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: Techniques are disclosed for for prioritizing a plurality of clusters. Prioritizing clusters may generally include identifying a scoring strategy for prioritizing the plurality of clusters. Each cluster is generated from a seed and stores a collection of data retrieved using the seed. For each cluster, elements of the collection of data stored by the cluster are evaluated according to the scoring strategy and a score is assigned to the cluster based on the evaluation. The clusters may be ranked according to the respective scores assigned to the plurality of clusters. The collection of data stored by each cluster may include financial data evaluated by the scoring strategy for a risk of fraud. The score assigned to each cluster may correspond to an amount at risk.

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: 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: The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable data analysts to quickly and efficiently explore large volume data sources. In particular, a data analysis system, such as described herein, may provide features to enable the data analyst to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the data analyst over the course of an investigation, as captured via the data analyst's queries and user interaction with the user interfaces provided by the data analysis system. Data analysis paths may involve exploration of high volume data sets, such as Internet proxy data, which may include trillions of rows of data. The data analyst may pursue a data analysis path that involves, among other things, applying filters, joining to other tables in a database, viewing interactive data visualizations, and so on.

Abstract: Techniques are disclosed for for prioritizing a plurality of clusters. Prioritizing clusters may generally include identifying a scoring strategy for prioritizing the plurality of clusters. Each cluster is generated from a seed and stores a collection of data retrieved using the seed. For each cluster, elements of the collection of data stored by the cluster are evaluated according to the scoring strategy and a score is assigned to the cluster based on the evaluation. The clusters may be ranked according to the respective scores assigned to the plurality of clusters. The collection of data stored by each cluster may include financial data evaluated by the scoring strategy for a risk of fraud. The score assigned to each cluster may correspond to an amount at risk.

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: 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: The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable data analysts to quickly and efficiently explore large volume data sources. In particular, a data analysis system, such as described herein, may provide features to enable the data analyst to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the data analyst over the course of an investigation, as captured via the data analyst's queries and user interaction with the user interfaces provided by the data analysis system. Data analysis paths may involve exploration of high volume data sets, such as Internet proxy data, which may include trillions of rows of data. The data analyst may pursue a data analysis path that involves, among other things, applying filters, joining to other tables in a database, viewing interactive data visualizations, and so on.

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: 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: 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: 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: 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 analyzes (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: 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: The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable data analysts to quickly and efficiently explore large volume data sources. In particular, a data analysis system, such as described herein, may provide features to enable the data analyst to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the data analyst over the course of an investigation, as captured via the data analyst's queries and user interaction with the user interfaces provided by the data analysis system. Data analysis paths may involve exploration of high volume data sets, such as Internet proxy data, which may include trillions of rows of data. The data analyst may pursue a data analysis path that involves, among other things, applying filters, joining to other tables in a database, viewing interactive data visualizations, and so on.

Abstract: The systems and methods described herein provide highly dynamic and interactive data analysis user interfaces which enable data analysts to quickly and efficiently explore large volume data sources. In particular, a data analysis system, such as described herein, may provide features to enable the data analyst to investigate large volumes of data over many different paths of analysis while maintaining detailed and retraceable steps taken by the data analyst over the course of an investigation, as captured via the data analyst's queries and user interaction with the user interfaces provided by the data analysis system. Data analysis paths may involve exploration of high volume data sets, such as Internet proxy data, which may include trillions of rows of data. The data analyst may pursue a data analysis path that involves, among other things, applying filters, joining to other tables in a database, viewing interactive data visualizations, and so on.