Abstract: A method for obtaining an emission probability includes obtaining a plurality of measurement reports (MRs) of a terminal in a target region and an engineering parameter of at least one base station in the target region, obtaining, based on parameter information in each of the plurality of MRs and the engineering parameter of the at least one base station, a feature vector corresponding to each of the plurality of MRs, processing, using a regression model, location information in each of the plurality of MRs and the feature vector corresponding to each of the plurality of MRs, to obtain a single-point positioning model, calculating, based on the single-point positioning model, the location information in each of the plurality of MRs, and the feature vector corresponding to each of the plurality of MRs, an emission probability of the feature vector corresponding to each of the plurality of MRs.

Abstract: A method for obtaining an emission probability includes obtaining a plurality of measurement reports (MRs) of a terminal in a target region and an engineering parameter of at least one base station in the target region, obtaining, based on parameter information in each of the plurality of MRs and the engineering parameter of the at least one base station, a feature vector corresponding to each of the plurality of MRs, processing, using a regression model, location information in each of the plurality of MRs and the feature vector corresponding to each of the plurality of MRs, to obtain a single-point positioning model, calculating, based on the single-point positioning model, the location information in each of the plurality of MRs, and the feature vector corresponding to each of the plurality of MRs, an emission probability of the feature vector corresponding to each of the plurality of MRs.

Abstract: In one embodiment of the present disclosure, an original graph including nodes is obtained. The nodes of the original graph are reordered to generate a reordered graph. Non-zero elements in an adjacency matrix for the reordered graph are clustered as compared with an adjacency matrix for the original graph. The adjacency matrix for the reordered graph is encoded with integers. The integers correspond to non-empty blocks in the adjacency matrix for the reordered graph.

Abstract: A computer-implemented method, a computer system, and a computer program product for trajectory similarity search is provided. The present invention may include, in response to receiving, by one or more processors, a search request for at least one trajectory similar to a query trajectory, determining, by one or more processors, a respective similarity between a query trajectory and a plurality of trajectories by calculating, in a synchronized way, a spatial distance measure and a time difference measure between the query trajectory and the plurality of trajectories. The present invention may further include, identifying, by one or more processors, the at least one trajectory from the plurality of trajectories based on the respective similarity between the query trajectory and the plurality of trajectories.

Abstract: A method for obtaining an emission probability includes obtaining a plurality of measurement reports (MRs) of a terminal in a target region and an engineering parameter of at least one base station in the target region, obtaining, based on parameter information in each of the plurality of MRs and the engineering parameter of the at least one base station, a feature vector corresponding to each of the plurality of MRs, processing, using a regression model, location information in each of the plurality of MRs and the feature vector corresponding to each of the plurality of MRs, to obtain a single-point positioning model, calculating, based on the single-point positioning model, the location information in each of the plurality of MRs, and the feature vector corresponding to each of the plurality of MRs, an emission probability of the feature vector corresponding to each of the plurality of MRs.

Abstract: Implementations of the present disclosure relate to methods, systems, and computer program products for graph data processing. In one implementation, a computer-implemented method is disclosed. In the method, an adjacency graph of a source graph may be determined by traversing the source graph based on a deep first search rule. Subgraphs may be extracted from the determined adjacency graph based on a predefined shape. Respective subgraph nodes may be built based on nodes in the respective subgraphs. Then the adjacency graph may be updated based on the respective subgraph nodes. In other implementations, a computer-implemented system and a computer program product for graph data processing are disclosed.

Abstract: In one embodiment of the present disclosure, an original graph including nodes is obtained. The nodes of the original graph are reordered to generate a reordered graph. Non-zero elements in an adjacency matrix for the reordered graph are clustered as compared with an adjacency matrix for the original graph. The adjacency matrix for the reordered graph is encoded with integers. The integers correspond to non-empty blocks in the adjacency matrix for the reordered graph.

Abstract: In one embodiment of the present disclosure, an original graph including nodes is obtained. The nodes of the original graph are reordered to generate a reordered graph. Non-zero elements in an adjacency matrix for the reordered graph are clustered as compared with an adjacency matrix for the original graph. The adjacency matrix for the reordered graph is encoded with integers. The integers correspond to non-empty blocks in the adjacency matrix for the reordered graph.

Abstract: In one embodiment of the present disclosure, an original graph including nodes is obtained. The nodes of the original graph are reordered to generate a reordered graph. Non-zero elements in an adjacency matrix for the reordered graph are clustered as compared with an adjacency matrix for the original graph. The adjacency matrix for the reordered graph is encoded with integers. The integers correspond to non-empty blocks in the adjacency matrix for the reordered graph.

Abstract: In one embodiment of the present disclosure, an original graph including nodes is obtained. The nodes of the original graph are reordered to generate a reordered graph. Non-zero elements in an adjacency matrix for the reordered graph are clustered as compared with an adjacency matrix for the original graph. The adjacency matrix for the reordered graph is encoded with integers. The integers correspond to non-empty blocks in the adjacency matrix for the reordered graph.

Abstract: In one embodiment of the present disclosure, an original graph including nodes is obtained. The nodes of the original graph are reordered to generate a reordered graph. Non-zero elements in an adjacency matrix for the reordered graph are clustered as compared with an adjacency matrix for the original graph. The adjacency matrix for the reordered graph is encoded with integers. The integers correspond to non-empty blocks in the adjacency matrix for the reordered graph.

Abstract: In one embodiment of the present disclosure, an original graph including nodes is obtained. The nodes of the original graph are reordered to generate a reordered graph. Non-zero elements in an adjacency matrix for the reordered graph are clustered as compared with an adjacency matrix for the original graph. The adjacency matrix for the reordered graph is encoded with integers. The integers correspond to non-empty blocks in the adjacency matrix for the reordered graph.