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Indexing method supporting time series data aggregation function

A technology of time series data and aggregation function, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of not supporting query operations and increasing the disk overhead of query operations, avoiding recursive traversal operations and reducing disk IO times, the effect of avoiding disk overhead

Inactive Publication Date: 2016-12-07
TSINGHUA UNIV
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Problems solved by technology

The formation mechanism of the materialized view itself determines that it does not support any range of query operations
In addition, as the amount of data increases, the disk overhead of query operations will also increase

Method used

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  • Indexing method supporting time series data aggregation function
  • Indexing method supporting time series data aggregation function
  • Indexing method supporting time series data aggregation function

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Experimental program
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Embodiment Construction

[0063] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0064] 1. an indexing method that supports time series data aggregation functions, is characterized in that, comprises two steps:

[0065] Step 1. Define the data model and query requirements for time series data

[0066] Definition 1: Data item: A data item D (data point) is a triplet (s, t, v), where s is the sensor ID and t is the timestamp, where s and t constitute a globally unique identifier, v is the value of the sensor, and the continuous time data items of the same sensor constitute the time series data. On this basis, the query problem to be solved in this paper is defined: on the time series data, the query time window t 1 ~t 2 (t 1 and t 2 is the most value and variance statistical information of the time series data at any time);

[0067] Definition 2: Summary information: In time series data, the statistical information of k consecutive dat...

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Abstract

The invention discloses an indexing method supporting time series data aggregation function, which supports fast ad hoc query of a simple aggregation operation. The basic thought of the method is that a summary table and segment trees (Segment Tree) are combined, and a segment forest model formed by multiple segment trees is established on the summary table, so that the full table scan operation of the summary table is avoided. Meanwhile, through dynamically constructing a segment forest in a bottom-up method, the defect that the conventional segment tree does not support increase is avoided. In addition, a query algorithm directly positions index data through calculation through calculation, the recursive traversal operation of the segment forest is avoided, and the frequency for disk IO is reduced. An experimental result shows that through adoption of a calculation query way of the summary table and the segment forest, the frequency for disk IO is effectively reduced and the query performance is remarkably improved.

Description

technical field [0001] The invention relates to a method for automatic type selection and parameter configuration of a big data system in the process of big data application development, and belongs to the technical field of computer database management. Background technique [0002] With the development of sensor technology and the popularization of the Internet, the speed of data collection and information dissemination has reached an unprecedented level. It is very important for aggregated information such as extreme value and mean value of data, how to quickly and accurately obtain these aggregated information is the research focus of this paper. [0003] To satisfy such queries, the database must support fast aggregation operations on massive amounts of data in any time range. [0004] Traditional relational databases mainly use summary tables or materialized views to speed up aggregation queries. Among them, the materialized view is to preprocess the query command in...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/2282G06F16/2272
Inventor 王建民黄向东郑亮帆康荣龙明盛刘英博
Owner TSINGHUA UNIV
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