Abstract: A device may receive data stored in one or more data sources associated with an organization based on utilizing one or more data discovery-related application programming interfaces (APIs) to access the data. The device may process, utilizing one or more data feature models via the one or more data discovery-related APIs, the data received from the one or more data sources to identify types of data included in the data based on a contextualization of the data. The one or more data feature models may identify a respective set of attributes expected to be included in the types of data. The device may perform multiple analyses of the data after identifying the types of data. The device may determine, based on a result of the multiple analyses, a score for the data. The device may perform one or more actions based on a respective result of the multiple analyses.

Abstract: A device may receive source code from a source data structure, and may receive information associated with a target data structure. The device may analyze the source code to extract statements, and may utilize natural language processing on the statements to identify functions and keywords associated with the source data structure. The device may train a machine learning model with the functions and the keywords to generate a trained machine learning model, and may process the information associated with the target data structure, with the trained machine learning model, to transform a source query to a target query compatible with the target data structure. The device may process the target query, with a neural network model, to generate an optimized target query, and may cause data from the source data structure to be migrated to the target data structure based on the optimized target query.

Abstract: Examples of a data integration and curation system are provided. The system may receive data extracting requirement related to a digital initiative and obtain digital initiative data associated with the same. The system may classify the digital initiative data into various data domains and extract metadata therefrom based on data analysis. The system may determine a word index including a plurality of words from a knowledge database. The plurality of words may be mapped with the data domains to identify a data anomaly in the digital initiative data. The system may determine an anomaly correction for resolving the data anomaly and update the plurality of data domains to include the anomaly correction. The system may identify a user parameter associated with the digital initiative. The system may associate the user parameter with the metadata to create user parameter data associated with the user parameter to generate a data extraction result.

Abstract: Examples for enhancing veracity of data are described herein. Data from a repository may be received based on a data receiving rule. From the received data, a first dataset may be generated using statistical modeling. Also, a first data veracity score for the first dataset is generated which is indicative of a degree of usability of the dataset. Another aspect relates to identifying an anomaly in the first dataset, the corrector, for each anomaly, to identify a correction technique from amongst a plurality of correction techniques. Further, a second dataset is generated using the identified correction technique having second data veracity score higher than the first data veracity score.

Abstract: Examples of a data migration system are provided. The system may receive a data migration requirement. The system may sort the data stored in the on-premise data warehouse into a plurality of data domains. The system may map the plurality of data domains to a usage parameter index and a storage parameter index to determine a data usage pattern and a data storage pattern. The system may evaluate the data storage pattern and the data usage pattern to determine a data assessment index. The system may determine a data migration index from mapping a plurality of cloud platforms to the data assessment index. The system may determine a data migration model compatible with the data assessment index. The system may generate a data migration output comprising the layout for transferring data stored in the on-premise data warehouse to a compatible cloud platform to resolve the data migration requirement.

Abstract: Examples for enhancing veracity of data are described herein. Data from a repository may be received based on a data receiving rule. From the received data, a first dataset may be generated using statistical modeling. Also, a first data veracity score for the first dataset is generated which is indicative of a degree of usability of the dataset. Another aspect relates to identifying an anomaly in the first dataset, the corrector, for each anomaly, to identify a correction technique from amongst a plurality of correction techniques. Further, a second dataset is generated using the identified correction technique having second data veracity score higher than the first data veracity score.

Abstract: A system for assessing a data model includes a data receiver, a model receiver, and a model assessment device. The data receiver receives training data, historical data, and production data. The model receiver receives the data model associated with the historical data and trained using the training data. The historical data includes a first outcome of the data model provided based on an input feature in the production data. The model assessment device identifies a key feature in the production data relative to the input feature based on a target category in the historical data and a statistical distribution of the input feature in the production data. The model assessment device determines a second outcome of the data model based on the key feature. In response to the second outcome being different from the first outcome, the model assessment device determines a veracity score for assessing the data model.

Abstract: An on-demand learning system provides an enhanced leaning environment capable of delivering relevant content on virtually any topic to specific learners. The learning system implements technical features that facilitate curation and subject matter validation of many different types of content. The technical architecture of the learning system also supports intelligent matching of learners to subject matter areas, creation of specific subject matter boards, and resilient maintenance of the boards.

Abstract: A device may receive issue resolution information, associated with a cognitive model, including an item of issue resolution information that describes an issue and a resolution to the issue. The device may assign the item of issue resolution information to a domain hierarchy, where the assigning is associated with a first user. The device may generate a question and an answer corresponding to the item of issue resolution information, where the generating of the question and the answer is associated with a second user. The device may approve the question and the answer, where the approving is associated with a third user. The device may generate a question/answer (QA) pair for the question and the answer. The device may create a data corpus including the QA pair, and provide the data corpus to cause the cognitive model to be trained based on the data corpus.

Abstract: A system for determining a vulnerability of source code includes a processor; and non-transitory computer readable media that includes instruction code that causes the processor to receive source code and a selection of one or more code analyzers for detecting vulnerability issues in the source code. The processor executes the one or more code analyzer to generate initial vulnerability data. The initial vulnerability data specifies one or more vulnerable code sections in the source code. The processor communicates the initial vulnerability data to a vulnerability analyzing engine. The vulnerability analyzing engine is configured to identify one or more code sections of the one or more code sections of the initial vulnerability data that correspond to false positives.

Abstract: Examples of a data integration and curation system are provided. The system may receive data extracting requirement related to a digital initiative and obtain digital initiative data associated with the same. The system may classify the digital initiative data into various data domains and extract metadata therefrom based on data analysis. The system may determine a word index including a plurality of words from a knowledge database. The plurality of words may be mapped with the data domains to identify a data anomaly in the digital initiative data. The system may determine an anomaly correction for resolving the data anomaly and update the plurality of data domains to include the anomaly correction. The system may identify a user parameter associated with the digital initiative. The system may associate the user parameter with the metadata to create user parameter data associated with the user parameter to generate a data extraction result.

Abstract: Examples of systems and method for data transition are described. In an example, the present disclosure provides for automating the process of data movement from on premise to cloud, i.e., source Data warehouse (DWH) movement, ETL to cloud base DWH, ETL. The present disclosure provides for objects identification, metadata extraction, automated data type mapping, target data definition script creation, data extraction in bulk using source native optimized utilities, users and access control mapping to the target DWH, binary object movement, end-end audit report, and reconciliation reports.

Abstract: A device may receive source code from a source data structure, and may receive information associated with a target data structure. The device may analyze the source code to extract statements, and may utilize natural language processing on the statements to identify functions and keywords associated with the source data structure. The device may train a machine learning model with the functions and the keywords to generate a trained machine learning model, and may process the information associated with the target data structure, with the trained machine learning model, to transform a source query to a target query compatible with the target data structure. The device may process the target query, with a neural network model, to generate an optimized target query, and may cause data from the source data structure to be migrated to the target data structure based on the optimized target query.

Abstract: The present disclosure relates to a system, computer readable medium, and method for training workers in occupational skills. The disclosure provides highly automated, artificial intelligence driven, ways of improving human capital through the acquisition, development, and verification of multiple career related proficiencies. Generally, the disclosure provides an integrated digital platform that allows workers to (1) receive training on their existing job skills, (2) verify mastery of the existing job skills through various assessments, and (3) receive recommendations regarding which job skills may be useful to acquire.

Abstract: A system for generating a document includes a user interface processing subsystem configured to generate an interface configured to receive one or more commands. The commands are provided in at least one of an audio format and a textual format. The user interface processing subsystem is configured to provide at least one response to the commands in at least one of: a graphical, audio, and textual format. A natural language processing subsystem of the system is configured to receive and convert the one or more commands into annotated structured text. A search engine of the system is configured to receive the annotated structured text and search a document database for one or more documents related to the annotated structured text.

Abstract: Examples of a data migration system are provided. The system may receive a data migration requirement. The system may sort the data stored in the on-premise data warehouse into a plurality of data domains. The system may map the plurality of data domains to a usage parameter index and a storage parameter index to determine a data usage pattern and a data storage pattern. The system may evaluate the data storage pattern and the data usage pattern to determine a data assessment index. The system may determine a data migration index from mapping a plurality of cloud platforms to the data assessment index. The system may determine a data migration model compatible with the data assessment index. The system may generate a data migration output comprising the layout for transferring data stored in the on-premise data warehouse to a compatible cloud platform to resolve the data migration requirement.

Abstract: A device may receive a feature submission identifying a candidate feature for a software application. The device may pre-process the feature submission to reformat data associated with the candidate feature to match a data format associated with stored information regarding past feature submissions. The device may perform natural language processing on the data associated with the candidate feature to determine a semantic meaning of the candidate feature. The device may compare the semantic meaning of the candidate feature with an index of past feature submissions. The device may provide a response indicating whether the candidate feature matches the past feature submissions wherein the response comprises an indication that the candidate feature does not match any past feature submission or information identifying one or more past feature submissions associated with a threshold semantic similarity to the candidate feature.

Abstract: Examples of systems and method for data transition are described. In an example, the present disclosure provides for automating the process of data movement from on premise to cloud, i.e., source Data warehouse (DWH) movement, ETL to cloud base DWH, ETL. The present disclosure provides for objects identification, metadata extraction, automated data type mapping, target data definition script creation, data extraction in bulk using source native optimized utilities, users and access control mapping to the target DWH, binary object movement, end-end audit report, and reconciliation reports.

Abstract: A device may receive data stored in one or more data sources associated with an organization based on utilizing one or more data discovery-related application programming interfaces (APIs) to access the data. The device may process, utilizing one or more data feature models via the one or more data discovery-related APIs, the data received from the one or more data sources to identify types of data included in the data based on a contextualization of the data. The one or more data feature models may identify a respective set of attributes expected to be included in the types of data. The device may perform multiple analyses of the data after identifying the types of data. The device may determine, based on a result of the multiple analyses, a score for the data. The device may perform one or more actions based on a respective result of the multiple analyses.

Abstract: A data intelligence platform may include a role-based workbench component configured to control access to or use of the data intelligence platform to identify an issue associated with data associated with an organization. The data intelligence platform may include a data intelligence component configured to: process the data to identify the issue related to the data, or perform a set of actions related to fixing the issue related to the data based on a result of processing the data. The data intelligence platform may include a data management component configured to facilitate access to a corpus component or to a source of the data. The data intelligence platform may include the corpus component configured to facilitate processing of the data to identify the issue or to perform the set of actions.