Space keyword query method based on social perception in distributed environment

Abstract

The invention discloses a space keyword query method based on social perception in distributed environment. The method comprises: through analyzing a Top-k query problem, providing an index for solving the problem and a query framework, an index structure introducing social information, using a retrieval characteristic of keywords, providing a Top-k query algorithm based on social perception on the basis of a SDPR distributed index structure, through inputting a query parameter, a result being first k objects with highest rankings in a data set, the objects satisfying a specific ranking function. Through space keyword query with weight, the method makes query more accurate, and a more accurate result can be obtained.

Description

technical field [0001] The invention relates to the technical field of retrieval, in particular to a social perception-based spatial keyword query method in a distributed environment. Background technique [0002] With the development of the Internet, various social network service products have emerged as the times require. These social network services are widely used and have become the most popular applications among users. Therefore, location-based social networks also contain a large number of valuable and meaningful Location data and spatial data have gradually become an important research object of academic research. The so-called spatial data is the data containing geographical location and text information. It has become a frequent operation to query the geographic information and text information in space. Spatial keyword query is a large number of In the spatial object of the query, the geographically related and text-related data are found. [0003] In recent y...

AI Technical Summary

Problems solved by technology

[0005] With the increasing number of users, a large amount of social data is generated, and the spatial data and social data are growing exponentially, resulting in a larger and larger search space. Traditional data processing technologies have many problems when faced with such a huge amount of data. The problem is that the cost of processing data is very high, and there is a problem of low efficiency. The parallel processing of spatial data is difficult, and parallel programming is difficult.

The efficiency of query processing in a centralized environment is facing great challenges, and the traditional spatial keyword query can no longer solve the current situation

[0006] Although the current search engine technology is very mature and can return a large amount of relevant data to users in a short period of time, the results returned by the search engine are not necessarily related to the query user, and the social relationship of the query user is not considered, and the returned results may not be relevant. not the user's concern

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