Large-scale data parallel query method based on subgraph matching

A large-scale data and query method technology, applied in the direction of electronic digital data processing, special data processing applications, digital data information retrieval, etc., can solve the problems that data query solutions cannot solve data query problems quickly and accurately

Inactive Publication Date: 2019-07-09
CENT SOUTH UNIV
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Problems solved by technology

[0007] The purpose of the present invention is to improve the efficiency of data query in large-scale data sets. Due to the huge scale of data today and the complex network relationships between data, traditional data query solutions can no longer solve data query problems quickly and accurately. This paper The invention provides a large-scale data parallel query method based on subgraph matching

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  • Large-scale data parallel query method based on subgraph matching
  • Large-scale data parallel query method based on subgraph matching
  • Large-scale data parallel query method based on subgraph matching

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[0019] In order to make the purpose, characteristics and advantages of the present invention more obvious and easy to understand, the present invention will be further described in the following in combination with the basic theory and formula drawings, according to the sequence of basic principles, macro flow and specific steps.

[0020] Before performing the specific query process, it is necessary to establish an adjacency table for the RDF data graph. The specific format of the adjacency table is as follows:

[0021]

[0022]

[0023] In the table, each vertex u is represented by an adjacency list [uid, ulabel, adjlist], where uid is the vertex ID, uLabel is the attribute corresponding to the vertex, adjList is the edge attribute of the point and the adjacent vertex attributes connected by the edge In general, adjList(u)={(ei.eLabel,ei.nLabel)}, where ei is the edge adjacent to point u, eLabel is the edge label attribute of ei, and nLabel is connected by the edge ei A...

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Abstract

In recent years, with the rapid development of a computer network, the RDF data volume on the Web rapidly increases, especially a large number of large-scale RDF data sets appear, and the mass data often has a complex network relationship, so that a traditional centralized query scheme cannot rapidly and accurately obtain a query result. The invention discloses a large-scale data parallel query method based on subgraph matching. The method is combined with a distributed platform, and mainly aims to improve the data query efficiency in a large-scale data set. Firstly, an adjacency list storagescheme is adopted for a data graph and a query graph, topological information and attribute information of the graph are fully utilized, and a query process is converted into fields including a judgment process; and then, the problem of matching sequence selection is solved by accurately evaluating the candidate number of the query points of each candidate region, the generation of intermediate results is reduced, and the exploration process of multiple candidate regions can be solved in parallel. Through the mode, the query efficiency can be effectively improved, and an accurate query resultcan be obtained.

Description

technical field [0001] The invention belongs to the relevant field of data query under large-scale data, in particular to a large-scale data parallel query method based on subgraph matching. Background technique [0002] With the widespread application of the Semantic Web in many fields such as unstructured data management, bioinformatics, and digital libraries, the amount of RDF data on the Web has grown rapidly, especially many large-scale RDF datasets have appeared. As of June 2018 , Links Open Data Cloud contains 1231 open datasets and 16132 links. Efficient and convenient organization of RDF data has become an urgent problem. [0003] RDF is a general language proposed by W3C to describe semantic Web information. RDF data uses triples as its basic unit. Each triple is composed of subject, predicate and object. The relationship between subject and object is described by the predicate, expressed as <s,p,o>. Compared with relational data, RDF data is a typical sch...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2453G06F16/2455
Inventor 季雅雯杨柳
Owner CENT SOUTH UNIV
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