Abstract: A method and system for managing an execution plan in a graph database includes: acquiring a graph query language, analyzing the graph query language by using a scanner to generate a predefined Token, and parsing the predefined Token into a data structure by using a parser to obtain an abstract syntax tree; next, processing the abstract syntax tree by using a planner to generate an execution plan, registering a mapping table to store variables in the execution plan and corresponding variable values, and optimizing the execution plan by using an optimizer, where the execution plan includes customized execution operators and customized topologies; and finally, traversing and scheduling the execution plan by using a scheduler, to output an obtained execution result. The present disclosure modularizes the execution operators, improves reusability, and reduces the development cost. The execution plan is scheduled by using an asynchronous notification mechanism, to reduce blocking.

Abstract: Disclosed are a method and a system for optimizing data storage of query statistics of a graph database. The method includes: periodically scanning, on storage servers in which partitions are located, all edges in the partitions; determining, according to all the edges in the partitions, partitions to which start points and end points belong, and calculating outgoing-edge correlation and incoming-edge correlation between partitions; calculating relevancies between partitions through a preset correlation matrix weight according to the outgoing-edge correlation and the incoming-edge correlation between partitions; and storing partitions with high relevancies on a same storage server.

Abstract: Disclosed are a method and system for verifying a resulting behavior of a graph query language. A lexical parser and a preset lexical rule are added to a BDD test framework, to construct an improved BDD test framework; a first Gherkin text in a graph query language standard is migrated to be a second Gherkin text in a graph query language; a first parsing result of the second Gherkin text is acquired from the improved BDD test framework; a first return result from a graph database is acquired, a comparison result between the first parsing result and the first return result is acquired, wherein the graph query language meets the graph query language standard if the first parsing result and the first return result are the same. The present disclosure solves the problem that the BDD test framework cannot recognize complex Gherkin texts, thereby facilitating development of graph databases.

Abstract: The present application relates to a data processing method and system of a distributed graph database. The present application solves the problem of how to improve the efficiency of adding, deleting, modifying and checking data in a graph database, realizes balanced storage and concurrent computing of distributed index, reduces network overhead caused by data operation, and improves the efficiency of data operation effectively.

Abstract: A method and system for managing an execution plan in a graph database includes: acquiring a graph query language, analyzing the graph query language by using a scanner to generate a predefined Token, and parsing the predefined Token into a data structure by using a parser to obtain an abstract syntax tree; next, processing the abstract syntax tree by using a planner to generate an execution plan, registering a mapping table to store variables in the execution plan and corresponding variable values, and optimizing the execution plan by using an optimizer, where the execution plan includes customized execution operators and customized topologies; and finally, traversing and scheduling the execution plan by using a scheduler, to output an obtained execution result. The present disclosure modularizes the execution operators, improves reusability, and reduces the development cost. The execution plan is scheduled by using an asynchronous notification mechanism, to reduce blocking.

Abstract: A UUID generation method for a distributed graph database, and a data processing method and apparatus for the distributed graph database. The UUID generation method for the distributed graph database comprises the following steps: receiving a UUID generation request, wherein the UUID generation request comprises an input field and a partition number of the input field; calling a Murmur hash function to generate an initial UUID according to the input field and the partition number of the input field; querying whether a duplicate key value exists between the initial UUID and a historical UUID to obtain a query result; and obtaining a target UUID for storage according to the query result and the initial UUID.

Abstract: A POI recommendation method based on the graph database includes: acquiring an ID and positioning information of a target user; according to the ID and the positioning information of the target user, acquiring a feature vector of the target user, a friend intimacy coefficient of the target user and a target POI set from a target graph database, wherein the target POI set is a set composed of POIs within a preset distance range queried according to the positioning information; according to the target POI set, acquiring a feature matrix of a user set with related POI consumption records from the target graph database, wherein the related POI consumption records are consumption records associated with any POI in the target POI set; and according to the friend intimacy coefficient, the feature vector of the target user and the feature matrix, determining a target POI from the target POI set.

Abstract: A UUID generation method for a distributed graph database, and a data processing method and apparatus for the distributed graph database. The UUID generation method for the distributed graph database comprises the following steps: receiving a UUID generation request, wherein the UUID generation request comprises an input field and a partition number of the input field; calling a Murmur hash function to generate an initial UUID according to the input field and the partition number of the input field; querying whether a duplicate key value exists between the initial UUID and a historical UUID to obtain a query result; and obtaining a target UUID for storage according to the query result and the initial UUID.

Abstract: A POI recommendation method based on the graph database includes: acquiring an ID and positioning information of a target user; according to the ID and the positioning information of the target user, acquiring a feature vector of the target user, a friend intimacy coefficient of the target user and a target POI set from a target graph database, wherein the target POI set is a set composed of POIs within a preset distance range queried according to the positioning information; according to the target POI set, acquiring a feature matrix of a user set with related POI consumption records from the target graph database, wherein the related POI consumption records are consumption records associated with any POI in the target POI set; and according to the friend intimacy coefficient, the feature vector of the target user and the feature matrix, determining a target POI from the target POI set.