Streaming time series data dimensionality reduction and simplified representation method based on piecewise linear representation

A technology of time series and segmented data, which is applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., and can solve the problems of "real-time" and data representation accuracy.

Inactive Publication Date: 2017-07-18
SHANDONG UNIV
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AI Technical Summary

Problems solved by technology

[0012] Traditional data dimension reduction and simplified representation methods have the problem of not being able to balance "real-time" and data representation accuracy

Method used

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  • Streaming time series data dimensionality reduction and simplified representation method based on piecewise linear representation
  • Streaming time series data dimensionality reduction and simplified representation method based on piecewise linear representation
  • Streaming time series data dimensionality reduction and simplified representation method based on piecewise linear representation

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Embodiment

[0055] A time series stream data dimensionality reduction and simplified representation method based on piecewise linear representation, such as figure 2 As shown, the steps are as follows:

[0056] S1, preset data segmentation and compression parameters, including single point maximum data fitting error ME_SP, segmented maximum data fitting error ME_ES, data trend point slope measurement parameter μ, data trend point time range measurement parameter ρ;

[0057] The single point maximum data fitting error ME_SP and the segmented maximum data fitting error ME_ES are optimization parameters specified by the user in advance. Through ME_SP and ME_ES, the accuracy of the data representation can be limited and optimized, and the data accuracy specified by the user can be achieved. Simplify the representation of the corresponding data. Data trend point slope measurement parameter (μ) and data trend point time range measurement parameter (ρ) are important parameters for screening data tre...

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Abstract

The invention relates to a streaming time series data dimensionality reduction and simplified representation method based on piecewise linear representation. The method comprises the following steps that: S1: presetting data segments and compression parameters; S2: carrying out data scanning on streaming time series data in a slide window way, and entering a streaming data buffer zone; S3: judging whether the fitting error of an initial segmented data segment exceeds an ME_ES (Maximum Error for Entire Segment) or not, carrying out reservation if the fitting error of the initial segmented data segment exceeds the ME_ES, and marking the initial data segment as "inseparable", and if the fitting error of the initial segmented data segment does not exceed the ME_ES, carrying out secondary optimal segmentation; and S4: moving the data segment which is marked as "inseparable" in the streaming data buffer zone out of the streaming data buffer zone, judging whether the streaming time series data to be processed is in the presence or not, if the streaming time series data to be processed is in the presence, returning to the S2, and otherwise, ending. By use of the method, data dimensionality reduction execution efficiency is guaranteed to a high limit, the fitting accuracy of data simplified representation is optimized to a certain range, and accuracy and the execution efficiency of data representation can be improved.

Description

Technical field [0001] The present invention relates to a dimensionality reduction and simplified representation method for time series stream data based on piecewise linear representation, in particular to a “massive” and “high-dimensional” time series based on piecewise linear representation (PLR) Data dimensionality reduction and simplified representation of streaming data belong to the technical field of big data analysis and data mining. Background technique [0002] With the advent of the Internet era, mobile communication technology and Internet of Things technology have been widely used and promoted. Various commercial interaction activities, various sensor equipment and detection equipment in the manufacturing industry have produced a large amount of time-based business data information. This data information is not only closely related to a specific moment, but also has data information as a whole. The characteristics of time continuity: it is constantly produced like ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/217
Inventor 胡宇鹏展鹏李学庆
Owner SHANDONG UNIV
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