Abstract: Embodiments provide systems and methods that find the quickest route between two locations on a graph with multi-edge constraints in a time and space efficient manner. In some embodiments, Dijkstra's algorithm is split into separate universes when a) a multiple-edge constraint is reached, and b) along each edge of a multi-edge constraint. In some embodiments, the split is performed for the purpose of finding the quickest (i.e. lowest weighted) route to the intersection(s) at the end of the constraints. These universes, in some embodiments, are merged or discarded when the intersection at the end of the constraint is found. Using these systems and methods, in some embodiments, the shortest path between two locations of a multi-edge constrained road network can be efficiently determined.

Abstract: Embodiments provide systems and methods that find the quickest route between two locations on a graph with multi-edge constraints in a time and space efficient manner. In some embodiments, Dijkstra's algorithm is split into separate universes when a) a multiple-edge constraint is reached, and b) along each edge of a multi-edge constraint. In some embodiments, the split is performed for the purpose of finding the quickest (i.e. lowest weighted) route to the intersection(s) at the end of the constraints. These universes, in some embodiments, are merged or discarded when the intersection at the end of the constraint is found. Using these systems and methods, in some embodiments, the shortest path between two locations of a multi-edge constrained road network can be efficiently determined.

Abstract: The present invention provides a technique for building natural language parsers by implementing a country and/or jurisdiction specific set of training data that is automatically converted during a build phase to a respective predictive model, i.e., an automated country specific natural language parser. The predictive model can be used without the training data to quantify any input address. This model may be included as part of a larger Geographic Information System (GIS) data-set or as a stand alone quantifier. The build phase may also be run on demand and the resultant predictive model kept in temporary storage for immediate use.