Abstract: Aspects of the present disclosure relate to controlling virtual reality (VR) content displayed on a VR head mounted display (HMD). Communication can be established between a computer system, a VR HMD, and a mobile device. A user input configured to control VR content displayed on a display of the VR HMD can be received on the mobile device. The VR content displayed on the VR HMD can then be controlled based on the user input received on the mobile device.

Abstract: A first user access, directed to a first network document, of a first user is detected. One or more first network document concepts in the first network document are detected. A first user profile of the first user is retrieved based on the first user access. A first knowledge gap of the first user is identified based on the first network document concepts and based on the first user profile. One or more additional network documents are obtained based on the one or more first network document concepts. The additional network documents include at least a first concept of the one or more first network document concepts. The first network document is modified to include at least a portion of the first concept from the one or more additional network documents. The modification is based on the first knowledge gap and on the one or more additional network documents.

Abstract: Targeted acquisition of data for model training includes automatically generating metadata describing samples, of an initial dataset, in neighborhoods of an embedding space in which the samples are embedded. The samples described by the automatically generated metadata are classified by a classification model, and include both correctly classified samples in the neighborhoods and incorrectly classified samples in the neighborhoods. Additionally, attributes of one or more correctly classified samples of the collection of samples and one or more incorrectly classified samples of the collection of samples are identified, and queries are generated based on the identified attributes, the queries tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

Abstract: The invention relates to an approach for data quality assessment for data analytics, the approach comprising providing a data set, the data set comprising multiple data fields, predicting by a first trained machine learning model at least one usage type of the data set using characteristics of the data fields as input, for each usage type of the at least one usage type, determining a usage specific data quality score of each of the predicted usage types, and using of the data set based on the at least one usage type and associated data quality score.

Abstract: According to a computer-implemented method, an available amount of each of multiple computing resources is determined by machine logic over a period of time at a computing device. The machine logic also determines an expected usage of each computing resource to execute each workflow in a queue. The machine logic also determines a time of execution of each workflow in the queue based on the available amount of each of the multiple computing resources over time and the expected usage of each computing resource to execute each workflow in the queue.

Abstract: Techniques for build recovery from inter-code commit failure. A build error for a software project is identified. A first software module, with one or more errors related to the build error, is selected. A comparison software module for the first software module is identified. The comparison software module includes at least one of: (i) a sibling software module to the first software module or (ii) an earlier version of the first software module. A potential problem with the first software module, related to the build error, is determined based on comparing the first software module with the comparison software module. A solution to the potential problem is generated based on the first software module. The solution includes a modification to the software code of the first software module. The solution is applied by modifying the software code of the first software module.

Abstract: A method of data classification includes: identifying a cluster of data classes; classifying columns of a current data set; identifying the cluster in the current data set; determining, based on the cluster, an expected column is missing from the current data set; determining a neighboring data set; identifying the expected column in the neighboring data set; classifying the expected column in the neighboring data set; creating a new data class in the current data set; and classifying an unclassified column in the current data set or the neighboring data set with the new data class.

Abstract: Data classification includes tracking classification of columns of data into data classes of a collection of classes available for classifying the columns, obtaining a target column of data, of a target dataset, to be classified into a data class of the collection of candidate classes, and classifying the target column of data into a data class of the collection of classes based on historical data classification characteristics provided by the tracking. The classifying includes selecting a group of candidate data classes of the collection of classes to compare to value(s) of the target column, the selecting excludes at least some candidate data classes of the collection from comparison to the value(s), and establishing a priority between the candidate data classes of the group of candidate classes in comparing the value(s) of the target column of data to the selected group of candidate classes.

Abstract: A method and system for improving data and memory reorganization and storage technology is provided. The method includes configuring data capture and analysis settings of a database system resulting in configured data capture settings. A data and associated memory analysis request is received and specified test code is selected. A specified portion of data and associated memory is selected and the specified analysis code is executed resulting in execution of said specified type of analysis with respect to the specified portion of said data and associated memory. The specified portion of said data and associated memory is modified and stored.

Abstract: In an approach to dynamically identifying and modifying the parallelism of a particular task in a pipeline, the optimal execution time of each stage in a dynamic pipeline is calculated. The actual execution time of each stage in the dynamic pipeline is measured. Whether the actual time of completion of the data processing job will exceed a threshold is determined. If it is determined that the actual time of completion of the data processing job will exceed the threshold, then additional instances of the stages are created.

Abstract: Data classification includes tracking classification of columns of data into data classes of a collection of classes available for classifying the columns, obtaining a target column of data, of a target dataset, to be classified into a data class of the collection of candidate classes, and classifying the target column of data into a data class of the collection of classes based on historical data classification characteristics provided by the tracking. The classifying includes selecting a group of candidate data classes of the collection of classes to compare to value(s) of the target column, the selecting excludes at least some candidate data classes of the collection from comparison to the value(s), and establishing a priority between the candidate data classes of the group of candidate classes in comparing the value(s) of the target column of data to the selected group of candidate classes.

Abstract: A method provides for classifying data fields of a dataset. A classifier configured for determining confidence values for a plurality of data classes for the data fields may be applied. Using the confidence values, data class candidates may be identified. Data fields may be determined for which a plurality of data class candidates is identifiable. Using previous user-selected data class assignments, a probability may be determined for the data class candidates that the respective data class candidate is a data class to which the respective data field is to be assigned. The data fields may be classified using the probabilities to select for the data fields a data class from the data class candidates. The dataset may be provided with metadata identifying for the data fields the data classes to which the respective data fields are assigned.

Abstract: In an approach for automatically ranking and routing data quality remediation tasks, a processor analyzes a data set ingested by a repository to produce a set of data quality problems. A processor computes a score for each data quality problem of the set of data quality problems. A processor identifies a route to send each data quality problem of the set of data quality problems. A processor exports each data quality problem according to the score and the route.

Abstract: According to a computer-implemented method, an available amount of each of multiple computing resources is determined by machine logic over a period of time at a computing device. The machine logic also determines an expected usage of each computing resource to execute each workflow in a queue. The machine logic also determines a time of execution of each workflow in the queue based on the available amount of each of the multiple computing resources over time and the expected usage of each computing resource to execute each workflow in the queue.

Abstract: A method and system for improving data and memory reorganization and storage technology is provided. The method includes configuring data capture and analysis settings of a database system resulting in configured data capture settings. A data and associated memory analysis request is received and specified test code is selected. A specified portion of data and associated memory is selected and the specified analysis code is executed resulting in execution of said specified type of analysis with respect to the specified portion of said data and associated memory. The specified portion of said data and associated memory is modified and stored.

Abstract: A method to identify potentially duplicative records in a data set is provided. A computer may collect a data profile for the data set that provides descriptive information with regard to attributes of the data set. Based, at least in part, on the data profile, weights are determined for the attributes. As values of a data record are compared to values of the same respective attributes in other records, the overall likelihood of a match or duplicate, as indicated by the degree of similarity between values, is modified based on the determined weights associated with the respective attributes.

Abstract: A self-learning computer-based system has access to multiple runtime modules that are each capable of performing a particular algorithm. Each runtime module implements the algorithm with different code or runs in a different runtime environment. The system responds to a request to run the algorithm by selecting the runtime module or runtime environment that the system predicts will provide the most desirable results based on parameters like accuracy, performance, cost, resource-efficiency, or policy compliance. The system learns how to make such predictions through training sessions conducted by a machine-learning component. This training teaches the system that previous module selections produced certain types of results in the presence of certain conditions. After determining whether similar conditions currently exist, the system uses rules inferred from the training sessions to select the runtime module most likely to produce desired results.

Abstract: A method, computer system, and a computer program product for data pipeline prioritization is provided. Embodiments may include receiving, by a cognitive rules engine, one or more data pipelines. Embodiments may then include analyzing, using a computational method of the cognitive rules engine, the one or more data pipelines. Embodiments may lastly include prioritizing the one or more data pipelines based on a result of the computational method of the cognitive rules engine.

Abstract: A method, system and computer program product for finding groups of potential duplicates in attribute values. Each attribute value of the attribute values is converted to a respective set of bigrams. All bigrams present in the attribute values may be determined. Bigrams present in the attribute values may be represented as bits. This may result in a bitmap representing the presence of the bigrams in the attribute values. The attribute values may be grouped using bitwise operations on the bitmap, where each group includes attribute values that are determined based on pairwise bigram-based similarity scores. The pairwise bigram-based similarity score reflects the number of common bigrams between two attribute values.

Abstract: A method, system and computer program product for finding groups of potential duplicates in attribute values. Each attribute value of the attribute values is converted to a respective set of bigrams. All bigrams present in the attribute values may be determined. Bigrams present in the attribute values may be represented as bits. This may result in a bitmap representing the presence of the bigrams in the attribute values. The attribute values may be grouped using bitwise operations on the bitmap, where each group includes attribute values that are determined based on pairwise bigram-based similarity scores. The pairwise bigram-based similarity score reflects the number of common bigrams between two attribute values.