CN109710662A - A kind of various dimensions dynamical min analysis method based on transaction data - Google Patents
A kind of various dimensions dynamical min analysis method based on transaction data Download PDFInfo
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- 230000001360 synchronised effect Effects 0.000 claims abstract description 8
- 238000009412 basement excavation Methods 0.000 claims abstract description 7
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Abstract
The invention belongs to data quantizations to analyze applied technical field, and in particular to a kind of various dimensions dynamical min analysis method based on transaction data, it is characterised in that the following steps are included: a. obtains transaction details data；B. it is obtained in the detailed data that crawl obtains and supports to excavate fixed dimension and dynamic dimension；C. the dynamic dimension in caching step b；D. synchronous dynamic dimension field；E. it establishes and excavates level logic；F. every dimension of support is shown；G. current dimension is chosen to be excavated and analyzed, data storage pre-establishes incidence relation in the present invention, before solving the problems, such as can only single dimension analyze data, various dimensions excavation can more be accurately positioned specific data, and precision data is analyzed.
Description
Technical field
The invention belongs to data quantizations to analyze applied technical field, and in particular to a kind of various dimensions based on transaction data are dynamic
State mining analysis method.
Background technique
Since transaction data detailed data amount is more, more lowly needed by dimension so directly carrying out analysis efficiency to detail
Degree is counted in advance.The analysis main method of transaction data is to be counted not by detailed data according to different statistical dimensions
Then the achievement data of same latitude is stored in statistical data in the statistical form of different statistical dimensions again, root when being analyzed
It is analyzed according to different dimensions.
The analysis main method of transaction data is to count different latitude according to different statistical dimensions by detailed data
Achievement data, then statistical data is stored in the statistical form of different statistical dimensions again, according to difference when being analyzed
Dimension is analyzed.Although can analyze in this way, it can not accomplish association analysis, it can not result be further is dug to analysis
Pick, can not various dimensions navigate to problem.
Summary of the invention
The object of the invention is to propose that a kind of fast implement carries out dynamical min and analysis to the transaction data of various dimensions
Method, and data are intuitively shown, facilitate and counted.
The present invention in view of the above shortcomings of the prior art, provides a kind of various dimensions dynamical min based on transaction data point
Analysis method.
The purpose of the present invention is what is be achieved through the following technical solutions:
A kind of various dimensions dynamical min analysis method based on transaction data, it is characterised in that the following steps are included:
A. transaction details data are obtained；
B. it is obtained in the detailed data that crawl obtains and supports to excavate fixed dimension and dynamic dimension；
C. the dynamic dimension in caching step b；
D. synchronous dynamic dimension field；
E. it establishes and excavates level logic；
F. every dimension of support is shown；
G. current dimension is chosen to be excavated and analyzed.
Each dimension includes Transaction Name, server, network segment, static fields and transaction response rate.
Detailed step in the step c are as follows: conjunction is grouped according to the fixed dimension and dynamic dimension that obtain in step b
And achievement data of trading, data are cached later.
Excavation level logic in the step e is that can excavate downwards to return up simultaneously.
The dimension excavated in the step g can switch on demand, while can excavate downwards and return up.
Working principle:
After getting transaction details data, the every dimension for needing to carry out mining analysis is enumerated, according to every dimension of statistics
Data grouping merging is carried out, corresponding index value is calculated according to the calculating function of indices.Synchronous dynamic field, which merges, fixes
Dimension establishes excavation relationship level and realizes dynamical min options menu.To realize transaction data various dimensions dynamical min and divide
Analysis.
Beneficial effects of the present invention:
1. data storage pre-establishes incidence relation in the present invention, before solving the problems, such as can only single dimension analyze data, it is more
Dimension excavation can more be accurately positioned specific data, precision data analysis.
2. caching dynamic dimension in the present invention in advance, make that dimension can be excavated when analysis with Dynamic Display, it can be more accurate
Ground positions dynamic dimension, more accurately location data.
3. the dimension currently excavated in the present invention can switch on demand, while can excavate downwards and return up, so that dig
Pick dimension becomes more accurate.
Detailed description of the invention
It is of the invention aforementioned and be detailed description below and become more apparent upon when reading in conjunction with the following drawings, in attached drawing:
Fig. 1 is work flow diagram schematic diagram of the invention.
Specific embodiment
It is further illustrated below by several specific embodiments and realizes the object of the invention technical solution, need to illustrate
It is that claimed technical solution includes but is not limited to following embodiment.
Embodiment 1
A kind of various dimensions dynamical min analysis method based on transaction data, it is characterised in that the following steps are included:
A. transaction details data are obtained；
B. it is obtained in the detailed data that crawl obtains and supports to excavate fixed dimension and dynamic dimension；
C. the dynamic dimension in caching step b；
D. synchronous dynamic dimension field；
E. it establishes and excavates level logic；
F. every dimension of support is shown；
G. current dimension is chosen to be excavated and analyzed.
Each dimension includes Transaction Name, server, network segment, static fields and transaction response rate.
Embodiment 2
A kind of various dimensions dynamical min analysis method based on transaction data, it is characterised in that the following steps are included:
A. transaction details data are obtained；
B. it is obtained in the detailed data that crawl obtains and supports to excavate fixed dimension and dynamic dimension；
C. the dynamic dimension in caching step b；
D. synchronous dynamic dimension field；
E. it establishes and excavates level logic；
F. every dimension of support is shown；
G. current dimension is chosen to be excavated and analyzed.
Each dimension includes Transaction Name, server, network segment, static fields and transaction response rate.
Detailed step in the step c are as follows: conjunction is grouped according to the fixed dimension and dynamic dimension that obtain in step b
And achievement data of trading, data are cached later.
Embodiment 3
A kind of various dimensions dynamical min analysis method based on transaction data, it is characterised in that the following steps are included:
A. transaction details data are obtained；
B. it is obtained in the detailed data that crawl obtains and supports to excavate fixed dimension and dynamic dimension；
C. the dynamic dimension in caching step b；
D. synchronous dynamic dimension field；
E. it establishes and excavates level logic；
F. every dimension of support is shown；
G. current dimension is chosen to be excavated and analyzed.
Each dimension includes Transaction Name, server, network segment, static fields and transaction response rate.
Detailed step in the step c are as follows: conjunction is grouped according to the fixed dimension and dynamic dimension that obtain in step b
And achievement data of trading, data are cached later.
Excavation level logic in the step e is that can excavate downwards to return up simultaneously.
Embodiment 4
A kind of various dimensions dynamical min analysis method based on transaction data, it is characterised in that the following steps are included:
A. transaction details data are obtained；
B. it is obtained in the detailed data that crawl obtains and supports to excavate fixed dimension and dynamic dimension；
C. the dynamic dimension in caching step b；
D. synchronous dynamic dimension field；
E. it establishes and excavates level logic；
F. every dimension of support is shown；
G. current dimension is chosen to be excavated and analyzed.
Each dimension includes Transaction Name, server, network segment, static fields and transaction response rate.
Detailed step in the step c are as follows: conjunction is grouped according to the fixed dimension and dynamic dimension that obtain in step b
And achievement data of trading, data are cached later.
Excavation level logic in the step e is that can excavate downwards to return up simultaneously.
The dimension excavated in the step g can switch on demand, while can excavate downwards and return up.
Claims (5)
1. a kind of various dimensions dynamical min analysis method based on transaction data, it is characterised in that the following steps are included:
A. transaction details data are obtained；
B. it is obtained in the detailed data that crawl obtains and supports to excavate fixed dimension and dynamic dimension；
C. the dynamic dimension in caching step b；
D. synchronous dynamic dimension field；
E. it establishes and excavates level logic；
F. every dimension of support is shown；
G. current dimension is chosen to be excavated and analyzed.
2. a kind of various dimensions dynamical min analysis method based on transaction data according to claim 1, it is characterised in that: institute
Stating each dimension includes Transaction Name, server, network segment, static fields and transaction response rate.
3. a kind of various dimensions dynamical min analysis method based on transaction data according to claim 1, it is characterised in that: institute
State the detailed step in step c are as follows: be grouped according to the fixed dimension obtained in step b with dynamic dimension and merge transaction index
Data later cache data.
4. a kind of various dimensions dynamical min analysis method based on transaction data according to claim 1, it is characterised in that: institute
Excavation level logic in step e is stated to be while can excavate downwards and return up.
5. a kind of various dimensions dynamical min analysis method based on transaction data according to claim 1, it is characterised in that: institute
Stating the dimension excavated in step g can switch on demand, while can excavate downwards and return up.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113379551A (en) * | 2021-07-02 | 2021-09-10 | 华青融天(北京)软件股份有限公司 | Transaction data analysis method and device and electronic equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020070953A1 (en) * | 2000-05-04 | 2002-06-13 | Barg Timothy A. | Systems and methods for visualizing and analyzing conditioned data |
| CN101599088A (en) * | 2008-11-18 | 2009-12-09 | 北京美智医疗科技有限公司 | The mining multi-dimensional data system and method for medical information system |
| CN102467559A (en) * | 2010-11-19 | 2012-05-23 | 金蝶软件（中国）有限公司 | Multilevel and multidimensional method and device for analyzing data attributes |
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2018
- 2018-12-26 CN CN201811602383.4A patent/CN109710662A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020070953A1 (en) * | 2000-05-04 | 2002-06-13 | Barg Timothy A. | Systems and methods for visualizing and analyzing conditioned data |
| CN101599088A (en) * | 2008-11-18 | 2009-12-09 | 北京美智医疗科技有限公司 | The mining multi-dimensional data system and method for medical information system |
| CN102467559A (en) * | 2010-11-19 | 2012-05-23 | 金蝶软件（中国）有限公司 | Multilevel and multidimensional method and device for analyzing data attributes |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113379551A (en) * | 2021-07-02 | 2021-09-10 | 华青融天(北京)软件股份有限公司 | Transaction data analysis method and device and electronic equipment |
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Effective date of registration: 20210622 Address after: 41401-41406, 14th floor, unit 1, building 4, No. 966, north section of Tianfu Avenue, Chengdu hi tech Zone, China (Sichuan) pilot Free Trade Zone, Chengdu, Sichuan 610093 Applicant after: Chengdu Kelai Network Technology Co.,Ltd. Address before: 610000 Chengdu City, Sichuan Province, China (Sichuan) Free Trade Pilot Zone, North Tianfu Avenue, Chengdu High-tech Zone, 966, 4 buildings, 1 Unit 13 and 14 floors Applicant before: COLASOFT Co.,Ltd. |
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Address after: 610000 12th, 13th and 14th floors, unit 1, building 4, No. 966, north section of Tianfu Avenue, Chengdu hi tech Zone, China (Sichuan) pilot Free Trade Zone, Chengdu, Sichuan Applicant after: Kelai Network Technology Co.,Ltd. Address before: 41401-41406, 14th floor, unit 1, building 4, No. 966, north section of Tianfu Avenue, Chengdu hi tech Zone, China (Sichuan) pilot Free Trade Zone, Chengdu, Sichuan 610093 Applicant before: Chengdu Kelai Network Technology Co.,Ltd. |
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Application publication date: 20190503 |