Abstract: Examples for graph database management comprise a graph database system including a graph processor engine to receive a graph database update from an application, a graph navigation query engine to access a real-time graph and process the graph database update on the real-time graph, and a synchronization engine to extract changes from the real-time graph and process the changes to a derived graph view and to a historical graph. Examples for managing a graph database also include receiving a graph query, determining a graph query type, and in the event that the graph query type is a navigational short query type, accessing a real-time graph on a graph navigation query engine and processing the navigation short query, and in the event that the graph query type is an analytical long query type, accessing a historical graph on a graph analytic query engine and processing the analytical long query.

Abstract: Recommending analytic tasks based on similarity of datasets is disclosed. One example is a system including a data processor, a matching module, and a recommendation module. The data processor receives an incoming dataset via a processing system, and generates a feature vector for the incoming dataset. The matching module determines similarity measures between the generated feature vector and representative feature vectors for a plurality of datasets in a data repository, and selects at least one dataset of the plurality of datasets based on the similarity measures. The recommendation module identifies at least one analytic task associated with the selected dataset, and recommends, to a computing device via the processing system, the at least one analytic task to be performed on the incoming dataset.

Abstract: An advisor flowchart is built, which flowchart comprises a set of nodes and each node is associated with a user attribute selected from a set of user attributes in a user space, the set of attributes comprising information about users, e.g., lifestyles, interests, demographics, etc. and each node is associated with a ranking of products from a product space. The advisor flowchart may be used to make a set of recommendations available to a user, where the set of recommendations comprise one or more top-ranked products associated with the node at which the user is currently positioned. At each node, the associated user attribute is used to formulate a question that may be posed to the user. The user's answer to the question may be used to progress to another node of the advisor flowchart.