Large-scale neighbor query system based on MapReduce and inverted Thiessen polygon

A Thiessen polygon and nearest neighbor query technology, which is applied in cloud computing and big data fields, can solve the problems of unsatisfactory indexing time consumption, inability to adapt to MapReduce parallel processing, poor scalability, etc., and achieve the effect of improving indexing efficiency

Inactive Publication Date: 2018-03-06
DALIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, because the existing indexing algorithms cannot adapt to the parallel processing of MapReduce, the time consumption of index construction is not ideal, and the scalability is not good.

Method used

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  • Large-scale neighbor query system based on MapReduce and inverted Thiessen polygon
  • Large-scale neighbor query system based on MapReduce and inverted Thiessen polygon
  • Large-scale neighbor query system based on MapReduce and inverted Thiessen polygon

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Embodiment 1

[0028] Embodiment 1: A large-scale neighbor query method based on MapReduce and inverted Thiessen polygons, the method is executed by a large-scale neighbor query system based on MapReduce and inverted Thiessen polygons, wherein the system stores multiple instructions , the instructions are adapted to be loaded and executed by a processor, wherein: MapReduce is an existing programming model for parallel computing of large-scale data sets, and the method includes the following steps:

[0029] S1. Construct a MapReduce-based inverted Voronoi index (Tyson polygon index, Inverted VoronoiIndex, IVI);

[0030] S2. Use the inverted Voronoi index to partition the data sets R and S to obtain VC partitions. The two partitions are because the Voronoi diagram needs to be combined with two partitions when the Voronoi diagram needs to be established later, so it is carried out in this step. Partitioning of the two datasets;

[0031] S3. Use MapReduce-based IVKNN to perform distributed kNN ...

Embodiment 2

[0047] Embodiment 2: This embodiment can be implemented as an independent technical solution or as a further illustration of each solution in Embodiment 1. This embodiment provides a large-scale neighbor query method based on MapReduce and inverted Thiessen polygons. This method It is an efficient algorithm based on MapReduce that uses Voronoi diagrams to process kNN queries. It can also solve the future development trend of wireless, networked, and mobile medical call systems. This embodiment also improves on the deficiencies in the prior art, and has good efficiency and scalability. In order to achieve the above object, the execution steps of the technical solution adopted in this embodiment are as follows: Carry out the establishment of the large-scale neighbor query index of MapReduce and inverted Thiessen polygon; MapReduce is a kind of current popular programming frame based on cloud platform, it can Process and generate large data sets, which leverage shared-nothing clu...

Embodiment 3

[0055] Example 3: With the rapid development of social security services today, people's material living standards are improving day by day, and the demand for medical services has become more humane and personalized. At the same time, more and more people need more convenient and perfect medical services.

[0056] With the rapid growth of mobile communication and location-based service-related technologies, technologies such as cloud computing, big data, Internet of Things, mobile computing, and spatial positioning have gradually matured, such as GPS, cameras, and Bluetooth data. This makes the storage and processing of various spatial data or objects face great challenges. Therefore, with the development of informatization, applications such as electronic medical records, nursing call center systems, and large-scale medical databases in the medical service industry are also developing rapidly, playing a greater role in improving work efficiency, improving medical services, a...

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Abstract

The invention discloses a large-scale neighbor query system based on MapReduce and an inverted Thiessen polygon and belongs to the fields of cloud computing and big data. The system is used for improving indexing efficiency of existing data query methods. The system is technically characterized in that multiple instructions are stored in the system, and the instructions are suitable for being loaded and executed in a processor, wherein inverted Voronoi indexes based on MapReduce are constructed; the inverted Voronoi indexes are used to perform partitioning on a dataset R and a dataset S; and an IVKNN based on MapReduce is used to perform distributed kNN query. The system has the advantages that algorithm indexing efficiency can be improved, and MapReduce is no longer influenced by space and time.

Description

technical field [0001] The invention belongs to the fields of cloud computing and big data, and relates to a MapReduce index that can effectively improve query efficiency in a distributed environment. Background technique [0002] MapReduce is a popular cloud-based programming framework that can process and generate large data sets, and it utilizes shared-nothing clusters to support data-intensive applications. The specific processing steps are: in the distributed cache system, when a MapReduce task processes a key / value pair, a set of intermediate key / value pairs is generated in the map function, and all intermediate values ​​are merged according to the same intermediate key, Each map is independent of other operations, i.e. all maps can be executed in parallel. A group of "reducers" in MapReduce can perform reduction operations, and the output of Map operations with the same key can be reduced to the same reducer at the same time. However, running a reduction process alo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/24532G06F16/2471G06F16/278G06F16/319
Inventor 季长清汪祖民刘艳吴锐李泽宇
Owner DALIAN UNIV
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