Abstract: An approach is disclosed that receives a code segment from a source code file. The received code segment is inputted to a trained knowledge manager from which one or more comparable code segments are received. Each of the comparable code segments are identified as having similar functionality as the selected code segment and each of the comparable code segments has a set of metadata. The metadata is used to select one of the comparable code segments and this selected code segment is used to replace the original code segment in the source code file.

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: An approach is provided for completing a pipeline graph. Using a deep learning based sequence model, an initial data pipeline having a sequence of nodes is generated. Mismatch(es) between data formats required by input and output in the sequence of nodes is identified. Virtual gap node(s) that correct the mismatch(es) are added to the initial data pipeline. For a given virtual gap node, tentative graph structures are determined using knowledge graphs and a crowd sourced validation system. Reuse forecast scores and performance scores for the tentative graph structures are calculated. Based on the reuse forecast scores and the performance scores, a final graph structure for implementing the given virtual gap node is determined.

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: A method includes determining a presence of one or more people around a user and determining, based on the presence of the one or more people, an exposure level. The method also includes receiving a message for the user, the message comprising a first portion and a second portion and removing, based on the exposure level, the first and second portions from the message to produce a first message. The method further includes removing, based on the exposure level, the first portion from the message to produce a second message and presenting, based on the exposure level, the first message to the user. The method also includes receiving, from the user, feedback about the first message and presenting the second message to the user in response to the feedback.

Abstract: A database of deployed configurations, as well as attempted configurations that failed is maintained and used as reference to compare against configurations of attempted software deployments. Upon detecting a failed deployment, disclosed embodiments search the database for working configurations that most closely resemble the failed configuration, and rank the configurations based on various criteria. Disclosed embodiments may then automatically select a highest ranked working configuration, and perform an automatic upgrade of the necessary components to create a working configuration.

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: 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: A method, the method comprising retrieving a sound intensity map for a venue, wherein the sound intensity map is divided up into a plurality of regions, wherein the sound intensity map predicts a sound quality for each region during a current event. Receiving data from a plurality of IOT enabled operated aerial vehicles, where each IOT enabled operated aerial vehicle of the plurality of IOT enabled operated aerial vehicles travels around different regions of the plurality of regions, wherein each IOT enabled operated aerial vehicle collects data during the event. Comparing the received data to the sound intensity map to determine the region where an audio component of a venue audio needs to be adjusted. Determining the adjustment required for the audio component and adjusting the audio equipment.

Abstract: Techniques are described relating to automatic data standardization in a managed services domain of a cloud computing environment. An associated computer-implemented method includes receiving a dataset during a data onboarding procedure and classifying datapoints within the dataset. The method further includes applying a machine learning data standardization model to each classified datapoint within the dataset and deriving a proposed set of data standardization rules for the dataset based upon any standardization modification determined consequent to model application. Optionally, the method includes presenting the proposed set of data standardization rules for client review and, responsive to acceptance of the proposed set of data standardization rules, applying the proposed set of data standardization rules to the dataset. The method further includes, responsive to acceptance of the proposed set of data standardization rules, updating the machine learning data standardization model accordingly.

Abstract: A method for generating data standardization rules includes receiving a training data set containing tokenized and tagged data values. A set of machine mining models is built using different learning algorithms for identifying tags and tag patterns using the training set. For each data value in a further data set: a tokenization is applied on the data value, resulting in a set of tokens. For each token of the set of tokens one or more tag candidates are determined using a lookup dictionary of tags and tokens and/or at least part of the set of machine mining models, resulting for each token of the set of tokens in a list of possible tags. Unique combinations of the sets of tags of the further data set having highest aggregated confidence values are provided for use as standardization rules.

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 computer-implemented method applies insights from a variety of data sources to each of the data sources. The method includes identifying a set of data sources, wherein each of the data sources are associated with a domain. The method includes analyzing documentation for each of the data sources. The method further includes extracting a set of attributes for each data source, and determining a data schema associated with each data source. The method includes mapping each data schema to a common domain schema. The method also includes linking, based on the mapping and on the set of attributes for each data source, common features across each data source. The method includes generating, in response to the linking, a knowledge graph. The method further includes preparing a visual display for a set of domain insights; and forking the set of domain insights into a first data source.

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, 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 presence of a secondary device is detected by a primary device. The primary device is operating in a first configuration. A current placement of the secondary device is identified by the primary device. Based on the current placement of the secondary device a list of potential future placements of the secondary device is determined by the primary device. The list of potential future placements of the secondary device is prioritized by the primary device. A first candidate future placement of the secondary device is selected based on the list by the primary device.

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, system and computer program product for determining a data standardization score for an attribute of a dataset. A data standardization score is calculated, which reflects whether data quality of attribute values would increase if a standardization rule is applied to the attribute values. Based on attribute metadata, it may be determined whether an indication to carry or not to carry out standardization is available for at least part of the attribute values of the dataset. In response to finding the indication, a respective value may be set for the data standardization score. In response to not finding the indication, a data standardization score algorithm may be run on the at least part of the attribute values of the dataset. The data standardization score value may be compared to a predefined criterion to determine whether data standardization is to be applied on the attribute.

Abstract: A method, system and computer program product for determining a data standardization score for an attribute of a dataset. A data standardization score is calculated, which reflects whether data quality of attribute values would increase if a standardization rule is applied to the attribute values. Based on attribute metadata, it may be determined whether an indication to carry or not to carry out standardization is available for at least part of the attribute values of the dataset. In response to finding the indication, a respective value may be set for the data standardization score. In response to not finding the indication, a data standardization score algorithm may be run on the at least part of the attribute values of the dataset. The data standardization score value may be compared to a predefined criterion to determine whether data standardization is to be applied on the attribute.

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.