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Reverse nearest neighbor query method and device based on semantic track big data

A technology of trajectory big data and query method, applied in the field of communication, can solve problems such as the inability to achieve RkNNST

Active Publication Date: 2019-03-19
SHIJIAZHUANG TIEDAO UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, in the prior art, spatial indexes for semantic trajectories, such as STR-tree, MV3R-tree, and MTSB-tree, only focus on global spatial features, while spatial text indexes such as IR tree and I3 only consider snapshots of spatial text objects position, which cannot meet the needs of implementing RkNNST on semantic trajectories

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  • Reverse nearest neighbor query method and device based on semantic track big data
  • Reverse nearest neighbor query method and device based on semantic track big data
  • Reverse nearest neighbor query method and device based on semantic track big data

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Embodiment Construction

[0024] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0025] In order to illustrate the technical solutions of the present invention, specific examples are used below to illustrate.

[0026] refer to figure 1 , figure 1 A schematic diagram of a general example of reverse nearest neighbor query for the semantic track provided by the embodiment of the present invention.

[0027] t1, t2 and t3 are three semantic trajectories. Each location is associ...

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Abstract

The method is suitable for the technical field of communication. The invention provides a reverse nearest neighbor query method and device based on semantic track big data. The method comprises the steps that a track index IMC tree is established according to a track data set, the IMC tree comprises an inverted arrangement table and a plurality of MC trees, the inverted arrangement table is used for storing keyword vocabulary tables, and the MC trees comprise track abstracts used for storing track local positions; establishing a query index WIBR tree according to the query data set; an IMC tree and a WIBR tree are accessed alternately, a query result rnk (q) of query q is determined, and the query result is that the query q in the IMC tree serves as a minimum related sub-track of nearest neighbor query. According to the reverse nearest neighbor query method and device based on the semantic track big data, reverse nearest neighbor query on a semantic track can be achieved, and a more specific and exact query result can be obtained.

Description

technical field [0001] The invention belongs to the technical field of communication, and in particular relates to a reverse nearest neighbor query method and device based on semantic trajectory big data. Background technique [0002] With the popularity and application of mobile devices and social networks, a large amount of semantically rich trajectory data (ie, semantic trajectory) is generated. The semantic trajectory includes a series of spatial text points, and each spatial text point includes a spatial position and a set of keywords. The keyword set is the semantic label of the spatial text point. Due to the rich knowledge contained in semantic trajectories, semantic trajectory retrieval has attracted great attention from commercial organizations and research institutions. Existing research on semantic trajectory retrieval mainly focuses on K-nearest neighbor query (kNN, k-NearestNeighbor). Reverse nearest neighbor query, as one of the important query types, has no...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/953G06F16/9536G06F16/31G06F16/33G06Q50/00
CPCG06Q50/01
Inventor 潘晓吴雷
Owner SHIJIAZHUANG TIEDAO UNIV