Abstract: A computer-implemented method, in accordance with one embodiment, includes collecting data relating to development of a software product, the collected data including a plurality of different types of data relating to the development of the software product. A portion of the collected data is selected based on a characteristic of an intended user, the portion of the collected data including a plurality of the types of data. The selected portion of the collected data is transformed into data visualizations representing the data, the different types of the data having different data visualizations relative to one another. The data visualizations are output in a single visual representation for display to the intended user.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises receiving and storing input data from at least two users; calculating a risk score for each identified risk in the received data based on priority risk factors affecting respectively identified risks; dynamically optimizing a risk analysis of the received input for multiple users within a user interface of a computing device by recalculating risk scores based on the received data and identified risks; and generating a notification for the user interface of the computing device based on the dynamic optimization of the risk analysis of the received input.

Abstract: An approach is disclosed that receives a user activity from a user that is using a datastore visualization display. The display displays a first visualization pertaining to one or more datastores with the datastore visualization display provided at least in part by a context driven lateral artificial intelligence (CDLAI) engine. The received user activity is provided as input to the CDLAI to generate a second visualization that is displayed to the user at the datastore visualization display that is displayed to the user. Artificial intelligence (AI) models that are used by the CDLAI are then trained based on the received user activity. The training results in updates to the visualizations.

Abstract: A computer-implemented method, in accordance with one aspect of the present invention, includes collecting data existing in a development system that relates to the code change request, in response to receiving a code change request to merge new code with existing code. Factors from the collected data are computed for assessing a risk of promoting the new code, the factors including at least: a developer information factor, a developer availability factor, and an environment health analysis factor. The factors are processed to compute a confidence score for the code change request. If the confidence score is in a first predefined range, the new code corresponding to the code change request is promoted for merging with the existing code, without human intervention. If the confidence score is in a second predefined range, an indication that human intervention is needed before promoting the code is output.

Abstract: A computer-implemented method, in accordance with one aspect of the present invention, includes collecting data existing in a development system that relates to the code change request, in response to receiving a code change request to merge new code with existing code. Factors from the collected data are computed for assessing a risk of promoting the new code, the factors including at least: a developer information factor, a developer availability factor, and an environment health analysis factor. The factors are processed to compute a confidence score for the code change request. If the confidence score is in a first predefined range, the new code corresponding to the code change request is promoted for merging with the existing code, without human intervention. If the confidence score is in a second predefined range, an indication that human intervention is needed before promoting the code is output.

Abstract: A computer-implemented method, according to one embodiment, includes causing a trained artificial intelligence (AI) model to derive data access privilege grants before data is stored in a predetermined database, and segmenting the data according to the data access privilege grants during storage of the data in the predetermined database. Metadata that defines the data access privilege grants is stored with the segments of data. The method further includes receiving, from a first user device, a query requesting data stored in the predetermined database. At least some of the metadata is read to identify a segment of the data associated with a data access privilege grant associated with the query. The method further includes allowing the first user device to access the identified segment of data for fulfilling the query.

Abstract: A computer-implemented method, in accordance with one embodiment, includes receiving a taxonomy specifying entities of a business service and levels of said entities. Time series data about the entities is collected and stored. Impactor propagation paths between entities is identified. A territory of a health impact of each entity is also identified. A health score for each of the entities, considering impacts on a health of the entity by at least one other entity, is computed based on the data, the propagation paths, and the territories of the entities. At least one of the health scores is output.

Abstract: A computer-implemented method, in accordance with one embodiment, includes collecting data relating to development of a software product, the collected data including a plurality of different types of data relating to the development of the software product. A portion of the collected data is selected based on a characteristic of an intended user, the portion of the collected data including a plurality of the types of data. The selected portion of the collected data is transformed into data visualizations representing the data, the different types of the data having different data visualizations relative to one another. The data visualizations are output in a single visual representation for display to the intended user.

Abstract: A computer-implemented method for receiving, at a database, an analytics request associated with a selected data line from a database and reading a set of additional bytes from the database corresponding to the selected data line. The method may further include parsing and formatting the set of additional bytes read from the database to generate, at the database, an analytics reply to the analytics request, where each of a plurality of data lines in the database represents a search query and where the analytics data comprises a number of search query requests on a subject matter and a number of search query requests for private data.

Abstract: A computer-implemented method, in accordance with one embodiment, includes collecting data relating to development of a software product, the collected data including a plurality of different types of data relating to the development of the software product. A portion of the collected data is selected based on a characteristic of an intended user, the portion of the collected data including a plurality of the types of data. The selected portion of the collected data is transformed into data visualizations representing the data, the different types of the data having different data visualizations relative to one another. The data visualizations are output in a single visual representation for display to the intended user.

Abstract: A computer-implemented method, computer system, and computer program product for managing a database are disclosed. Features thereof include selecting data lines for analytics, each data line including original data, and adding, to each of the selected data lines, a set of additional bytes to represent metadata about a corresponding one of the data lines. The features further include receiving a request to receive a first data line, and reading a first set of additional bytes corresponding to the first data line.

Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: reading lines of code included in a collection of source code; identifying an assigned tag in the collection of source code; identifying a start tag location and an end tag location associated with the assigned tag, wherein the lines of code included between the start tag location and the end tag location identify a code block; processing the code block to generate logging data for the code block based, at least in part, on the assigned tag; and providing the logging data for linking to executable byte code compiled from the collection of source code, wherein the logging data is used to log code data relating to the code block during execution of the executable byte code.

Abstract: Techniques for data evaluation using machine learning are provided. A textual document is received, and the textual document is parsed using a recurrent neural network (RNN) to extract a plurality of keywords. A first subset of keywords which are known by a user and a second subset of keywords which are unknown by the user are each identified. A summary of the textual document is generated based on the second subset of keywords. The summary is output, comprising: outputting information related to a first keyword of the second subset of keywords and, upon determining that the first keyword is understood by the user, outputting information related to a second keyword of the second subset of keywords.

Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: reading lines of code included in a collection of source code; identifying an assigned tag in the collection of source code; identifying a start tag location and an end tag location associated with the assigned tag, wherein the lines of code included between the start tag location and the end tag location identify a code block; processing the code block to generate logging data for the code block based, at least in part, on the assigned tag; and providing the logging data for linking to executable byte code compiled from the collection of source code, wherein the logging data is used to log code data relating to the code block during execution of the executable byte code.

Abstract: Techniques for data evaluation using machine learning are provided. A textual document is received, and the textual document is parsed using a recurrent neural network (RNN) to extract a plurality of keywords. A first subset of keywords which are known by a user and a second subset of keywords which are unknown by the user are each identified. A summary of the textual document is generated based on the second subset of keywords. The summary is output, comprising: outputting information related to a first keyword of the second subset of keywords and, upon determining that the first keyword is understood by the user, outputting information related to a second keyword of the second subset of keywords.

Abstract: A computer-implemented method, computer system, and computer program product for managing a database are disclosed. Features thereof include selecting data lines for analytics, each data line including original data, and adding, to each of the selected data lines, a set of additional bytes to represent metadata about a corresponding one of the data lines. The features further include receiving a request to receive a first data line, and reading a first set of additional bytes corresponding to the first data line.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises receiving and storing input data from at least two users; calculating a risk score for each identified risk in the received data based on priority risk factors affecting respectively identified risks; dynamically optimizing a risk analysis of the received input for multiple users within a user interface of a computing device by recalculating risk scores based on the received data and identified risks; and generating a notification for the user interface of the computing device based on the dynamic optimization of the risk analysis of the received input.

Abstract: A method for inhibiting ectopic calcification of bodily organs caused by elevated LDL cholesterol is provided. The method includes administering an LDL lowering agent to a patient, comprising a PCSK9 inhibitor selected from Evolocumab, Alirocumab, Praluent, Repatha Pushtonix, Repatha autoinjector, and combinations of the foregoing.

Abstract: Methods for inhibiting stenosis, obstruction and/or calcification of a heart valve following implantation in a vessel having a wall are disclosed. In one aspect the method includes providing a bioprosthetic heart valve mounted on an elastical stent; treating the bioprosthetic heart valve with a tissue fixative; coating the stent and the bioprosthetic valve with a coating composition including one or more therapeutic agents; implanting the bioprosthetic valve into the vessel in a diseased natural valve site; eluting the coating composition from the bioprosthetic valve; and inhibiting stenosis, obstruction and/or calcification of the bioprosthetic heart valve by preventing the attachment of stem cells to the bioprosthetic heart valve, the stem cells circulating external and proximate to the bioprosthetic heart valve by activating nitric oxide production (i) in the circulating stem cells, (ii) in an endothelial cell lining covering the bioprosthetic heart valve tissue, (iii) or both.

Abstract: Methods for inhibiting stenosis, obstruction and/or calcification of a heart valve following implantation in a vessel having a wall are disclosed. In one aspect the method includes providing a bioprosthetic heart valve mounted on an elastical stent; treating the bioprosthetic heart valve with a tissue fixative; coating the stent and the bioprosthetic valve with a coating composition including one or more therapeutic agents; implanting the bioprosthetic valve into the vessel in a diseased natural valve site; eluting the coating composition from the bioprosthetic valve; and inhibiting stenosis, obstruction and/or calcification of the bioprosthetic heart valve by preventing the attachment of stem cells to the bioprosthetic heart valve, the stem cells circulating external and proximate to the bioprosthetic heart valve by activating nitric oxide production (i) in the circulating stem cells, (ii) in an endothelial cell lining covering the bioprosthetic heart valve tissue, (iii) or both.