Establishment of a distributed spatiotemporal multidimensional indexing method for mobile medical services

A mobile medical and distributed technology, applied in the field of data indexing, can solve the problems of not simple and complicated space-time indexing efficiency and scalability, and achieve the effect of meeting high concurrency requirements and improving satisfaction

Active Publication Date: 2020-01-17
大连乾函科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, improving the efficiency and scalability of spatio-temporal indexes is not straightforward
Therefore, distributed index calculations face very challenging problems, such as sequential calculations and complex indexes and support for query types, etc.

Method used

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  • Establishment of a distributed spatiotemporal multidimensional indexing method for mobile medical services
  • Establishment of a distributed spatiotemporal multidimensional indexing method for mobile medical services
  • Establishment of a distributed spatiotemporal multidimensional indexing method for mobile medical services

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Embodiment 1

[0022] Embodiment 1: The present invention provides a distributed spatio-temporal multi-dimensional index method for mobile medical services to improve the efficiency of medical services and make full use of medical resources. The invention also improves the shortcomings of the query method in the prior art to improve accuracy and real-time performance, thereby realizing multi-dimensional indexing.

[0023] In order to achieve the above purpose, the technical solution adopted by the present invention is as follows: Firstly, the research and overview of the present situation of the distributed indexing method are carried out. Then, a new distributed efficient indexing method is proposed, which allows spatio-temporal high-dimensional indexing of mHealth resources in a distributed environment. Third, carry out big data experiments, which strongly prove the effectiveness and scalability of the indexing method, and also prove that the method can effectively support time and space q...

Embodiment 2

[0038] Embodiment 2: Study the factors affecting the performance of the algorithm through experiments. Mainly, in-depth experiments are conducted to evaluate the state-of-the-art spatial algorithms for IDI and to study IDI in detail. We first introduce the details of the experimental setup, including datasets, default parameters, and system environment, then evaluate the performance and analyze the experimental results.

[0039] (1) Setup and configuration: Build a server cluster with 32 servers. Each server is composed of dual-core AMD2.0GHz CPU, 73GB SCSI hard disk and 8GB memory, and Ubuntu10.10 server operating system and hadoop1.2 are installed. Each slave node cluster runs a TaskTracker and DataNode maintenance process runs. The DFS block default size is 64MB. Apply both solutions on the same cluster of nodes. In the experiment, the Real Data Set (RDS), Dalian's medical vehicle GPS dataset, including 110 systems of medical ambulances and traffic conditions in the cit...

Embodiment 3

[0043] Embodiment 3: The efficiency of index creation is significantly improved: firstly, the efficiency of constructing MRITS, MRIV, MRTree and MRV indexes is evaluated by changing the number of cluster nodes from 2 to 32. refer to Image 6 , which illustrates the impact of node data changes on the index construction time of RSD and SDS datasets. These three index structures increase almost linearly as the cluster nodes grow. However, the construction time of MRITS is the shortest compared to the number of nodes of MRIV, MRtree and MRV. Especially when the number of nodes is 8 and 16, MRITS is 50% faster than MRIV, 6 times faster than MRTree, and 4 times faster than MRV. This is because on the basis of the existing medical area, the time-space index of the inverted medical area is directly divided, no pre-calculation is required, and only the grid indexing process is required. The index structure based on R-tree and Voronoi requires a complex construction process. Due to ...

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Abstract

The invention relates to a distributed space-time multi-dimensional indexing method for mobile medical service, which belongs to the field of data indexing and is used for solving the problem of multi-dimensional indexes. The technical point is: establishing a grid index based on a regional center; and establishing an inverted space-time double index which comprises an inverted time index and an inverted spatial index, and the effect is that since the Skyline query is in the form of a multi-result set, so long as the data set is not empty, the relatively optimal medical service resource can besent to the patient all the time. It is sent to the patient to avoid the situation that the query result is empty, which can greatly improve the user's satisfaction.

Description

technical field [0001] The invention belongs to the field of data indexing, and relates to a method for establishing a distributed time-space multidimensional index for mobile medical services. [0002] technical background [0003] Mobile medical services have great prospects. It not only makes full use of medical resources, but also relieves the pressure on social medical services and meets the medical needs of patients. It is rapidly becoming a new application prospect in the medical field. Mobile medical services are generally regarded as a typical representative of the Internet + smart medical field, which is a combination of private and public medical service models. How to improve the efficiency of medical services and how to make full use of medical resources has always been a concern of the community. [0004] The traditional indexing approach is to perform a linear or continuous scan of the entire connected mHealth resources in order to find the appropriate resourc...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H80/00G06F16/22G06F16/2458
CPCG06F16/2264G06F16/27
Inventor 季长清秦静汪祖民武晓霞
Owner 大连乾函科技有限公司
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