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MapReduce-based big picture distance connection query method

A technology for connecting queries and distances on graphs, applied in the information field, can solve problems such as large search space and calculation amount, and achieve the effect of reducing the number of iterations and reducing the number of iterations

Inactive Publication Date: 2012-10-17
PEKING UNIV
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Problems solved by technology

However, this will lead to a very large search space and computational load

Method used

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  • MapReduce-based big picture distance connection query method
  • MapReduce-based big picture distance connection query method
  • MapReduce-based big picture distance connection query method

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Embodiment Construction

[0033] The specific implementation steps and detailed methods are described below.

[0034] This implementation method is carried out on the Hadoop platform, and is mainly optimized for the three factors that affect the execution efficiency of iterative tasks in the MapReduce environment. Here, firstly, the frame diagram of the whole method is given, and the responsibility of each main module is explained, and then the design and implementation of each module are described in detail with reference to the drawings.

[0035] 1. Algorithm framework

[0036] The method of this patent requires efficient implementation of the distance join query algorithm on Hadoop, and requires reducing the search space of the algorithm as much as possible, reducing the number of iterations of the algorithm, and at the same time being able to adaptively select an appropriate implementation method for each iteration.

[0037] In response to the above requirements, this patent proposes the following...

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Abstract

The invention discloses a MapReduce-based big picture distance connection query method. The method comprises the steps of: (1) extracting initialized query parameters including original picture, accessed nodes, extension range and query result; (2) conducting two-way extension on the original picture on hadoop, starting extension from a source node set and a target node set, conducting each extension on the basis of a cost model, conducting pruning operation by adopting dynamic threshold, and adding newly-extended node to the accessed node set; (3) continuously traversing the incompletely extended remaining nodes until all the nodes meeting the extension range are extended; and (4) after completing iteration, recording route query results between the target nodes and the source nodes in the accessed node set, and returning a query result. The invention provides a cost model-based self-adaption method under the MapReduce environment, reduces the extension space and iteration times by utilizing dynamic threshold-based pruning two-way searching algorithm and Segment index, and improves the task execution efficiency.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to a method for effectively performing distance connection query on large image data in a MapReduce environment. Background technique [0002] In modern society, graphs are used more and more widely. Most operations on graphs require iterative searches, such as reachability queries, shortest path queries, minimum spanning tree queries, and distance join queries. The search space for these operations is large. Taking the distance connection query as an example, given a directed weighted graph G, source node set S, target node set T and distance threshold d t , the distance join query requires returning node pairs between S and T, the shortest distance between these node pairs is less than a given threshold d t . Figure 1 gives an example of a distance join query. Figure 1(a) represents the original graph G, where nodes a and b are nodes in S, nodes g and h are no...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 周家帅高军王衎王腾蛟杨冬青唐世渭
Owner PEKING UNIV
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