Abstract: A method comprises storing first transaction data for a first transaction producing a first version of a first dataset, second transaction data for a second transaction transforming a first version of the first dataset to a second version of the first dataset, and third transaction data for a third transaction transforming the second version of the first dataset to a third version of the first dataset; storing dependency information indicating a first dependency of the third transaction on the second transaction and a second dependency of the second transaction on the first transaction; receiving a first instruction to revoke a first permission of a first user to access the second version of the first dataset; automatically revoking a certain permission of the first user to access the third version of the first dataset based on the dependency information.

Abstract: A method comprises creating metadata identifying columns of tables and column operations of one or more data transforms of the columns in a data pipeline and including links to code segments in human-readable form corresponding to the one or more data transforms; executing a build job that effects the one or more data transforms on one or more datasets to generate one or more derived datasets; causing, after the executing, a presentation of a graphical user interface (GUI) including a graphical representation of the one or more data transforms based on the metadata, wherein the method is performed by one or more processors.

Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.

Abstract: Systems and methods are provided for creating and managing a data integration workspace. The workspace may comprise one or more views of data (or datasets) stored in or accessible by the system. Models may be generated and updated based on the plurality of datasets and presented via a graphical user interface. Feedback received via a graphical user interface presenting a model may be used to annotate an underlying dataset associated with the model. Responsive to a modification of the underlying dataset or the rules for using the underlying dataset to generate the model, other related datasets and/or models may be automatically updated accordingly. Templates associated with one or more types of users may be defined. Each template may comprise one or more specific models related to a specific type of user.

Abstract: In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

Abstract: A computer-implemented system and method for data revision control in a large-scale data analytic systems. In one embodiment, for example, a computer-implemented method comprises the operations of storing a first version of a dataset that is derived by executing a first version of driver program associated with the dataset; and storing a first build catalog entry comprising an identifier of the first version of the dataset and comprising an identifier of the first version of the driver program.

Abstract: The programming notebook system, methods, and user interfaces described herein provide software developers with enhanced tools by which a programming notebook workflow and session history associated with code cells in a programming notebook may be tracked and maintained. As a developer progresses through a development workflow, the developer can select an option to save a program code card representing some or all of the program code cell inputs. A card editor user interface may present an aggregated listing of all program code the developer has provided across multiple code cells during the current session which the developer can edit, refine, and/or comment. The card editor may also allow the developer to add associated user interface code to display a UI component associated with the program code card, and allow the developer to add a description and tags for the card so that the card can be searched for and reused.

Abstract: Computing systems methods, and non-transitory storage media are provided for retrieving information regarding an operation to be performed by a platform, performing a preliminary validation of the operation, generating details regarding the preliminary validation, transmitting at least a subset of the details of the preliminary validation to the platform, and populating the generated details on an interface. If the preliminary validation fails, the platform refrains from performing the operation. Furthermore, the logic describing the operation can be executed on different platforms and is not bound or limited to one platform.

Abstract: Techniques for propagation of deletion operations among a plurality of related datasets are described herein.

Abstract: Systems and methods are provided for creating and managing a data integration workspace. The workspace may comprise one or more views of data (or datasets) stored in or accessible by the system. Models may be generated and updated based on the plurality of datasets and presented via a graphical user interface. Feedback received via a graphical user interface presenting a model may be used to annotate an underlying dataset associated with the model. Responsive to a modification of the underlying dataset or the rules for using the underlying dataset to generate the model, other related datasets and/or models may be automatically updated accordingly. Templates associated with one or more types of users may be defined. Each template may comprise one or more specific models related to a specific type of user.

Abstract: A resource dependency system displays two dynamically interactive interfaces in a resource dependency user interface, a hierarchical resource repository and a dependency graph user interface. User interactions on each interface can dynamically update either interface. For example, a selection of a particular resource in the dependency graph user interface causes the system to update the dependency graph user interface to indicate the selection and also updates the hierarchical resource repository to navigate to the appropriate folder corresponding to the stored location of the selected resource. In another example, a selection of a particular resource in the hierarchical resource repository causes the system to update the hierarchical resource repository to indicate the selection and also updates the dependency graph user interface to display an updated graph, indicate the selection and, in some embodiments, focus on the selected resource by zooming into a portion of the graph.

Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.

Abstract: Systems, methods, and non-transitory computer readable media are provided for providing parameterized states. Resource customization information may be obtained. The resource customization information may identify a resource and define a customized view of the resource. A parameterized state may be generated based on the resource customization information. Activation of the parameterized state may cause the customized view of the resource to be presented. An interface through which the parameterized state is accessible may be provided.

Abstract: A resource dependency system displays two dynamically interactive interfaces in a resource dependency user interface, a hierarchical resource repository and a dependency graph user interface. User interactions on each interface can dynamically update either interface. For example, a selection of a particular resource in the dependency graph user interface causes the system to update the dependency graph user interface to indicate the selection and also updates the hierarchical resource repository to navigate to the appropriate folder corresponding to the stored location of the selected resource. In another example, a selection of a particular resource in the hierarchical resource repository causes the system to update the hierarchical resource repository to indicate the selection and also updates the dependency graph user interface to display an updated graph, indicate the selection and, in some embodiments, focus on the selected resource by zooming into a portion of the graph.

Abstract: Embodiments of the present disclosure relate to a data analysis system that may automatically generate memory-efficient clustered data structures, automatically analyze those clustered data structures, and provide results of the automated analysis in an optimized way to an analyst. The automated analysis of the clustered data structures (also referred to herein as data clusters) may include an automated application of various criteria or rules so as to generate a compact, human-readable analysis of the data clusters. The human-readable analyses (also referred to herein as “summaries” or “conclusions”) of the data clusters may be organized into an interactive user interface so as to enable an analyst to quickly navigate among information associated with various data clusters and efficiently evaluate those data clusters in the context of, for example, a fraud investigation. Embodiments of the present disclosure also relate to automated scoring of the clustered data structures.

Abstract: Techniques for propagation of deletion operations among a plurality of related datasets are described herein.

Abstract: In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.

Abstract: In various embodiments, systems, methods, and techniques are disclosed for generating a collection of clusters of related data from a seed. Seeds may be generated based on seed generation strategies or rules. Clusters may be generated by, for example, retrieving a seed, adding the seed to a first cluster, retrieving a clustering strategy or rules, and adding related data and/or data entities to the cluster based on the clustering strategy. Various cluster scores may be generated based on attributes of data in a given cluster. Further, cluster metascores may be generated based on various cluster scores associated with a cluster. Clusters may be ranked based on cluster metascores. Various embodiments may enable an analyst to discover various insights related to data clusters, and may be applicable to various tasks including, for example, tax fraud detection, beaconing malware detection, malware user-agent detection, and/or activity trend detection, among various others.