Abstract: A computer-implemented system includes identifying a target hierarchical taxonomy comprising a plurality of distinct hierarchical taxonomy categories; extracting a plurality of distinct taxonomy tokens from the plurality of distinct hierarchical taxonomy categories; computing a taxonomy vector corpus based on the plurality of distinct taxonomy tokens; computing a plurality of distinct taxonomy clusters based on an input of the taxonomy vector corpus; constructing a hierarchical taxonomy classifier based on the plurality of distinct taxonomy clusters; converting a volume of unlabeled structured datasets to a plurality of distinct corpora of taxonomy-labeled structured datasets based on the hierarchical taxonomy classifier; and outputting at least one corpus of taxonomy-labeled structured datasets of the plurality of distinct corpora of taxonomy-labeled structured datasets based on an input of a data classification query.

Abstract: A system, method, and computer-program product includes identifying a target hierarchical taxonomy that includes a plurality of distinct hierarchical taxonomy categories, extracting a plurality of distinct taxonomy tokens from the plurality of distinct hierarchical taxonomy categories, computing a taxonomy vector corpus based on the plurality of distinct taxonomy tokens, computing a plurality of distinct taxonomy clusters based on an input of the taxonomy vector corpus, constructing a hierarchical taxonomy classifier based on the plurality of distinct taxonomy clusters, converting a volume of unlabeled structured datasets to a plurality of distinct corpora of taxonomy-labeled structured datasets based on the hierarchical taxonomy classifier, and outputting at least one corpus of taxonomy-labeled structured datasets of the plurality of distinct corpora of taxonomy-labeled structured datasets based on an input of a data classification query.

Abstract: A computer-implemented system includes identifying a target hierarchical taxonomy comprising a plurality of distinct hierarchical taxonomy categories; extracting a plurality of distinct taxonomy tokens from the plurality of distinct hierarchical taxonomy categories; computing a taxonomy vector corpus based on the plurality of distinct taxonomy tokens; computing a plurality of distinct taxonomy clusters based on an input of the taxonomy vector corpus; constructing a hierarchical taxonomy classifier based on the plurality of distinct taxonomy clusters; converting a volume of unlabeled structured datasets to a plurality of distinct corpora of taxonomy-labeled structured datasets based on the hierarchical taxonomy classifier; and outputting at least one corpus of taxonomy-labeled structured datasets of the plurality of distinct corpora of taxonomy-labeled structured datasets based on an input of a data classification query.

Abstract: An apparatus includes processor(s) to: receive a request for a data catalog; in response to the request specifying a structural feature, analyze metadata of multiple data sets for an indication of including it, and to retrieve an indicated degree of certainty of detecting it for data sets including it; in response to the request specifying a contextual aspect, analyze context data of the multiple data sets for an indication of being subject to it, and to retrieve an indicated degree of certainty concerning it for data sets subject to it; selectively include each data set in the data catalog based on the request specifying a structural feature and/or a contextual aspect, and whether each data set meets what is specified; for each data set in the data catalog, generate a score indicative of the likelihood of meeting what is specified; and transmit the data catalog to the requesting device.

Abstract: Computerized pipelines can transform input data into data structures compatible with models in some examples. In one such example, a system can obtain a first table that includes first data referencing a set of subjects. The system can then execute a sequence of processing operations on the first data in a particular order defined by a data-processing pipeline to modify an analysis table to include features associated with the set of subjects. Executing each respective processing operation in the sequence to generate the modified analysis table may involve: deriving a respective set of features from the first data by executing a respective feature-extraction operation on the first data; and adding the respective set of features to the analysis table. The system may then execute a predictive model on the modified analysis table for generating a predicted value based on the modified analysis table.

Abstract: Computerized pipelines can transform input data into data structures compatible with models in some examples. In one such example, a system can obtain a first table that includes first data referencing a set of subjects. The system can then execute a sequence of processing operations on the first data in a particular order defined by a data-processing pipeline to modify an analysis table to include features associated with the set of subjects. Executing each respective processing operation in the sequence to generate the modified analysis table may involve: deriving a respective set of features from the first data by executing a respective feature-extraction operation on the first data; and adding the respective set of features to the analysis table. The system may then execute a predictive model on the modified analysis table for generating a predicted value based on the modified analysis table.

Abstract: An apparatus includes processor(s) to: receive a request for a data catalog; in response to the request specifying a structural feature, analyze metadata of multiple data sets for an indication of including it, and to retrieve an indicated degree of certainty of detecting it for data sets including it; in response to the request specifying a contextual aspect, analyze context data of the multiple data sets for an indication of being subject to it, and to retrieve an indicated degree of certainty concerning it for data sets subject to it; selectively include each data set in the data catalog based on the request specifying a structural feature and/or a contextual aspect, and whether each data set meets what is specified; for each data set in the data catalog, generate a score indicative of the likelihood of meeting what is specified; and transmit the data catalog to the requesting device.

Abstract: An apparatus includes a processor to: provide a set of feature routines to a set of processor cores to detect features of a data set distributed thereamong; generate metadata indicative of the detected features; generate context data indicative of contextual aspects of the data set; provide the metadata and context data to each processor core, and distribute a set of suggestion models thereamong to enable derivation of a suggested subset of data preparation operations to be suggested to be performed on the data set; transmit indications of the suggested subset to a viewing device, and receive therefrom indications of a selected subset of data preparation operations selected to be performed; compare the selected and suggested subsets; and in response to differences therebetween, re-train at least one suggestion model of the set of suggestion models based at least on the combination of the metadata, context data and selected subset.

Abstract: An apparatus includes a processor to: provide a set of feature routines to a set of processor cores to detect features of a data set distributed thereamong; generate metadata indicative of the detected features; generate context data indicative of contextual aspects of the data set; provide the metadata and context data to each processor core, and distribute a set of suggestion models thereamong to enable derivation of a suggested subset of data preparation operations to be suggested to be performed on the data set; transmit indications of the suggested subset to a viewing device, and receive therefrom indications of a selected subset of data preparation operations selected to be performed; compare the selected and suggested subsets; and in response to differences therebetween, re-train at least one suggestion model of the set of suggestion models based at least on the combination of the metadata, context data and selected subset.

Abstract: An apparatus includes a processor to: provide a set of feature routines to a set of processor cores to detect features of a data set distributed thereamong; generate metadata indicative of the detected features; generate context data indicative of contextual aspects of the data set; provide the metadata and context data to each processor core, and distribute a set of suggestion models thereamong to enable derivation of a suggested subset of data preparation operations to be suggested to be performed on the data set; transmit indications of the suggested subset to a viewing device, and receive therefrom indications of a selected subset of data preparation operations selected to be performed; compare the selected and suggested subsets; and in response to differences therebetween, re-train at least one suggestion model of the set of suggestion models based at least on the combination of the metadata, context data and selected subset.

Abstract: An apparatus includes a processor to: receive multiple normalized metadata portions based on metadata portions originating from vendor devices storing data sets of a distributed online library system; compare the multiple pieces of information between pairs of normalized metadata portions to identify at least one pair of identical portions of data; analyze the pieces of information of normalized metadata portions corresponding to an identified pair of identical portions of data to determine if there is a dependency relationship between each portion of data of the pair and another identical portion of data stored within another device; and in response to there being such a pair of dependency relationships, generate a visualization that includes a combination of graphical elements depicting the pair of dependency relationships, and transmit the visualization to the client device to enable a visual presentation of the visualization.

Abstract: An apparatus includes a processor to: receive multiple normalized metadata portions based on metadata portions originating from vendor devices storing data sets of a distributed online library system; compare the multiple pieces of information between pairs of normalized metadata portions to identify at least one pair of identical portions of data; analyze the pieces of information of normalized metadata portions corresponding to an identified pair of identical portions of data to determine if there is a dependency relationship between each portion of data of the pair and another identical portion of data stored within another device; and in response to there being such a pair of dependency relationships, generate a visualization that includes a combination of graphical elements depicting the pair of dependency relationships, and transmit the visualization to the client device to enable a visual presentation of the visualization.

Abstract: An apparatus may include a processor and storage to store instructions that cause the processor to perform operations including: generate a current data set model descriptive of a characteristic of a current data set; compare the current data set model to at least one previously generated data set model descriptive of a characteristic of a previously analyzed data set; in response to detection of a match within a similarity threshold: retrieve an indication from a correlation database of an action previously performed on a previously analyzed data set; select a computer language based on node data descriptive of characteristics of a node device execution environment; generate node instructions in the selected computer language and based on the current data set model to cause the node device to perform the previously performed action on a portion of the current data set; and transmit the node instructions to the node device.

Abstract: An apparatus may include a processor and storage to store instructions that cause the processor to perform operations including: generate a current data set model descriptive of a characteristic of a current data set; compare the current data set model to at least one previously generated data set model descriptive of a characteristic of a previously analyzed data set; in response to detection of a match within a similarity threshold: retrieve an indication from a correlation database of an action previously performed on a previously analyzed data set; select a computer language based on node data descriptive of characteristics of a node device execution environment; generate node instructions in the selected computer language and based on the current data set model to cause the node device to perform the previously performed action on a portion of the current data set; and transmit the node instructions to the node device.

Abstract: Computer-implemented systems and methods are disclosed for specifying an Extract, Transform, Load (ETL) process. For example, systems and methods are provided for specifying the ETL process through ETL diagrams, and allowing ETL diagrams with different components to be easily traversed. Behaviors of underlying engine technology of the ETL process may be encapsulated so that users may build a job or data flow of the ETL process without knowing specific details of the underlying engine technology.

Abstract: Systems and methods for impact analysis across multiple domains using non-data types of relationships between objects are provided. A data model can be formed. The data model can include objects representative of physical data in separate domains and relationships of non-data types between the objects. An impact analysis interface can be generated using the data model. The impact analysis interface can depict the objects and the non-data types of relationships between the objects.

Abstract: Systems and methods for impact analysis across multiple domains using non-data types of relationships between objects are provided. A data model can be formed. The data model can include objects representative of physical data in separate domains and relationships of non-data types between the objects. An impact analysis interface can be generated using the data model. The impact analysis interface can depict the objects and the non-data types of relationships between the objects.

Abstract: A computer-implemented graphical user interface system for generating a database query includes a create region, a plurality of clause-specific regions and a navigation region. The create region may be used to receive a plurality of graphical icons and to create an association between the plurality of graphical icons to define the database query. The plurality of graphical icons may include a plurality of clause-specific icons that identify clauses for the database query and a plurality of table icons that identify sets of data in the database. The plurality of clause-specific interface regions are each associated with a particular clause-specific icon and may be used to receive user input to define one or more parameters associated with the particular clause-specific icon. The navigation region may be used to receive user input to select between the create region and the plurality of clause-specific interface regions.

Abstract: Provided is a computer system for translating business information and associating the translated business information with user-defined business terms in a processor executable data management system. The computer system comprises a computer readable storage media for providing a data store and a data management system for execution on one or more data processors for storing information relating to a plurality of user-generated business terms in accordance with a schema. The data management system also includes a user interface engine configured to provide a plurality of user interfaces.

Abstract: Provided is a computer system for translating business information and associating the translated business information with user-defined business terms in a processor executable data management system. The computer system comprises a computer readable storage media for providing a data store and a data management system for execution on one or more data processors for storing information relating to a plurality of user-generated business terms in accordance with a schema. The data management system also includes a user interface engine configured to provide a plurality of user interfaces.