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
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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:
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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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