Abstract: Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Abstract: Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Abstract: Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Abstract: An in-memory graph query runtime is integrated inside a database management system and is capable of performing simple patter-matching queries against homogeneous graphs. The runtime efficiently combines breadth-first (BFS) and depth-first (DFS) neighbor traversal algorithms to achieve a hybrid runtime that takes the best from both sides. As a result, the hybrid runtime is able to process arbitrarily large queries with a fixed amount of memory, optimizing for memory locality.

Abstract: An in-memory graph query runtime is integrated inside a database management system and is capable of performing simple patter-matching queries against homogeneous graphs. The runtime efficiently combines breadth-first (BFS) and depth-first (DFS) neighbor traversal algorithms to achieve a hybrid runtime that takes the best from both sides. As a result, the hybrid runtime is able to process arbitrarily large queries with a fixed amount of memory, optimizing for memory locality.

Abstract: An in-memory graph query runtime is integrated inside a database management system and is capable of performing simple patter-matching queries against homogeneous graphs. The runtime efficiently combines breadth-first (BFS) and depth-first (DFS) neighbor traversal algorithms to achieve a hybrid runtime that takes the best from both sides. As a result, the hybrid runtime is able to process arbitrarily large queries with a fixed amount of memory, optimizing for memory locality.

Abstract: Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Abstract: Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Abstract: Techniques support graph pattern matching queries inside a relational database management system (RDBMS) that supports SQL execution. The techniques compile a graph pattern matching query into a SQL query that can then be executed by the relational engine. As a result, techniques enable execution of graph pattern matching queries on top of the relational engine by avoiding any change in the existing SQL engine.

Abstract: Herein are techniques to accelerate finding a top few shortest paths between two vertices of a graph. In an embodiment, a computer calculates, for a graph that contains vertices that include landmark vertices, distances between each vertex and each landmark vertex. Based on the distances from each vertex to each landmark vertex, a top few shortest paths from a source vertex to a target vertex are calculated. In an embodiment, triangulation establishes a lower bound on a distance from a neighbor vertex of a current vertex to a target vertex of a query. In an embodiment, distance predictions based on the distance lower bounds are used to accelerate a K-A star search for the top few shortest paths.

Abstract: An in-memory graph query runtime is integrated inside a database management system and is capable of performing simple patter-matching queries against homogeneous graphs. The runtime efficiently combines breadth-first (BFS) and depth-first (DFS) neighbor traversal algorithms to achieve a hybrid runtime that takes the best from both sides. As a result, the hybrid runtime is able to process arbitrarily large queries with a fixed amount of memory, optimizing for memory locality.

Abstract: An in-memory graph query runtime is integrated inside a database management system and is capable of performing simple patter-matching queries against homogeneous graphs. The runtime efficiently combines breadth-first (BFS) and depth-first (DFS) neighbor traversal algorithms to achieve a hybrid runtime that takes the best from both sides. As a result, the hybrid runtime is able to process arbitrarily large queries with a fixed amount of memory, optimizing for memory locality.

Abstract: An in-memory graph query runtime is integrated inside a database management system and is capable of performing simple patter-matching queries against homogeneous graphs. The runtime efficiently combines breadth-first (BFS) and depth-first (DFS) neighbor traversal algorithms to achieve a hybrid runtime that takes the best from both sides. As a result, the hybrid runtime is able to process arbitrarily large queries with a fixed amount of memory, optimizing for memory locality.

Abstract: Techniques are described for generating and re-using reachability graphs for efficient execution of queries. In an embodiment, a query is received for execution on a data graph. Such a query may include one or more expressions for edges in the data graph, which when executed select one or more paths in the data graph to generate results for the query. The system uses a repository to store reachability graphs and may determine whether a reachability graph for an expression of the query for the data graph is stored in a repository. Such a reachability graph is generated by applying the expression on the data graph to qualify or disqualify the edges in the data graph to be included as part of edges of the reachability graph. For example, an edge in a reachability graph exists between two vertices when at least one edge of the data graph has qualified between two vertices of the data graph that correspond to the two vertices of the reachability graph.

Abstract: Herein are computerized techniques for processing a heterogeneous graph according to scan-avoidant query planning. In an embodiment, a computer respectively stores a first and second kind of vertices of a property graph into a first and second vertex tables. The computer generates, without scanning the second vertex table: a) an initial partial result of a query of the property graph based on the first vertex table, and b) a subsequent partial result of the query based on the initial partial result and the second kind of vertices. Herein are graph encodings that are dense, without requiring extra computation, and that exploit graph heterogeneity to achieve an aggregation granularity that reduces data working set scope, optimizes for caching, and encourages compression. Herein are query execution mechanisms and techniques that intelligently avoid accessing circumstantially extraneous data and/or structures and that can horizontally scale.

Abstract: Techniques are described for generating and re-using reachability graphs for efficient execution of queries. In an embodiment, a query is received for execution on a data graph. Such a query may include one or more expressions for edges in the data graph, which when executed select one or more paths in the data graph to generate results for the query. The system uses a repository to store reachability graphs and may determine whether a reachability graph for an expression of the query for the data graph is stored in a repository. Such a reachability graph is generated by applying the expression on the data graph to qualify or disqualify the edges in the data graph to be included as part of edges of the reachability graph. For example, an edge in a reachability graph exists between two vertices when at least one edge of the data graph has qualified between two vertices of the data graph that correspond to the two vertices of the reachability graph.