Abstract based time-series data index building method

A time series and construction method technology, applied in the field of big data and databases, can solve problems such as large bandwidth, large data transmission volume, and high time cost

Active Publication Date: 2017-06-13
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method needs to feed back a large amount of data, which consumes a lot of bandwidth during data transmission and causes high latency.
The second is to first compress the query results and then return them to the visualization module, which can greatly reduce the bandwidth consumption during data transmission, but when compressing the data, it is still necessary to scan all the data that meets the query conditions. The time cost of this process is still high
However, both methods have the disadvantages of extended query time and large data transmission volume.

Method used

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  • Abstract based time-series data index building method
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  • Abstract based time-series data index building method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0143] This embodiment elaborates in detail that when an abstract-based time-series data index construction method of the present invention is applied to stock data, the index construction algorithm is first used to construct the data index, and then an adaptive time-series data query is used based on the constructed index structure Algorithm query visualization process.

[0144] The time series data T={9.33, 9.91, 10, 10.43, 10.48, 10.32, 10.68, 11.11, 11.16, 11.26, 11.43, 11.63, 11.89, 11.67, 11.54, 11.34, 11.22, 10.79, 11.07, 11.1, 10.69, 10.98, 10.92, 10.92, 10.92, 10.08, 10.56, 10.58, 11.27, 12.46, 12.49, 12.51, 12.6, 12.53, 12.15, 12.72, 12.81, 12.8, 12.51, 12.65}, the corresponding time is from 1 to 40

[0145] figure 1 It is an abstract-based time series data index construction method of the present invention and a schematic flow chart of index construction in this embodiment; it can be seen from the figure that index construction includes the following steps:

[014...

Embodiment 2

[0176] According to the parameters described in Embodiment 1, this example specifically illustrates the algorithm for constructing the tree index structure in Step 4 of the present invention and the execution process of Step 4 in Embodiment 1.

[0177] The specific process is: according to the nodes of the lower layer, the bottom-up method is adopted to merge and generate the nodes of the upper layer, and the root tree index structure is constructed from the bottom up.

[0178] Specifically, in the implementation of this example, the process of merging the nodes at the bottom layer, i.e. the nodes at the leaf layer, to generate nodes at the middle layer is as follows:

[0179] Step 4): Using the data summary information obtained in step 3), construct a tree index structure;

[0180] The data transmitted in the previous step is the data summary information of 10 time series segments, denoted as N j , subscript j Indicates the order of the corresponding time series segments, w...

Embodiment 3

[0201] According to the parameters described in Embodiment 1 and the index structure constructed in Embodiment 2, this example specifically elaborates the incremental method from step A to step F in an adaptive time series data query algorithm that uses the present invention to construct an index structure Inquiry process.

[0202] Specific to the implementation of this example, follow the following process:

[0203] Step (1): Read the upper part of the tree-like index structure saved based on step 5 into the memory, construct the query statement, initialize the query result array and the maximum time delay and query acceptable to the user from the start of the query to the display of the data visualization The time required to go deep into a layer;

[0204] Specific to the implementation of this example, the index structure is as follows image 3, has a three-layer structure. First, read the root node of the tree-like index structure and the intermediate nodes of the second...

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Abstract

The invention discloses an abstract based time-series data index building method and belongs to the technical field of big data and databases. The core content lies in that time-series data are divided into small time segments, the time segments are then compressed with a linear fitting method to form abstracts of the segments, and a tree index structure is built for the compressed abstract data; when visualized query is needed, an incremental visualization technique on the basis of the index structure is adopted to ensure the visualization method to be accurate and efficient, and multiple times of queries and uses can be provided for building the index structure once. In the constructed tree index structure, fitting error of nodes top and down is lessened, display can be queried according to different query accuracies, the index structure is searched in querying results, the amount data is greatly reduced as compared with that in querying original data by scanning each time, the query results are ensured to be returned within time delay specified by a user, and the visualization method is ensured to be efficient.

Description

technical field [0001] The invention relates to an abstract-based time series data index construction method, which belongs to the technical field of big data and databases. Background technique [0002] Time-series data represents streaming data generated in sensor networks, financial markets, healthcare, surveillance, and many other fields. With the proliferation of high-frequency streaming data sources, there is an urgent need for interactive analysis and real-time visualization techniques for large amounts of data. Examples include trend analysis, pattern recognition, correlation analysis, interactive data discovery, and more. [0003] A typical example of time series data visualization is to display the value of a sensor over a specific time range. The current visualization tools deal with this problem, generally divided into two methods: the first method first queries all values ​​that meet the conditions from the database, and renders them to the visualization modul...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/2246G06F16/2455G06F16/248
Inventor 曹朝曲大成张林
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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