Abstract: Systems and methods are provided for investigation network activities. Network activity information may be accessed. The network activity information may describe for an individual (1) respective relationship with one or more persons; and (2) respective activity status information indicating whether a given person has engaged in a particular activity. A network activity graph may be generated based on the network activity information. The network activity graph may include two or more nodes representing the individual and the one or more persons. Connections between the nodes may represent the respective relationships between the individual and the one or more persons. Data corresponding to the network activity graph may be presented through an interface.

Abstract: Systems, methods, and apparatus related to memory devices. In one approach, an artificial intelligence system uses a memory device to provide inference results. Image data from a camera is provided to the memory device. The memory device stores the image data received from the camera. The memory device includes dynamic random access memory (DRAM), and static random access memory (SRAM). The memory device also includes a processor to run a neural network. The neural network uses the image data as input. An output from the neural network provides an inference result. In one example, the memory device has a same form factor as a conventional DRAM device. The memory device includes a multiply-accumulate (MAC) engine that supports computations for the neural network.

Abstract: An apparatus, and a method, performed by one or more processors are disclosed. The method receives a build request associated with performing an external data processing task on a first data set, the first data set being stored in memory associated with a data processing platform to be performed at a system external to the data processing platform. The method generates a task identifier for the data processing task, and provides, in association with the task identifier, the first data set to an agent associated with the external system with an indication of the data processing task, the agent being arranged to cause performance of the task at the external system, to receive a second data set resulting from performance of the task, and to provide the second data set and associated metadata indicative of the transformation. The method receives the second data set and metadata from the agent associated with the external system and stores the second data set and associated metadata.

Abstract: An apparatus, and a method, performed by one or more processors are disclosed. The method receives a build request associated with performing an external data processing task on a first data set, the first data set being stored in memory associated with a data processing platform to be performed at a system external to the data processing platform. The method generates a task identifier for the data processing task, and providing, in association with the task identifier, the first data set to an agent associated with the external system with an indication of the data processing task, the agent being arranged to cause performance of the task at the external system, to receive a second data set resulting from performance of the task, and to provide the second data set and associated metadata indicative of the transformation. The method receives the second data set and metadata from the agent associated with the external system and stores the second data set and associated metadata.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: A database system comprised of a decoupled compute layer and storage layer is implemented to store, build, and maintain a canonical dataset, a temporary buffer, and an edits dataset. The canonical dataset is a set of batch updated data. The data is appended in chunks to the canonical dataset such that the canonical dataset becomes a historical dataset over time. The buffer is a write ahead log that contains the most recent chunks of data and provides atomicity and durability for the database system. The edits dataset is the set of data that contains edits such as cell mutations, row appends and/or row deletions. The database system enables users to make cell or row-level edits to tables and observe those edits in analytical systems or downstream builds with minimal latency.

Abstract: A database system comprised of a decoupled compute layer and storage layer is implemented to store, build, and maintain a canonical dataset, a temporary buffer, and an edits dataset. The canonical dataset is a set of batch updated data. The data is appended in chunks to the canonical dataset such that the canonical dataset becomes a historical dataset over time. The buffer is a write ahead log that contains the most recent chunks of data and provides atomicity and durability for the database system. The edits dataset is the set of data that contains edits such as cell mutations, row appends and/or row deletions. The database system enables users to make cell or row-level edits to tables and observe those edits in analytical systems or downstream builds with minimal latency.

Abstract: Computer-implemented systems and methods are disclosed to interface with one or more storage devices storing a plurality of documents, wherein each of the plurality of documents is associated with one or more tags of one or more predefined hierarchies of tags, wherein the one or more hierarchies of tags include multiple dimensions. In accordance with some embodiments, a method is provided to identify one or more documents from the data storage devices. The method comprises acquiring, via an interface, a selection of one or more tags of the one or more predefined hierarchies of tags. The method further comprises identifying one or more documents from the data storage devices in response to the selection, the identified one or more documents having tags that have a relationship with the selected tags, and providing data corresponding to the identified documents for displaying in the interface.

Abstract: An apparatus, and a method, performed by one or more processors are disclosed. The method may comprise receiving a build request associated with performing an external data processing task on a first data set, the first data set being stored in memory associated with a data processing platform to be performed at a system external to the data processing platform. The method may also comprise generating a task identifier for the data processing task, and providing, in association with the task identifier, the first data set to an agent associated with the external system with an indication of the data processing task, the agent being arranged to cause performance of the task at the external system, to receive a second data set resulting from performance of the task, and to provide the second data set and associated metadata indicative of the transformation.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: Systems and methods are provided for investigation network activities. Network activity information may be accessed. The network activity information may describe for an individual (1) respective relationship with one or more persons; and (2) respective activity status information indicating whether a given person has engaged in a particular activity. A network activity graph may be generated based on the network activity information. The network activity graph may include two or more nodes representing the individual and the one or more persons. Connections between the nodes may represent the respective relationships between the individual and the one or more persons. Data corresponding to the network activity graph may be presented through an interface.

Abstract: An apparatus, and a method, performed by one or more processors are disclosed. The method may comprise receiving a build request associated with performing an external data processing task on a first data set, the first data set being stored in memory associated with a data processing platform to be performed at a system external to the data processing platform. The method may also comprise generating a task identifier for the data processing task, and providing, in association with the task identifier, the first data set to an agent associated with the external system with an indication of the data processing task, the agent being arranged to cause performance of the task at the external system, to receive a second data set resulting from performance of the task, and to provide the second data set and associated metadata indicative of the transformation.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: A database system comprised of a decoupled compute layer and storage layer is implemented to store, build, and maintain a canonical dataset, a temporary buffer, and an edits dataset. The canonical dataset is a set of batch updated data. The data is appended in chunks to the canonical dataset such that the canonical dataset becomes a historical dataset over time. The buffer is a write ahead log that contains the most recent chunks of data and provides atomicity and durability for the database system. The edits dataset is the set of data that contains edits such as cell mutations, row appends and/or row deletions. The database system enables users to make cell or row-level edits to tables and observe those edits in analytical systems or downstream builds with minimal latency.

Abstract: Systems, methods, and non-transitory computer readable media are provided for managing expiration of modules. An expiry dataset may be maintained. The expiry dataset may include a set of identifiers corresponding to a set of modules, a set of expiry values for the set of modules, and a set of termination tasks for the set of modules. A request to refresh a module may be received from a client. Responsive to the reception of the request, an expiry value and a termination task for the module within the expiry dataset may be updated. The expiry value may be independent of a timestamp associated with the request.

Abstract: Systems, methods, and non-transitory computer readable media are provided for managing expiration of modules. An expiry dataset may be maintained. The expiry dataset may include a set of identifiers corresponding to a set of modules, a set of expiry values for the set of modules, and a set of termination tasks for the set of modules. A request to refresh a module may be received from a client. Responsive to the reception of the request, an expiry value and a termination task for the module within the expiry dataset may be updated. The expiry value may be independent of a timestamp associated with the request.

Abstract: Computer-implemented systems and methods are disclosed to interface with one or more storage devices storing a plurality of documents, wherein each of the plurality of documents is associated with one or more tags of one or more predefined hierarchies of tags, wherein the one or more hierarchies of tags include multiple dimensions. In accordance with some embodiments, a method is provided to identify one or more documents from the data storage devices. The method comprises acquiring, via an interface, a selection of one or more tags of the one or more predefined hierarchies of tags. The method further comprises identifying one or more documents from the data storage devices in response to the selection, the identified one or more documents having tags that have a relationship with the selected tags, and providing data corresponding to the identified documents for displaying in the interface.

Abstract: Computer-implemented systems and methods are disclosed to interface with one or more storage devices storing a plurality of documents, wherein each of the plurality of documents is associated with one or more tags of one or more predefined hierarchies of tags, wherein the one or more hierarchies of tags include multiple dimensions. In accordance with some embodiments, a method is provided to identify one or more documents from the data storage devices. The method comprises acquiring, via an interface, a selection of one or more tags of the one or more predefined hierarchies of tags. The method further comprises identifying one or more documents from the data storage devices in response to the selection, the identified one or more documents having tags that have a relationship with the selected tags, and providing data corresponding to the identified documents for displaying in the interface.

Abstract: An apparatus, and a method, performed by one or more processors are disclosed. The method may comprise receiving a build request associated with performing an external data processing task on a first data set, the first data set being stored in memory associated with a data processing platform to be performed at a system external to the data processing platform. The method may also comprise generating a task identifier for the data processing task, and providing, in association with the task identifier, the first data set to an agent associated with the external system with an indication of the data processing task, the agent being arranged to cause performance of the task at the external system, to receive a second data set resulting from performance of the task, and to provide the second data set and associated metadata indicative of the transformation.