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Skyline Query Method Based on Space Time Series Data Stream Application

A query method and data flow technology, applied in the field of Skyline's query system, can solve problems such as poor scalability and poor universality

Active Publication Date: 2021-03-26
DALIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For example, some Skyline algorithms based on MapReduce have modified the Hadoop framework, but there are still problems of poor scalability and poor universality
The MapReduce-based dynamic Skyline query method that we researched and designed before can only perform offline batch processing of non-real-time data, and cannot be well used for real-time data query

Method used

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  • Skyline Query Method Based on Space Time Series Data Stream Application
  • Skyline Query Method Based on Space Time Series Data Stream Application
  • Skyline Query Method Based on Space Time Series Data Stream Application

Examples

Experimental program
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Effect test

Embodiment 1

[0032] A skyline query system based on space time series data stream applications. Invention steps are as follows:

[0033] S1: Based on space timing division:

[0034] We will split the continuous time series into time slices by time windows. Such as figure 1 As shown, the method is as follows: Given a set of objects P, each data point p k The time attribute value (t) in a bounded interval [T min , T max ], construct a uniform partition {t 0 ,...,t B},t i Definition:

[0035] t i =T min +l×i, l=(T max -T min ) / B,i=0,...,B;

[0036] to form a set of time slices {b 0 ,...,b B-1}, each time slice b i =[t i ,t i+1 ), with a fixed length l. The time attribute value of each point is mapped to time slice b s(t) ∈{b 0 ,...,b B-1}, where s(t) is defined as B is the number of evenly divided bounded intervals.

[0037] The value of the interval fixed length (l) of different granularities depends on the actual application situation. In order to reduce the amount...

Embodiment 2

[0054] This embodiment relates to the specific application of the Skyline query method based on the spatial time series data stream application described in Embodiment 1:

[0055] The skyline query system based on the space time series data flow application is used for mobile medical calls. The cloud center service system provides spatial grid pruning strategies and continuous network medical data monitoring to execute dynamic Skyline and global Skyline algorithms, and input the thresholds of each attribute And the query result is sent according to the end of the execution time to improve the attributes of the hospital. That is, the system performs the following steps:

[0056] S1. The cloud center service system provides module index data structures with distributed dynamic Skyline and global Skyline algorithms. At the same time, we use the Spark streaming system to start multiple Maps to read time stream data, and each Map reads different HDFS data. Fragments generate data ...

Embodiment 3

[0059] The skyline query method based on the space time series data stream application in Example 1 is used for epidemic detection. First, we will divide the time series of epidemic monitoring into several time segments according to the time window, and then perform Skyline query on the data of each time segment. Static query. For the time object set P when the epidemic occurs, each data point p k The time attribute value (t) in a bounded interval [T min , T max ], construct a uniform partition {t 0 ,...,t B},t i The definition of t i =T min +l×i, l=(T max -T min ) / B, i=0, . . . , B. Form a collection of time slices {b 0 ,...,b B-1}, each time slice b i =[t i ,t i+1 ), with a fixed length l. The time attribute value of each point is mapped to time slice b s(t) ∈{b 0 ,...,b B-1}, where s(t) is as Among them, the value of interval (l) of different particle sizes depends on the actual monitoring time. At the same time, in order to reduce the amount of calcula...

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Abstract

The invention discloses a spatial time sequence data stream application-based Skyline query method, belongs to the field of application of a dynamic Skyline query in a data stream, and aims to solve the problem in real-time query processing of massive data. According to the technical key points, the method comprises the steps of S1, segmenting a continuous time sequence into a plurality of time slices according to time windows based on spatial time sequence division; S2, for each time slice, generating a grid inverted index; and S3, mapping moment query points to corresponding Skyline grids, then obtaining global Skyline grids by using a global Skyline grid calculation method to serve as a candidate set, performing the dynamic Skyline query on network node data in the candidate set according to a time sequence, and performing calculation to obtain an effective global Skyline result. The method has the effects that the result query is performed at a moment when executive time is over, so that the result is more accurate and conforms to the actual condition.

Description

technical field [0001] The invention relates to the application field of dynamic skyline query in data flow, and is a skyline query system based on space time series data flow application. The system involves large-scale data analysis, space time series massive data processing, and global skyline calculation. Background technique [0002] With the rapid development of the Internet and the Internet of Things and the wide application of technologies such as social networks and cloud computing, massive data technology has developed rapidly. Massive amounts of data are collected and recorded for research and analysis in science, engineering and business. According to the latest research, data sources such as the global Internet, mobile Internet, and GPS networks generate more than 2.5×10 18 Bytes of massive data, and these massive data come from a wide range of sources. The data on the Internet doubles every two years, and the Internet of Things, mobile Internet, Internet of V...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/22G06F16/2457G06F16/9537G16H40/67
CPCG06F16/2264G06F16/2457G06F16/9537
Inventor 季长清秦静谢雨婧李媛媛
Owner DALIAN UNIV
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