Distributed index for graph database searching and parallel generation method of distributed index

Abstract

The invention discloses a distributed index for graph database searching and a parallel generation method of the distributed index. The distributed index for graph database searching comprises a delta-neighbor peak lookup table, a matching peak cutting index and a distributed matching peak cutting index. The generation method of the distributed index is a MapReduce-based parallel algorithm. According to the distributed index and the parallel generation method, the searching space can be reduced, the searching efficiency can be improved, the defect of enormous time and space expenses for building indexes on graph databases can be overcome, in addition, the index access time required by searching each time can be minimized, and the problem of the searching time increase caused by the index access expenses is solved.

Description

technical field

[0001] The invention belongs to the technical field of keyword search for graph databases, and relates to a distributed index for graph database search and a parallel generation method thereof, in particular to a method for realizing rapid retrieval on large-scale graphs with more than one million vertices. Distributed indexing technology introduced for search results. Background technique

[0002] In recent years, a large amount of graph-structured data has emerged in the fields of social network, bioinformatics, chemical information, software engineering, semantic web, transportation network, etc. For example, in a social network, users are represented as vertices, which contain data such as the user's name, age, preferences, etc., and the relationship between users is represented as edges between vertices, thus forming a huge graph. In this context, applications based on graph databases have begun to flourish and show considerable economic and social valu...

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