Abstract: Multidimensional databases are well-suited for viewing data at different levels of detail. Graph databases are well-suited for modeling data sets with complex relationships. A novel platform for analysis and planning is enabled by linking multidimensional and graph databases. Graphs are data structures stored in graph databases. Graphs use nodes and edges to model data elements, some of which are derived. A graph is traversed to derive new data elements. To perform analysis on the graph data elements, graph traversal paths are stored as tuples in a fact table. This fact table is in turn loaded into the multidimensional database by mapping the fact table's attribute columns to dimensions of the multidimensional database.

Abstract: Multidimensional databases are well-suited for viewing data at different levels of detail. Graph databases are well-suited for modeling data sets with complex relationships. A novel platform for analysis and planning is enabled by linking multidimensional and graph databases. Graphs are data structures stored in graph databases. Graphs use nodes and edges to model data elements, some of which are derived. A graph is traversed to derive new data. elements. To perform analysis on the graph data elements, graph traversal paths are stored as tuples in a fact table. This fact table is in turn loaded into the multidimensional database by mapping the fact table's attribute columns to dimensions of the multidimensional database.

Abstract: Multidimensional databases are well-suited for viewing data at different levels of detail. Graph databases are well-suited for modeling data sets with complex relationships. A novel platform for analysis and planning is enabled by linking multidimensional and graph databases. Graphs are data structures stored in graph databases. Graphs use nodes and edges to model data elements, some of which are derived. A graph is traversed to derive new data elements. To perform analysis on the graph data elements, graph traversal paths are stored as tuples in a fact table. This fact table is in turn loaded into the multidimensional database by mapping the fact table's attribute columns to dimensions of the multidimensional database.

Abstract: Multidimensional databases are well-suited for viewing data at different levels of detail. Graph databases are well-suited for modeling data sets with complex relationships. A novel platform for analysis and planning is enabled by linking multidimensional and graph databases. Graphs are data structures stored in graph databases. Graphs use nodes and edges to model data elements, some of which are derived. A graph is traversed to derive new data elements. To perform analysis on the graph data elements, graph traversal paths are stored as tuples in a fact table. This fact table is in turn loaded into the multidimensional database by mapping the fact table's attribute columns to dimensions of the multidimensional database.

Abstract: Implementations generally relate to cost and profitability planning. In some implementations, a method includes constructing a graph data structure based on supply chain information and financial information. The method also includes computing cost information and profitability information based on the graph data structure. The method also includes generating a grid model from the cost information and the profitability information, where the grid model includes measures that are associated with dimension members arranged in hierarchies.

Abstract: Described herein are methods and systems for regulating the communication between a group of heterogeneous nodes. In one embodiment, the heterogeneous nodes include parents and their children. According to techniques of one embodiment of the present invention, children are permitted to communicate with one another via a social network if their respective parent(s) are acquaintances (e.g., dad of first child is a friend of dad of second child). Conversely, children are not permitted to communicate with one another via a social network if their respective parent(s) are not acquaintances. In one embodiment, one parent is an “acquaintance” of another parent, if the parents are connected via a social network.

Abstract: This disclosure provides a database, methods and associated software to implement and manage a prediction market. By collecting individual predictions as a set of predicted outcomes, such as a range of outcomes, the principles presented herein permit aggregation of overlapping predictions to generate a cumulative probability distribution, effectively using “crowd wisdom” to build a probability model of an event. Through the use of “roll-up” and “roll-down” techniques, this disclosure provides a way of applying new information not only to an event being predicted, but also to the outcomes of other, related events. Conversion to a common probability distribution format may be used to simplify and accelerate mathematical operations, easing the burden in quickly calculating and updating a cumulative probability model for events in each affected dimension in the predicted market.

Abstract: Methods and apparatus for updating a database using a multi-dimensional data model in which data corresponding to the database are organized in multi-dimensional data blocks. Each data block has summary information associated therewith corresponding to a current state of evolution of the associated data block. Dependency information is maintained in the data model which identifies data dependencies within and among the data blocks. A list of change entries identifies changes made to the data blocks and times at which the changes were made. A request to apply a change entry to a data block is received. The change entry is applied to the data block, including generating a dependency list of data blocks whose data depends directly or indirectly from the updated data block. The data blocks in the dependency list are processed in accordance with a dependency update algorithm to update the data blocks in the dependency list.

Abstract: Methods and apparatus for updating a database using a multi-dimensional data model in which data corresponding to the database are organized in multi-dimensional data blocks. Each data block has summary information associated therewith corresponding to a current state of evolution of the associated data block. Dependency information is maintained in the data model which identifies data dependencies within and among the data blocks. A list of change entries identifies changes made to the data blocks and times at which the changes were made. A request to apply a change entry to a data block is received. The change entry is applied to the data block, including generating a dependency list of data blocks whose data depends directly or indirectly from the updated data block. The data blocks in the dependency list are processed in accordance with a dependency update algorithm to update the data blocks in the dependency list.

Abstract: Methods and apparatus for updating a database using a multi-dimensional data model in which data corresponding to the database are organized in multi-dimensional data blocks. Each data block has summary information associated therewith corresponding to a current state of evolution of the associated data block. Dependency information is maintained in the data model which identifies data dependencies within and among the data blocks. A list of change entries identifies changes made to the data blocks and times at which the changes were made. A request to apply a change entry to a data block is received. The change entry is applied to the data block, including generating a dependency list of data blocks whose data depends directly or indirectly from the updated data block. The data blocks in the dependency list are processed in accordance with a dependency update algorithm to update the data blocks in the dependency list.

Abstract: Methods and apparatus for updating a database using a multi-dimensional data model in which data corresponding to the database are organized in multi-dimensional data blocks. Each data block has summary information associated therewith corresponding to a current state of evolution of the associated data block. Dependency information is maintained in the data model which identifies data dependencies within and among the data blocks. A list of change entries identifies changes made to the data blocks and times at which the changes were made. A request to apply a change entry to a data block is received. The change entry is applied to the data block, including generating a dependency list of data blocks whose data depends directly or indirectly from the updated data block. The data blocks in the dependency list are processed in accordance with a dependency update algorithm to update the data blocks in the dependency list.