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Digest index generation method for time sequence key value type industrial process data

A technology for industrial process and time-series data, which is applied in unstructured text data retrieval, electrical digital data processing, special data processing applications, etc., can solve problems such as difficult, fast and efficient query of time-series data, so as to avoid missing check behavior and improve Efficiency, reducing the effect of string comparisons

Inactive Publication Date: 2018-08-31
CHONGQING UNIV
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AI Technical Summary

Problems solved by technology

The disadvantage is that most of the current time-series data indexing methods are based on a single dimensionality reduction processing representation or symbolic representation method, which makes it difficult to quickly and efficiently query time-series data

Method used

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  • Digest index generation method for time sequence key value type industrial process data
  • Digest index generation method for time sequence key value type industrial process data
  • Digest index generation method for time sequence key value type industrial process data

Examples

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Embodiment

[0026] Example: such as figure 1 and figure 2 Shown; A summary index generation method for time-series key-value industrial process data, which includes:

[0027] S1: Obtain time-series key-value industrial process data;

[0028] S2: Perform smooth noise preprocessing on the acquired time series data to obtain time series data with time stamps;

[0029] The specific steps of performing smooth noise preprocessing on the acquired time series data in the step S2 are as follows:

[0030] S21: Perform deviation detection on the original time series data; find noise, outliers and unusual values, examine the definition domain and data type of each attribute and the range of acceptable values ​​for each attribute;

[0031] S22: By inspecting the values ​​in the data field, smoothing the ordered data by obtaining the smoothed data value according to the bin average method in the binning method, discretizing the continuous data, obtaining the preprocessed time series data, and incre...

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Abstract

The invention discloses a digest index generation method for time sequence key value type industrial process data. The method comprises the steps that S1, the time sequence key value type industrial process data is acquired; S2, smooth noise preprocessing is performed on acquired time sequence data to obtain time sequence data with timestamps; S3, a symbolic aggregate approximate representation method is adopted to represent the time sequence data obtained after preprocessing; and S4, results obtained after symbolic aggregate approximate representation are subjected to mode clustering, and theresults obtained after mode clustering are made into indexes by the adoption of a prefix algorithm. The method has the advantages that based on the data preprocessing method, the symbolic aggregate approximate representation method and the prefix tree algorithm are fused to form the digest index generation method for the time sequence key value type industrial process data; and through the method, the dimension of the original time sequence data can be lowered, features of the original data are effectively extracted, and the digest index generation method is realized by the adoption of the prefix tree algorithm.

Description

technical field [0001] The invention relates to the technical field of time series data mining, in particular to a method for generating a summary index of time series key-value industrial process data. Background technique [0002] Time series data widely exists in industrial processes, climate detection, medical diagnosis and other fields. Time-series key-value industrial process data, as a typical time-series data, has the characteristics of high dimensionality and massive volume, so the traditional data summary index generation method cannot analyze this type of data well. Symbolic aggregation approximate representation is a mature symbolic representation method, which is widely used in time series data preprocessing and pattern discovery. Its advantage is that it can use more mature and efficient data mining algorithms for string operations. A prefix tree is a key tree structure, which is a variant of a hash tree. A typical application is to count and sort a large nu...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/322G06F16/3331G06F16/35
Inventor 张可韩载道李媛
Owner CHONGQING UNIV
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