Abstract: Systems and processes for managing a data warehouse using an activity graph are disclosed. One example process may include selectively tracking new versions of an entity based on a tracking type, removing entities based on data retention rules, removing entities based on data decay rules, and summarizing decayed data.

Abstract: A computer-implemented method for optimizing a data pipeline system includes processing a data pipeline configuration manifest to generate a framework of the data pipeline system and a data flow logic package of the data pipeline system. The data pipeline configuration manifest includes an object-oriented metadata model of the data pipeline system. The computer-implemented method further includes monitoring performance of the data pipeline system during execution of the data flow logic package to obtain a performance metric for the data pipeline system, and modifying, with a processor, the framework of the data pipeline system based on the data pipeline configuration manifest and the performance metric.

Abstract: Systems and processes for accessing data from a graph database are described. The system receives a request comprising time-based information. Time-based information of the request is compared with a first time interval, which is associated with at least one node of a graph. The node of the graph is matched based on the time-based information being at least partially within the first time interval. The system returns a result comprising an indicator of the node of the graph, wherein the node of the graph is associated with an entity, and the node of the graph includes an attribute of the entity.

Abstract: Systems and processes for managing a data warehouse using an activity graph are disclosed. One example process may include selectively tracking new versions of an entity based on a tracking type, removing entities based on data retention rules, removing entities based on data decay rules, and summarizing decayed data.

Abstract: Systems and processes for managing a data warehouse using an activity graph are disclosed. One example process may include selectively tracking new versions of an entity based on a tracking type, removing entities based on data retention rules, removing entities based on data decay rules, and summarizing decayed data.

Abstract: Systems and processes for accessing data from a graph database are described. The system receives a request comprising time-based information. Time-based information of the request is compared with a first time interval, which is associated with at least one node of a graph. The node of the graph is matched based on the time-based information being at least partially within the first time interval. The system returns a result comprising an indicator of the node of the graph, wherein the node of the graph is associated with an entity, and the node of the graph includes an attribute of the entity.

Abstract: An administrator of an enterprise storage set may be tasked with storing a large number and variety of data sets on a large number and variety of storage components. However, the manual selection of a physical schema by an administrator may be time-consuming, may generate inefficient physical schemata, and may not be easily reevaluated as the data sets and storage set change. Presented herein are techniques for automatically determining a physical schema by comparing the storage factors of each data set (e.g., data size, relationships with other data sets, and usages of the data set by users) with the storage capabilities of the storage components, selecting a suitable storage component, and implementing the storage of the data set on the storage component. An embodiment of these techniques may thereby achieve an automated identification of a physical schema with improved efficiency and flexibility of the physical schema while conserving administrative resources.

Abstract: Systems and processes for storing correlated data in a graph database are described. A first user node may be stored. In response to a request, a first event node and a second user node may be stored. The first event node may be associated with the first user node through an edge indicating participation. The first event node may be associated with the second user node through an edge indicating participation. In response to a content access, a second event node and a content node may be stored. The second event node may be associated with the first user node through an edge indicating participation. The second event node may be associated with the content node through an edge indicating usage. The first user node may be associated with the content node through an edge indicating the first user node handles the content node.

Abstract: Systems and processes for managing graph queries on navigational relationships of database entities are described. A graph index may be used to store a class of navigational relationships to expand query capabilities and improve query efficiency of a traditional relational database. Entity relationships stored in the graph index may be dynamically defined and made accessible to query writers. In addition, users can specify particular entity attributes to be replicated in the graph index from the corresponding entities stored in the traditional relational database. Query performance may be improved by leveraging the graph index data and querying both the database and the graph index. Queries of the underlying database may be narrowed by first searching the graph index. Queries of the graph index may also be narrowed by first searching the underlying database. Historic query data may be stored and used to improve subsequent query execution plans.

Abstract: Systems and processes for accessing data from a graph database are described. The system receives a request comprising time-based information. Time-based information of the request is compared with a first time interval, which is associated with at least one node of a graph. The node of the graph is matched based on the time-based information being at least partially within the first time interval. The system returns a result comprising an indicator of the node of the graph, wherein the node of the graph is associated with an entity, and the node of the graph includes an attribute of the entity.

Abstract: Systems and processes for storing correlated data in a graph database are described. A first user node may be stored. In response to a request, a first event node and a second user node may be stored. The first event node may be associated with the first user node through an edge indicating participation. The first event node may be associated with the second user node through an edge indicating participation. In response to a content access, a second event node and a content node may be stored. The second event node may be associated with the first user node through an edge indicating participation. The second event node may be associated with the content node through an edge indicating usage. The first user node may be associated with the content node through an edge indicating the first user node handles the content node.

Abstract: Systems and methods for dynamically managed data retention are described. The system comprises a tiered framework having a plurality of namespaces. The namespaces are configured by a user to have selected data retention attributes. Data including a manifest may be received by the system, processed, and directed to a namespace based upon the manifest. Data storage partitions may be created automatically in association with a namespace, and the data partitions may be assigned partition attributes. Data in a storage partition may be migrated automatically to another namespace based on the partition attributes. Code necessary for creating storage partitions and migrating data is generated by the data management system.

Abstract: Systems and methods for dynamically managed data retention are described. The system comprises a tiered framework having a plurality of namespaces. The namespaces are configured by a user to have selected data retention attributes. Data including a manifest may be received by the system, processed, and directed to a namespace based upon the manifest. Data storage partitions may be created automatically in association with a namespace, and the data partitions may be assigned partition attributes. Data in a storage partition may be migrated automatically to another namespace based on the partition attributes. Code necessary for creating storage partitions and migrating data is generated by the data management system.

Abstract: Systems and processes for managing a data warehouse using an activity graph are disclosed. One example process may include selectively tracking new versions of an entity based on a tracking type, removing entities based on data retention rules, removing entities based on data decay rules, and summarizing decayed data.

Abstract: Systems and processes for managing data in a data warehouse using an activity graph are described. The activity graph may include nodes representing entities (or versions thereof) interconnected by edges representing relationships (or versions thereof) between those entities. The nodes representing versions of an entity may be captured as a directed acyclic graph (DAG). New nodes and edges may be added to the activity graph as new entities and relationships are formed. As changes are made to an entity or relationship, new nodes or edges representing new versions of the entity or relationship may be created and added to the activity graph based on the entity's or relationship's tracking type. Existing nodes and edges may be removed from the activity based on data retention rules and/or data decay rules. In some examples, nodes and edges may be summarized by collapsing multiple nodes or multiple edges into a single node or edge.

Abstract: Systems and processes for storing correlated data in a graph database are described. A first user node may be stored. In response to a request, a first event node and a second user node may be stored. The first event node may be associated with the first user node through an edge indicating participation. The first event node may be associated with the second user node through an edge indicating participation. In response to a content access, a second event node and a content node may be stored. The second event node may be associated with the first user node through an edge indicating participation. The second event node may be associated with the content node through an edge indicating usage. The first user node may be associated with the content node through an edge indicating the first user node handles the content node.

Abstract: A computer-implemented method for optimizing a data pipeline system includes processing a data pipeline configuration manifest to generate a framework of the data pipeline system and a data flow logic package of the data pipeline system. The data pipeline configuration manifest includes an object-oriented metadata model of the data pipeline system. The computer-implemented method further includes monitoring performance of the data pipeline system during execution of the data flow logic package to obtain a performance metric for the data pipeline system, and modifying, with a processor, the framework of the data pipeline system based on the data pipeline configuration manifest and the performance metric.

Abstract: If a response is with a threshold of the predicted response, then the contradictory response is classified as being independent and it is not truly contradictory. If a response is beyond a threshold, the response is classified as not being independent and is likely worthy of further investigation.

Abstract: A method and apparatus for receiving objects is provided. Embodiments provide for receiving, from a source database server, at a target database server, an object. If the object type of the object is involved in an inheritance hierarchy, then the target database server inspects the object to determine a source identifier that identifies to a source database server the object type associated with the object. The target database determines a target identifier that identifies to the target database server the object type associated with the object based on a mapping that associates source identifiers to target identifiers. Alternately, if the object type of the object is not involved in an inheritance hierarchy, then the target database server inspects the object to determine a set of characteristics of the object. The target database server determines the target identifier for the object based on the characteristics of the object.

Abstract: Systems and methods for dynamically managed data retention are described. The system comprises a tiered framework having a plurality of namespaces. The namespaces are configured by a user to have selected data retention attributes. Data including a manifest may be received by the system, processed, and directed to a namespace based upon the manifest. Data storage partitions may be created automatically in association with a namespace, and the data partitions may be assigned partition attributes. Data in a storage partition may be migrated automatically to another namespace based on the partition attributes. Code necessary for creating storage partitions and migrating data is generated by the data management system.