database release, wherein the first version is based on a first script; receive an incremental script associated with the first version of the database release; implement a database modeling tool to generate a second script, thereby creating a second version of the database release based on at least the first script and the incremental script; automatically generate an incremental database package based on at least the second version of the database release; and deploy the incremental database package, wherein deploying further comprises creating a second version of the database release based on the second script, wherein the second script is a baseline script for the second version of the database release.

Abstract: A database server is configured to receive input data which includes a data table. Each record of the table includes a record key and one or more entries. Each of the one or more entries is associated with a corresponding column key of the data table. Instructions are received for sorting the input data according to the column key. The instructions indicate an order in which to sort the input data. Based on the order indicated by the instructions, a pluggable sorting index associated with a column key of the data table is generated. The pluggable sorting index includes the record keys arranged in a sorted order according to sortable properties of entries associated with the column key. A sorted dataset which includes the entries associated with the column key arranged according to the sorted order of the pluggable sorting index is generating using the pluggable sorting index.

Abstract: A method for predicting equipment failure includes receiving parameter sample values associated with parameters of benchmark equipment and operational status information associated with the benchmark equipment. The parameter sample values and operational status information are periodically acquired. A model is generated for relating one or more of the parameters to benchmark equipment failure. For each parameter, a threshold value at which an output of the model indicates benchmark equipment failure is determined. Next, parameters of an equipment under test having parameter sample values that match the determined threshold values are determined. For each determined parameter, benchmark equipment having parameter sample values that match the parameter sample values of the equipment under test that match the determine threshold values are determined. Survivability data for the equipment under test is generated based on survivability data associated with the determined benchmark equipment.

Abstract: A joiner accesses a first sorted dataset and a second sorted dataset. Each dataset includes a corresponding plurality of data blocks, each including a set of records. Each record is associated with a corresponding record key. A set of first records for each first data block of the first dataset is arranged based on values of the first record keys. A set of second records for each second data block of the second dataset is arranged based on values of the second record keys. A first root element is extracted from the first sorted dataset. A second root element is extracted from the second sorted dataset. In response to determining that the first and second root elements match, an output is generated by joining the first record associated with the first root element with the second record associated with the second root element.

Abstract: A data storage device that includes a memory operable to store a data table and an indexing engine implemented by a processor. The indexing engine is configured to receive data and to store the data in the data table. The indexing engine is further configured to determine an index key and data location information for the stored data and to determine a set of index table references based on the index key. Each index table reference identifies an index table that links index keys with data location information. The indexing engine is further configured to identify a set of index tables corresponding with the set of index table references and to store the index key and the data location information for the stored data in one or more index tables from the set of index tables.

Abstract: A device that includes a parsing engine and an execution engine. The parsing engine is configured to identify micro operations corresponding with operations that are common among at least two jobs from a set of jobs and to store the identified micro operations in memory. The execution engine is configured to receive a job from the set of jobs, to identity micro operations and dependencies within the job, and to execute the identified micro operations for the job in accordance with the identified dependencies.

Abstract: A database server is configured to receive input data which includes a data table. Each record of the table includes a record key and one or more entries. Each of the one or more entries is associated with a corresponding column key of the data table. Instructions are received for sorting the input data according to the column key. The instructions indicate an order in which to sort the input data. Based on the order indicated by the instructions, a pluggable sorting index associated with a column key of the data table is generated. The pluggable sorting index includes the record keys arranged in a sorted order according to sortable properties of entries associated with the column key. A sorted dataset which includes the entries associated with the column key arranged according to the sorted order of the pluggable sorting index is generating using the pluggable sorting index.