A query event identification and detection method and system for time series big data

A time-series data and detection method technology, applied in the fields of artificial intelligence and big data computing, can solve problems such as poor flexibility, frustrated data processing flexibility, and inability to meet changing user query requirements, and achieve the effect of reducing work pressure

Active Publication Date: 2020-02-04
重庆重大知识产权运营有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But there are also shortcomings. People first need to clarify the data that may be related, which leads to the frustration of data processing flexibility, and at the same time cannot meet the changing user query needs, and general query users are also non-professionals, unable to complete similar data processing operations.
In addition, in the face of massive data sets, traditional technical solutions consume a lot of time complexity and space complexity, have poor flexibility, and cannot meet the query requirements very well.

Method used

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  • A query event identification and detection method and system for time series big data
  • A query event identification and detection method and system for time series big data
  • A query event identification and detection method and system for time series big data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0068] A query event identification and detection method oriented to time series big data provided in this embodiment includes the following steps:

[0069] S1. Build a domain ontology library for a specific domain;

[0070] S2. Define the atomic feature operator set according to the domain object;

[0071] The following steps are required to define the atomic feature operator set according to the domain object:

[0072] S20. Classifying common data features of domain concepts;

[0073] S21. Define the feature operator to include calculation parameters;

[0074] S22. Construct a feature operator, and name the operator according to the feature;

[0075] S23. Deploy feature operators to each node of the big data computing cluster.

[0076] The process of defining the mean characteristic operator for the data in the financial field is as follows:

[0077] Step 1 is to classify the financial data. The financial data can propose the mean value feature, variance feature, amplit...

Embodiment 2

[0128] Such as figure 1 As shown, the working principle diagram of the query event recognition and detection method for time series big data includes a human-computer interaction interface module, a semantic reasoning module, a domain ontology library module, a time series data feature calculation cluster module, and an advanced query event generation and recognition module.

[0129] The main function of the human-computer interaction interface is to receive user query requests, and the query requests are natural language text input or voice interaction.

[0130] The main functions of the domain ontology library module are: building the basic knowledge of specific domains, the hierarchical logic of specific domain concepts, and the predicate logic representation of specific domain concepts;

[0131] The main function of the semantic reasoning module is: perform word segmentation and feature extraction on the user query request, and then use the reasoning engine to perform sema...

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Abstract

The invention discloses a timing sequence big data oriented query event recognition and detection method and system. The method includes the following steps: establishing a domain ontology library, and defining an atomic feature operator set; acquiring a user demand, obtaining an inference result according to semanteme, and acquiring an associated data set based on the inference result and a timing sequence data set; processing the associated data set based on a big data feature calculation platform to obtain a timing sequence data feature set; and establishing an advanced query event according to the timing sequence data feature set, and feeding back and recognizing the detection result. The timing sequence big data oriented query event recognition and detection method can solve the problem that a user natural language query request cannot directly be recognized by a computer in big data query, establishes the big data advanced query event through a domain ontology technique, overcomes a gap between mass data and the conventional databases, overcomes a gap between non-professionals and a complex data query system, relieves workload of developers of the conventional database systems, and can allow the data query system get more flexible and convenient.

Description

technical field [0001] The invention relates to the field of big data calculation and artificial intelligence, in particular to a query event identification and detection method for time series big data. Background technique [0002] With the advent of the era of big data, traditional database systems have been unable to meet the needs of massive data queries. At the same time, there have been many non-computer professionals who use data systems to obtain complex queries from massive data. The gap between platforms and the gap between traditional data platforms and massive data storage becomes even more important. [0003] Usually, in the face of massive data, the traditional approach is to establish multiple versions of the database system, and store the data separately according to the field of the data. To handle multiple requirements at the same time, use the following approach: [0004] (1) Develop multiple sets of data query programs. When responding to data query ne...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/242G06F16/2457
CPCG06F16/243G06F16/24575
Inventor 钟将妙晓龙
Owner 重庆重大知识产权运营有限公司
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