Abstract: Systems, methods, and non-transitory computer-readable media are provided for data analysis. A user interface comprising boards corresponding to one or more objects and one or more operations on the input and/or output objects of the boards can be generated for high-scale top-down data analysis.

Abstract: The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable the data analyst to quickly and efficiently explore large volume data sources, such as computer code or intellectual system. A user interface can be implanted that includes a plurality of nodes associated with portions of a process. The nodes may include indications of logical relationships between nodes. Node identifiers may be associated with particular nodes and be usable to select particular nodes. Reliability scores associated with particular nodes can be included.

Abstract: In some embodiments, systems and methods for visualizing one or more datasets include importing a plurality of root objects, each root object including linked data attributes and obtaining a joined dataset based on the plurality of root objects, that includes for each or the plurality of root objects, a plurality of rows of related attribute data linked to each root object as a result of a join operation. The systems and methods perform an aggregation computation on the plurality of rows of related attribute data corresponding to each of the plurality of root objects to produce a corresponding single aggregation row of consolidated data for each root object and present a user interface that shows each of the plurality of root objects with their corresponding single aggregation row of consolidated data resulting from the aggregation computation, in a one-to-one manner.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for data analysis. A user interface comprising boards corresponding to one or more objects and one or more operations on the input and/or output objects of the boards can be generated for high-scale top-down data analysis.

Abstract: In some embodiments, systems and methods for visualizing one or more datasets include importing a plurality of root objects, each root object including linked data attributes and obtaining a joined dataset based on the plurality of root objects, that includes for each or the plurality of root objects, a plurality of rows of related attribute data linked to each root object as a result of a join operation. The systems and methods perform an aggregation computation on the plurality of rows of related attribute data corresponding to each of the plurality of root objects to produce a corresponding single aggregation row of consolidated data for each root object and present a user interface that shows each of the plurality of root objects with their corresponding single aggregation row of consolidated data resulting from the aggregation computation, in a one-to-one manner.

Abstract: The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable the data analyst to quickly and efficiently explore large volume data sources, such as computer code or intellectual system. A user interface can be implanted that includes a plurality of nodes associated with portions of a process. The nodes may include indications of logical relationships between nodes. Node identifiers may be associated with particular nodes and be usable to select particular nodes. Reliability scores associated with particular nodes can be included.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for data analysis. A user interface comprising boards corresponding to one or more objects and one or more operations on the input and/or output objects of the boards can be generated for high-scale top-down data analysis.

Abstract: The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable the data analyst to quickly and efficiently explore large volume data sources, such as computer code or intellectual system. A user interface can be implanted that includes a plurality of nodes associated with portions of a process. The nodes may include indications of logical relationships between nodes. Node identifiers may be associated with particular nodes and be usable to select particular nodes. Reliability scores associated with particular nodes can be included.

Abstract: A resource dependency system dynamically determines and generates for display a minimized and collapsed resource dependency toolbar using two or more indicators to display a summarized view of dependency relationships to one or more selected items. For example, the system can analyze a resource dependency graph and determine root items, or items that do not depend on other items but are depended on by a selected item. The system can also determine leaf items, which no other items depend on. The system can also determine intermediary items that depend on root items and/or leaf items. Then, based on preconfigured instructions, the system can group the root, leaf, and intermediary items into two or more indicators and display the indicators on a graphical user interface conveying information about the selected item and how it is related to other items.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for data analysis. A user interface comprising boards corresponding to one or more objects and one or more operations on the input and/or output objects of the boards can be generated for high-scale top-down data analysis.

Abstract: Systems, methods, and non-transitory computer-readable media are provided for data analysis. A user interface comprising boards corresponding to one or more objects and one or more operations on the input and/or output objects of the boards can be generated for high-scale top-down data analysis.

Abstract: A resource dependency system dynamically determines and generates for display a minimized and collapsed resource dependency toolbar using two or more indicators to display a summarized view of dependency relationships to one or more selected items. For example, the system can analyze a resource dependency graph and determine root items, or items that do not depend on other items but are depended on by a selected item. The system can also determine leaf items, which no other items depend on. The system can also determine intermediary items that depend on root items and/or leaf items. Then, based on preconfigured instructions, the system can group the root, leaf, and intermediary items into two or more indicators and display the indicators on a graphical user interface conveying information about the selected item and how it is related to other items.

Abstract: A resource dependency system dynamically determines and generates for display a minimized and collapsed resource dependency toolbar using two or more indicators to display a summarized view of dependency relationships to one or more selected items. For example, the system can analyze a resource dependency graph and determine root items, or items that do not depend on other items but are depended on by a selected item. The system can also determine leaf items, which no other items depend on. The system can also determine intermediary items that depend on root items and/or leaf items. Then, based on preconfigured instructions, the system can group the root, leaf, and intermediary items into two or more indicators and display the indicators on a graphical user interface conveying information about the selected item and how it is related to other items.

Abstract: Systems and methods for providing full data provenance visualization for versioned datasets. A method includes receiving selection of a versioned dataset that is within a data pipeline system. The method also includes determining the full data provenance of the selected versioned dataset. The full data provenance may comprise a set of versioned datasets. The method further includes providing for display of a visualization of the full data provenance of the selected versioned dataset. The visualization comprises a graph. The graph comprises a compound node for the selected versioned dataset and for each versioned dataset in the set of versioned datasets. The graph further comprises edges connecting the compounds nodes. Each edge represents a derivation dependency between versions of the versioned datasets represented by the compound nodes connected by the edge.

Abstract: Systems and methods for providing full data provenance visualization for versioned datasets. A method includes receiving selection of a versioned dataset that is within a data pipeline system. The method also includes determining the full data provenance of the selected versioned dataset. The full data provenance may comprise a set of versioned datasets. The method further includes providing for display of a visualization of the full data provenance of the selected versioned dataset. The visualization comprises a graph. The graph comprises a compound node for the selected versioned dataset and for each versioned dataset in the set of versioned datasets. The graph further comprises edges connecting the compounds nodes. Each edge represents a derivation dependency between versions of the versioned datasets represented by the compound nodes connected by the edge.

Abstract: Systems and methods for providing full data provenance visualization for versioned datasets. A method includes receiving selection of a versioned dataset that is within a data pipeline system. The method also includes determining the full data provenance of the selected versioned dataset. The full data provenance may comprise a set of versioned datasets. The method further includes providing for display of a visualization of the full data provenance of the selected versioned dataset. The visualization comprises a graph. The graph comprises a compound node for the selected versioned dataset and for each versioned dataset in the set of versioned datasets. The graph further comprises edges connecting the compounds nodes. Each edge represents a derivation dependency between versions of the versioned datasets represented by the compound nodes connected by the edge.