Abstract: Embodiments determine indications of sites using their similarity and travel duration. One embodiment is a method that operates on real estate properties. The method receives destinations of commutes. In order to improve usability, the method determines a real estate property within a cluster of real estate properties that have similar features and that have proximate commute durations. Indications of at least one real estate property are presented to the user.

Abstract: Data are maintained in a distributed computing system that describe a graph. The graph represents relationships among items. The graph has a plurality of vertices that represent the items and a plurality of edges connecting the plurality of vertices. At least one vertex of the plurality of vertices includes a set of label values indicating the at least one vertex's strength of association with a label from a set of labels. The set of labels describe possible characteristics of an item represented by the at least one vertex. At least one edge of the plurality of edges includes a set of label weights for influencing label values that traverse the at least one edge. A label propagation algorithm is executed for a plurality of the vertices in the graph in parallel for a series of synchronized iterations to propagate labels through the graph.

Abstract: Data are maintained in a distributed computing system that describe a graph. The graph represents relationships among items. The graph has a plurality of vertices that represent the items and a plurality of edges connecting the plurality of vertices. At least one vertex of the plurality of vertices includes a set of label values indicating the at least one vertex's strength of association with a label from a set of labels. The set of labels describe possible characteristics of an item represented by the at least one vertex. At least one edge of the plurality of edges includes a set of label weights for influencing label values that traverse the at least one edge. A label propagation algorithm is executed for a plurality of the vertices in the graph in parallel for a series of synchronized iterations to propagate labels through the graph.

Abstract: Embodiments relate to searching or comparing sites. One embodiment is a real estate search-or-compare method based on commute durations. The method efficiently processes public transportation and real estate property data to compute the durations of travel between the real estate properties and the vehicle stops. These durations are stored. A request framework is introduced that allows to express a wide range of search-or-compare requests. During request processing, the method identifies parts of the commute paths that depend on any real estate property. Because durations for these parts were precomputed and stored, the method can determine commute durations to every real estate property in a scalable manner. As a result, the method rapidly responds to requests within the real estate market of one of the largest metropolitan areas in existence today. Other embodiments include: searching or comparing based on a monetary cost, transportation using private cars, and sites other than real estate properties.

Abstract: Embodiments relate to searching or comparing sites. One embodiment is a real estate search-or-compare method based on commute durations. The method efficiently processes public transportation and real estate property data to compute the durations of travel between the real estate properties and the vehicle stops. These durations are stored. A request framework is introduced that allows to express a wide range of search-or-compare requests. During request processing, the method identifies parts of the commute paths that depend on any real estate property. Because durations for these parts were precomputed and stored, the method can determine commute durations to every real estate property in a scalable manner. As a result, the method rapidly responds to requests within the real estate market of one of the largest metropolitan areas in existence today. Other embodiments include: searching or comparing based on a monetary cost, transportation using private cars, and sites other than real estate properties.

Abstract: Embodiments relate to searching or comparing sites. One embodiment is a real estate search-or-compare method based on commute durations. The method efficiently processes public transportation and real estate property data to compute the durations of travel between the real estate properties and the vehicle stops. These durations are stored. A request framework is introduced that allows to express a wide range of search-or-compare requests. During request processing, the method identifies parts of the commute paths that depend on any real estate property. Because durations for these parts were precomputed and stored, the method can determine commute durations to every real estate property in a scalable manner. As a result, the method rapidly responds to requests within the real estate market of one of the largest metropolitan areas in existence today. Other embodiments include: searching or comparing based on a monetary cost, transportation using private cars, and sites other than real estate properties.

Abstract: Data are maintained in a distributed computing system that describe a graph. The graph represents relationships among items. The graph has a plurality of vertices that represent the items and a plurality of edges connecting the plurality of vertices. At least one vertex of the plurality of vertices includes a set of label values indicating the at least one vertex's strength of association with a label from a set of labels. The set of labels describe possible characteristics of an item represented by the at least one vertex. At least one edge of the plurality of edges includes a set of label weights for influencing label values that traverse the at least one edge. A label propagation algorithm is executed for a plurality of the vertices in the graph in parallel for a series of synchronized iterations to propagate labels through the graph.

Abstract: Data are maintained in a distributed computing system that describe a graph. The graph represents relationships among items. The graph has a plurality of vertices that represent the items and a plurality of edges connecting the plurality of vertices. At least one vertex of the plurality of vertices includes a set of label values indicating the at least one vertex's strength of association with a label from a set of labels. The set of labels describe possible characteristics of an item represented by the at least one vertex. At least one edge of the plurality of edges includes a set of label weights for influencing label values that traverse the at least one edge. A label propagation algorithm is executed for a plurality of the vertices in the graph in parallel for a series of synchronized iterations to propagate labels through the graph.

Abstract: Embodiments relate to producing a plan of a route in a transportation system. The method receives route requirements, including a starting location and an ending location. The method builds a model of the transportation system from data about vehicles. The model abstracts a “prospect travel” between two locations using any of a range of choices of vehicles and walks that can transport between the two locations. Given anticipated wait durations for the vehicles and their ride durations, the method determines an expected minimum travel duration using any of these choices. The method combines the expectations for various locations in a scalable manner. As a result, a route plan that achieves a shortest expected travel duration, and meets other requirements, is computed for one of the largest metropolitan areas in existence today. Other embodiments include a computer system and a product service that implement the method.

Abstract: Embodiments relate to searching or comparing sites. One embodiment is a real estate search-or-compare method based on commute durations. The method efficiently processes public transportation and real estate property data to compute the durations of travel between the real estate properties and the vehicle stops. These durations are stored. A request framework is introduced that allows to express a wide range of search-or-compare requests. During request processing, the method identifies parts of the commute paths that depend on any real estate property. Because durations for these parts were precomputed and stored, the method can determine commute durations to every real estate property in a scalable manner. As a result, the method rapidly responds to requests within the real estate market of one of the largest metropolitan areas in existence today. Other embodiments include: searching or comparing based on a monetary cost, transportation using private cars, and sites other than real estate properties.

Abstract: Embodiments relate to searching or comparing sites. One embodiment is a real estate search-or-compare method based on commute durations. The method efficiently processes public transportation and real estate property data to compute the durations of travel between the real estate properties and the vehicle stops. These durations are stored. A request framework is introduced that allows to express a wide range of search-or-compare requests. During request processing, the method identifies parts of the commute paths that depend on any real estate property. Because durations for these parts were precomputed and stored, the method can determine commute durations to every real estate property in a scalable manner. As a result, the method rapidly responds to requests within the real estate market of one of the largest metropolitan areas in existence today. Other embodiments include: searching or comparing based on a monetary cost, transportation using private cars, and sites other than real estate properties.

Abstract: Embodiments relate to searching or comparing sites. One embodiment is a real estate search-or-compare method based on commute durations. The method efficiently processes public transportation and real estate property data to compute the durations of travel between the real estate properties and the vehicle stops. These durations are stored. A request framework is introduced that allows to express a wide range of search-or-compare requests. During request processing, the method identifies parts of the commute paths that depend on any real estate property. Because durations for these parts were precomputed and stored, the method can determine commute durations to every real estate property in a scalable manner. As a result, the method rapidly responds to requests within the real estate market of one of the largest metropolitan areas in existence today. Other embodiments include: searching or comparing based on a monetary cost, transportation using private cars, and sites other than real estate properties.

Abstract: Embodiments relate to producing a plan of a route in a transportation system. The method receives route requirements, including a starting and an ending locations. The method builds a model of the transportation system from data about vehicles. The model abstracts a “prospect travel” between two locations using any of a range of choices of vehicles and walks that can transport between the two locations. Given anticipated wait durations for the vehicles and their ride durations, the method determines an expected minimum travel duration using any of these choices. The method combines the expectations for various locations in a scalable manner. As a result, a route plan that achieves a shortest expected travel duration, and meets other requirements, is computed for one of the largest metropolitan areas in existence today. Other embodiments include a computer system and a product service that implement the method.

Abstract: Embodiments relate to searching or comparing sites. One embodiment is a real estate search-or-compare method based on commute durations. The method efficiently processes public transportation and real estate property data to compute the durations of travel between the real estate properties and the vehicle stops. These durations are stored. A request framework is introduced that allows to express a wide range of search-or-compare requests. During request processing, the method identifies parts of the commute paths that depend on any real estate property. Because durations for these parts were precomputed and stored, the method can determine commute durations to every real estate property in a scalable manner. As a result, the method rapidly responds to requests within the real estate market of one of the largest metropolitan areas in existence today. Other embodiments include: searching or comparing based on a monetary cost, transportation using private cars, and sites other than real estate properties.

Abstract: Embodiments are disclosed for using an improved locality sensitive hashing (LSH) operation for the K-means clustering algorithm. In some embodiments, parameters of an LSH function are optimized with respect to a new cost model. In other embodiments, an LSH operation is applied with optimized parameters to a K-means clustering algorithm.

Abstract: Embodiments relate to producing a plan of a route in a transportation system. The method receives route requirements, including a starting location and an ending location. The method builds a model of the transportation system from data about vehicles. The model abstracts a “prospect travel” between two locations using any of a range of choices of vehicles and walks that can transport between the two locations. Given anticipated wait durations for the vehicles and their ride durations, the method determines an expected minimum travel duration using any of these choices. The method combines the expectations for various locations in a scalable manner. As a result, a route plan that achieves a shortest expected travel duration, and meets other requirements, is computed for one of the largest metropolitan areas in existence today. Other embodiments include a computer system and a product service that implement the method.

Abstract: Data are maintained in a distributed computing system that describe a graph. The graph represents relationships among items. The graph has a plurality of vertices that represent the items and a plurality of edges connecting the plurality of vertices. At least one vertex of the plurality of vertices includes a set of label values indicating the at least one vertex's strength of association with a label from a set of labels. The set of labels describe possible characteristics of an item represented by the at least one vertex. At least one edge of the plurality of edges includes a set of label weights for influencing label values that traverse the at least one edge. A label propagation algorithm is executed for a plurality of the vertices in the graph in parallel for a series of synchronized iterations to propagate labels through the graph.

Abstract: Data are maintained in a distributed computing system that describe a directed graph representing relationships among a set of items. The directed graph models a condition having an associated problem. The directed graph has graph components having associated data fields. The relationships are analyzed to identify a solution to the problem. As part of the analysis, a new value for the data field associated with a graph component is identified responsive to an operation performed during the analysis. The new value is compared with an existing value of the data field, and the data field is modified. The modification may include inserting the new value into an overflow vector of data, and replacing the existing value in the data field with exception information identifying the location of the new value. An exception flag associated with the data field is set to indicate that the exception information is being used.

Abstract: Embodiments relate to searching or comparing sites. One embodiment is a real estate search-or-compare method based on commute durations. The method efficiently processes public transportation and real estate property data to compute the durations of travel between the real estate properties and the vehicle stops. These durations are stored. A request framework is introduced that allows to express a wide range of search-or-compare requests. During request processing, the method identifies parts of the commute paths that depend on any real estate property. Because durations for these parts were precomputed and stored, the method can determine commute durations to every real estate property in a scalable manner. As a result, the method rapidly responds to requests within the real estate market of one of the largest metropolitan areas in existence today. Other embodiments include: searching or comparing based on a monetary cost, transportation using private cars, and sites other than real estate properties.

Abstract: Embodiments relate to producing a plan of a route in a transportation system. The method receives route requirements, including a starting and an ending locations. The method builds a model of the transportation system from data about vehicles. The model abstracts a “prospect travel” between two locations using any of a range of choices of vehicles and walks that can transport between the two locations. Given anticipated wait durations for the vehicles and their ride durations, the method determines an expected minimum travel duration using any of these choices. The method combines the expectations for various locations in a scalable manner. As a result, a route plan that achieves a shortest expected travel duration, and meets other requirements, is computed for one of the largest metropolitan areas in existence today. Other embodiments include a computer system and a product service that implement the method.