Abstract: Various systems and methods are described for gathering events and analyzing the events and nodes associated with the events. Various arrangements may include receiving events from one or more applications. An event may include two nodes and an action. These events may be stored and processed to determine relations between nodes. These relations may then be processed to determine the similarity between nodes. Further, the nodes may be ranked according to the importance of the nodes as compared with each other.

Abstract: Various methods and systems for calculating ranks of importance of nodes of an enterprise are described. A plurality of relations may be accessed, wherein each relation of the plurality of relations comprises an indication of two nodes of a plurality of nodes, and an indication of a relationship between the two nodes. A matrix may be created using the plurality of relations. The matrix may be processed using the Perron-Frobenius theorem. The plurality of nodes of the enterprise may be ranked according to importance, wherein each node is either a user or an item.

Abstract: Various systems and methods for collaborative filtering of enterprise objects are described. A first object and a second object, which may be enterprise objects, may be compared. Vectors associated with each object may be created. These vectors may contain elements that indicate one or more actions performed involving other objects. Using these vectors, a similarity comparison may be performed to determine the amount of similarity between the objects. Based on a similarity value determined from the similarity comparison between the vectors, the second object may be recommended to a user.

Abstract: Various methods and systems for calculating ranks of importance of nodes of an enterprise are described. A plurality of relations may be accessed, wherein each relation of the plurality of relations comprises an indication of two nodes of a plurality of nodes, and an indication of a relationship between the two nodes. A matrix may be created using the plurality of relations. The matrix may be processed using the Perron-Frobenius theorem. The plurality of nodes of the enterprise may be ranked according to importance, wherein each node is either a user or an item.

Abstract: Various systems and methods are described for gathering events and analyzing the events and nodes associated with the events. Various arrangements may include receiving events from one or more applications. An event may include two nodes and an action. These events may be stored and processed to determine relations between nodes. These relations may then be processed to determine the similarity between nodes. Further, the nodes may be ranked according to the importance of the nodes as compared with each other.

Abstract: Various systems and methods for collaborative filtering of enterprise objects are described. A first object and a second object, which may be enterprise objects, may be compared. Vectors associated with each object may be created. These vectors may contain elements that indicate one or more actions performed involving other objects. Using these vectors, a similarity comparison may be performed to determine the amount of similarity between the objects. Based on a similarity value determined from the similarity comparison between the vectors, the second object may be recommended to a user.

Abstract: Ranks for documents can comprise calculating coefficients indicating connections between users and documents. The coefficients cam be stored as a part of a core data structure on disk for a sparse matrix. The coefficients can be used to calculate rank values for the documents. The using step can include, (a) for each row of the core data structure, reading a row of the core data structure into local memory, inflating the row, converting the row into a row of a damped matrix and multiplying the row of a damped matrix by a current vector to get a value of the next vector; (b) comparing the next vector to the current vector; if the difference is greater than an error value, set the next vector as the current vector and repeat step (a) if the difference is less than an error value, determine rank values from the next vector.