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Dynamic Skyline query method based on cloud computing

A query method and cloud computing technology, which is applied in the fields of spatial database, big data processing, and cloud computing, can solve the problems of low algorithm efficiency, high software and hardware requirements, and high system resource occupation, and achieve narrow candidate sets, high query efficiency, and The effect of reducing computational overhead

Inactive Publication Date: 2017-04-26
DALIAN JIAOTONG UNIVERSITY
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  • Summary
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Massive data is stored in these cloud computing platforms. Due to the large amount of data, these massive data processing technologies have very high requirements on software and hardware, and occupy a lot of system resources, which brings about the problem of low algorithm efficiency.

Method used

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  • Dynamic Skyline query method based on cloud computing
  • Dynamic Skyline query method based on cloud computing
  • Dynamic Skyline query method based on cloud computing

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] Embodiment 1: as Figure 5 As shown, a dataset of used cars. The condition of the user’s ideal second-hand car, the power is 150 horsepower, and the mileage is 5K kilometers, that is, the coordinate of the reference object is ref=. At the query time point, its dynamic Skyline result is a second-hand car whose condition is close to the user’s expectation car. In short, when the data set is given, the static Skyline results can already be determined. If the time changes, and a query request is specified, when considering the dominance relationship between objects in the query data set relative to the query request point, the result of such a Skyline query is uncertain, that is, the dynamic Skyline query depends on the difference of the query reference object , the query results are also different. If it is considered that the user’s query may change, the used car data object queried will also change, and when there is a dominance relationship, such a multi-factor query ...

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Abstract

The invention discloses a dynamic Skyline query method based on cloud computing. The method is used for performing global Skyline lattice computing for dynamic Skyline query of mass data. The whole computing process is a DAG execution process, and obtained data points serve as a candidate set for dynamic Skyline computing; during dynamic Skyline computing, the data points in the candidate set are scanned, and the data points are converted into coordinates to convert dynamic Skyline computing into static Skyline computing with a query point being an origin of coordinates; in the DAG execution process, a Tez-based MapReduce operation is started, a Map function is utilized for parallel performance in all nodes, and the result is cached; next, another MapReduce operation is started to check the Skyline lattice dominance relation among the data points after conversion, Skyline computing is performed, and the result is returned. A polling mode is adopted to perform global Skyline lattice computing, Skyline computing is performed after effective pruning, and therefore a large amount of computing is saved.

Description

technical field [0001] The invention belongs to the field of cloud computing, spatial database and big data processing, and relates to a dynamic data query method. Background technique [0002] The Skyline algorithm is the process of returning a set of data objects that are not dominated by any other data objects from a given data set. The result of the query is a part of the special data objects in the data set, that is, the boundary points of the data set. Its main goal is to extract the data set The boundary points of are provided to the user as a candidate set for decision-making. The new dynamic Skyline query algorithm we designed has two characteristics, the first is to support dynamic Skyline query, the second is to support distributed computing environment, it can run in the cloud computing system for big data processing, and the third is to use stream-based The advanced dynamic DAG optimization technology is more efficient than previous distributed dynamic Skyline ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/2228G06F16/2471
Inventor 李媛媛季长清肖鹏邓武徐克圣
Owner DALIAN JIAOTONG UNIVERSITY
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