Abstract: A method includes determining, by a controller, a portion of data that is selected by a user. The portion of data includes source data that is to be transformed by at least one shaping function. The method also includes generating, by the controller, a first output recommendation data that communicates at least one recommended shaping function to apply to the portion of data. The first output recommendation data is generated based on patterns of shaping functions that have been previously chosen. The patterns of shaping functions that have been previously chosen can be chosen by a plurality of system users. The method also includes determining whether to apply the at least one recommended shaping function to the portion of data. The method also includes applying the at least one recommended shaping function based on the determining.

Abstract: Determination of a degree of similarity among and between a set of text notation schema instances. One type of text notation schema instance is the JSON type. In some embodiments, the degree of similarity is expressed as a schema variance value which is determined by individually comparing the schema instances of the set of text notation schema instances to a representative majority schema. Also, determining a quality of a data source associated with the plurality of text notation schema instances based, at least in part, upon the similarity value.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for enforcing governance policies. In response to comparing an estimated confidence and a confidence boundary for data in a data source, a data governance policy for the data source is enforced by re-profiling the data source.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving a plurality of assets from a data catalog and a respective plurality of classifications applied to each asset in the data catalog, extracting, for a plurality of features, feature data from the plurality of assets and the plurality of asset classifications, generating a feature vector based on the extracted feature data; and generating, by a machine learning (ML) algorithm and based on the feature vector, a first classification rule specifying a condition for applying a first classification of the plurality of classifications to a first asset of the plurality of assets.

Abstract: Systems, methods, and computer program products to perform an operation comprising receiving a plurality of rules associated with a first seat in a mass-transit vehicle, determining a plurality of physical attributes for a first passenger, of a plurality of passengers, based on data describing the first passenger received from a plurality of data sources, determining a plurality of non-physical attributes for the first passenger based on the received data describing the first passenger, determining that at least one of the plurality of physical attributes and the plurality of non-physical attributes of the first passenger satisfy each rule in the set of rules associated with the first seat, computing, based on a machine learning (ML) model a score for the first passenger, determining that the score exceeds a threshold score associated with the first seat, and allocating the first passenger to the first seat in the mass-transit vehicle.

Abstract: An approach for joining operations on document-oriented databases. The approach consists of receiving database identifiers, common attributes and results attributes for core and target databases being joined. Common attributes are searched for in the databases. The searches performed include string, expansive, character and nested. Common attribute join conflicts are identified and input is received to resolve attribute join conflicts. Resolved join conflicts are updated in a join substitution database for subsequent use and joined data results are output to virtual table(s).

Abstract: A computing system is configured to process at least one voice input of a user and output an audio result. The computing system comprises at least one microphone configured to receive the voice input from the user. The computing system further comprises at least one processor configured to at least interpret the voice input and generate the audio result. The computing system also comprises an output speaker device configured to output the audio result. The audio result includes a description of whether one or more behavioral health states associated with the user have been detected.

Abstract: A computing system is configured to process at least one voice input of a user and output an audio result. The computing system comprises at least one microphone configured to receive the voice input from the user. The computing system further comprises at least one processor configured to at least interpret the voice input and generate the audio result. The computing system also comprises an output speaker device configured to output the audio result. The audio result includes a description of whether one or more behavioral health states associated with the user have been detected.

Abstract: A computer-implemented method includes inserting a bloom filter creation stage after an inner data source identification stage, wherein a join operation is to be performed to join an outer data source with the inner data source. The method inserts a bloom filter search stage after an outer data source identification stage, wherein each row of data from the outer data source is searched against a bloom filter for the inner data source during the bloom filter search stage. The method initializes a read on the inner data source. Subsequent to determining the bloom filter creation stage is complete, the method initializes a read on the outer data source. The method performs the join operation at a join stage.

Abstract: A source dataset is enriched by standardization of address data, date and time analysis, and demographic analysis. The enriched source dataset is used to form one or more distinct clusters that are unique combinations of values for one or more attributes of the enriched source dataset. One or more related datasets are found for each of the clusters, and the related datasets are merged into the enriched source dataset using a distributed join operation, wherein the distributed join allows each row of the source dataset to be joined with a different one of the related datasets, where the different one of the related datasets is closest to the cluster to which the row belongs.

Abstract: A computer-implemented method includes inserting a bloom filter creation stage after an inner data source identification stage, wherein a join operation is to be performed to join an outer data source with the inner data source. The method inserts a bloom filter search stage after an outer data source identification stage, wherein each row of data from the outer data source is searched against a bloom filter for the inner data source during the bloom filter search stage. The method initializes a read on the inner data source. Subsequent to determining the bloom filter creation stage is complete, the method initializes a read on the outer data source. The method performs the join operation at a join stage.

Abstract: A computing system is configured to process at least one voice input of a user and output an audio result. The computing system comprises at least one microphone configured to receive the voice input from the user. The computing system further comprises at least one processor configured to at least interpret the voice input and generate the audio result. The computing system also comprises an output speaker device configured to output the audio result. The audio result includes a description of whether one or more behavioral health states associated with the user have been detected.

Abstract: A computing system is configured to process at least one voice input of a user and output an audio result. The computing system comprises at least one microphone configured to receive the voice input from the user. The computing system further comprises at least one processor configured to at least interpret the voice input and generate the audio result. The computing system also comprises an output speaker device configured to output the audio result. The audio result includes a description of whether one or more behavioral health states associated with the user have been detected.

Abstract: A computer manages methods for utilizing an index to manage access to data in a dataset stored in one or more file locations in an ETL tool by receiving a request to access a dataset associated with one or more file locations, wherein the dataset is stored in the one or more file locations. The computer queries an index for the one or more file locations associated with the dataset, wherein the dataset has another index for data in the dataset. The computer receives the one or more file locations associated with the dataset. The computer determines to cache the request to access the one or more file locations for the dataset until one or more thresholds are met, wherein the cached request is part of a total number of cached requests.

Abstract: A source dataset is enriched by standardization of address data, date and time analysis, and demographic analysis. The enriched source dataset is used to form one or more distinct clusters that are unique combinations of values for one or more attributes of the enriched source dataset. One or more related datasets are found for each of the clusters, and the related datasets are merged into the enriched source dataset using a distributed join operation, wherein the distributed join allows each row of the source dataset to be joined with a different one of the related datasets, where the different one of the related datasets is closest to the cluster to which the row belongs.

Abstract: User-inferred data integration actions within tabular data. A user action with respect to a first portion of tabular data is detected. Examples of user action include a deletion, addition and/or modification in a row, column, cell or a combination thereof. The data integration tool may determine if the user action is a recognized action or a learned action, based on at least one type of the user action and at least one characteristic of the first portion of the tabular data. Suggests to the user an option to replay the recognized action or the learned action on a second portion of the tabular data, wherein the first portion and the second portion have at least one common characteristic. If the user action is neither a recognized action nor a learned action, the data integration tool suggests to the user an option to learn, or store, the user action in memory.

Abstract: An approach for outage avoidance. The approach receives a scheduled task, wherein the scheduled task is completed at a scheduled time by a device. The approach receives an outage time of a service disruption, wherein the service disruption occurs at the outage time. The approach determines whether the service disruption conflicts with the scheduled task, based on the outage time and the scheduled time. Responsive to a determination that the service disruption conflicts with the scheduled task, the approach modifies the scheduled task to be completed at a modified time, wherein the outage time does not conflict with the modified time.

Abstract: An approach for outage avoidance. The approach receives a scheduled task, wherein the scheduled task is completed at a scheduled time by a device. The approach receives an outage time of a service disruption, wherein the service disruption occurs at the outage time. The approach determines whether the service disruption conflicts with the scheduled task, based on the outage time and the scheduled time. Responsive to a determination that the service disruption conflicts with the scheduled task, the approach modifies the scheduled task to be completed at a modified time, wherein the outage time does not conflict with the modified time.

Abstract: Communications systems including multiple computer devices where: (i) for a set of computer devices, used by a common user, it is determined which computer device the user is most actively using; and (ii) an operational mode of a “wireless service” (see definition, below) on one of the other computer devices (that is, a non-most-active computer device) is changed in its operational mode. For example, if a user has two smart phones and the user's first smart phone is determined to be more active than the second smart phone, then on the second smart phone (that is, the non-most-active computer device) a social media home page updating service is turned off. In some embodiments, the change from first operational mode to second operational mode will mean that wireless transceiver hardware (such as an antenna) is turned off for the non-most-active device(s).

Abstract: Communications systems including multiple computer devices where: (i) for a set of computer devices, used by a common user, it is determined which computer device the user is most actively using; and (ii) an operational mode of a “wireless service” (see definition, below) on one of the other computer devices (that is, a non-most-active computer device) is changed in its operational mode. For example, if a user has two smart phones and the user's first smart phone is determined to be more active than the second smart phone, then on the second smart phone (that is, the non-most-active computer device) a social media home page updating service is turned off. In some embodiments, the change from first operational mode to second operational mode will mean that wireless transceiver hardware (such as an antenna) is turned off for the non-most-active device(s).