CN108629034A - A kind of big data public trust analysis method - Google Patents
A kind of big data public trust analysis method Download PDFInfo
- Publication number
- CN108629034A CN108629034A CN201810442991.7A CN201810442991A CN108629034A CN 108629034 A CN108629034 A CN 108629034A CN 201810442991 A CN201810442991 A CN 201810442991A CN 108629034 A CN108629034 A CN 108629034A
- Authority
- CN
- China
- Prior art keywords
- data
- public
- grade
- data source
- letter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention proposes a kind of big data public trust analysis method, including：Data source to collecting data is analyzed, and multiple grades are divided according to the public credibility of the body release of data source and public approval degree of belief, including：Authoritative grade, government's grade and generally acknowledged grade；It is automatic to carry out public letter and score and add public letter level identification to every data of data source according to its grade according to target domain or the rank of target dimension data source public trust；According to the public letter appraisal result and public affairs letter level identification to data source, the dimension or the public trust integrated value of the FIELD Data are calculated.The present invention carries out grade assessment to data source, is assessed the public trust of the FIELD Data by data model.
Description
Technical field
The present invention relates to big data analysis technical field, more particularly to a kind of big data public trust analysis method.
Background technology
Big data (big data), refer to can not be captured with conventional software tool within certain time, manage and
The data acquisition system of processing is to need new tupe that could have stronger decision edge, see clearly discovery power and process optimization ability
Magnanimity, high growth rate and diversified information assets
The public trust of big data is that data are credible, available basis, is the whether believable foundation stone of big data analysis result.Greatly
Data can encounter situations such as third party emits data source without notarial data source, false puppet in data acquisition, be served by process
In encounter service object the situations such as query generated to the authenticities of data, source.
Invention content
The purpose of the present invention aims to solve at least one of described technological deficiency.
For this purpose, it is an object of the invention to propose a kind of big data public trust analysis method.
To achieve the goals above, the embodiment of the present invention provides a kind of big data public trust analysis method, including as follows
Step：
Step S1, the data source to collecting data are analyzed, according to the public credibility of the body release of data source and greatly
Crowd approves that degree of belief divides multiple grades, including：Authoritative grade, government's grade and generally acknowledged grade；
Step S2, according to target domain or the rank of target dimension data source public trust, to every data root of data source
It is automatic to carry out public letter scoring and add public letter level identification according to its grade in step sl；
Step S3 calculates the dimension or the field number according to the public letter appraisal result and public affairs letter level identification to data source
According to public trust integrated value, wherein grade score value q2* governments of public trust value Q=authority grade score value q1* authority grade weight w 1+ governments
Grade weight w 2+ generally acknowledges that grade score value q3* generally acknowledges grade weight w 3.
Further, in the step S1, the public credibility of the body release of the data source and public approval degree of belief point
For three grades：
Authoritative grade：The publicity information of authoritative institution or tissue publication；
Government's grade：The publicity information of governments at all levels' publication；
Generally acknowledge grade：Generally acknowledge the publicities information such as degree high organization, business entity.
Further, in the step S2, phase is distributed to the data source according to the grade of the body release of data source height
The public letter scoring answered.
Further, in the step S2, by arranged below, identification and the mark of data public affairs letter rank are automatically performed：
The addresses URL in input data source, setting domain name identifies, content of pages identifies, the identification of PV values；It is arranged in domain name identification
Appearance, PV values rank, content of pages identification content, data source public affairs letter score value and identification field values fill content.
Further, in the step S3,
Data volume/data count the * 100 in authoritative grade score value q1=authority's level data source；
Data volume/data count the * 100 in government's grade score value q2=government's level data source；
Generally acknowledge that grade score value q3=generally acknowledges data volume/data count * 100 of data source.
Big data public trust analysis method according to the ... of the embodiment of the present invention is borrowed during big data acquisition, application service
Official's certification, the modes such as authority's approval is helped to solve problems.In data production practices application, the data of official's certification are chosen
Source has the data source of authoritative mechanism, and carries out grade assessment to data source, by data model to the public affairs of the FIELD Data
Reliability is assessed.
The additional aspect of the present invention and advantage will be set forth in part in the description, and will partly become from the following description
Obviously, or practice through the invention is recognized.
Description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention will become in the description from combination following accompanying drawings to embodiment
Obviously and it is readily appreciated that, wherein：
Fig. 1 is the flow chart according to the big data public trust analysis method of the embodiment of the present invention.
Specific implementation mode
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, it is intended to for explaining the present invention, and is not considered as limiting the invention.
The present invention provides a kind of big data public trust analysis method, realize to big data public trust, i.e. gathered data source
The analysis of confidence level.
As shown in Figure 1, the big data public trust analysis method of the embodiment of the present invention, includes the following steps：
Step S1, the data source to collecting data are analyzed, according to the public credibility of the body release of data source and greatly
Crowd approves that degree of belief divides multiple grades, including：Authoritative grade, government's grade and generally acknowledged grade.
Specifically, the public credibility of the body release of above-mentioned data source and public approving degree of belief, it is known that mechanism be to prestore
In the database, unknown to pass through keyword such as Supreme Judicial Court, general bureau etc., the automatic judgement of the index comprehensives intelligence such as visit capacity.
In this step, the public credibility of the body release of data source and public approval degree of belief are divided into three grades：
1) authoritative grade：The publicity information of authoritative institution or tissue publication, such as Supreme People's Court；
2) government's grade：The publicity information of governments at all levels' publication, such as people's courts at different levels；
3) generally acknowledge grade：Generally acknowledge the publicities information, such as Shanghai Exchange such as degree high organization, business entity.
Step S2, according to target domain or the rank of target dimension data source public trust, to every data root of data source
It is automatic to carry out public letter scoring and add public letter level identification according to its grade in step sl.
In this step, corresponding public letter is distributed to the data source according to the grade of the body release of data source height to comment
Point.The ID authentication of data public affairs letter rank is realized by automatic scoring, remarks in data production process.
According to target domain or the rank of target dimension data source public trust, each data of the data source is carried out automatically
Public affairs letter scoring, score value is higher, and public trust is higher：
The data 3 in authoritative level data source are divided, and identification field gxlevel is 3；
The data 2 in government's level data source are divided, and identification field gxlevel is 2；
Generally acknowledge that the data 1 in level data source are divided, identification field gxlevel is 1.
Then, in intelligent acquisition system, it is arranged by the following steps, system is automatically performed the identification of data public affairs letter rank
With mark：
1) input data origin url address, setting domain name identifies, content of pages identifies, the identification of PV values；
2) domain name identification setting is divided into：GOV, COM, ORG, AC, EDU, NET etc.；
3) PV values (the page browsing person-time number of the websites page view) rank：1w or less, w grades, 10w grades, 100w grades；
4) content of pages identifies：Highest, general bureau, government, mechanism, group, company etc.；
5) data source public affairs letter score value setting：1 point, 2 points, 3 points；
6) identification field values are filled with：1,2,3；
By automatically processing for system, the data for having public letter scoring can be obtained, are target domain or target dimension data
The basis of whole public credibility scoring, is risk model or the believable support of risk report.
Step S3 calculates the dimension or the field number according to the public letter appraisal result and public affairs letter level identification to data source
According to public trust integrated value.Wherein,
Grade score value q2* governments of public trust value Q=authority grade score value q1* authority grade weight w 1+ governments grade weight w 2+ generally acknowledges grade
Score value q3* generally acknowledges grade weight w 3 (1)
Specifically, calculation is as follows：
Data volume/data count the * 100 in authoritative grade score value q1=authority's level data source；
Data volume/data count the * 100 in government's grade score value q2=government's level data source；
Generally acknowledge that grade score value q3=generally acknowledges data volume/data count * 100 of data source.
By taking table 1 as an example, the evaluation of big data public trust is scored using mathematical model, objectively responds the field or the dimension data
Whole public letter situation.
Table 1
Big data public trust analysis method according to the ... of the embodiment of the present invention is borrowed during big data acquisition, application service
Official's certification, the modes such as authority's approval is helped to solve problems.In data production practices application, the data of official's certification are chosen
Source has the data source of authoritative mechanism, and carries out grade assessment to data source, by data model to the public affairs of the FIELD Data
Reliability is assessed.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
Centainly refer to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be any
One or more embodiments or example in can be combined in any suitable manner.
Although the embodiments of the present invention has been shown and described above, it is to be understood that above-described embodiment is example
Property, it is not considered as limiting the invention, those skilled in the art are not departing from the principle of the present invention and objective
In the case of can make changes, modifications, alterations, and variations to the above described embodiments within the scope of the invention.The scope of the present invention
By appended claims and its equivalent limit.
Claims (5)
1. a kind of big data public trust analysis method, which is characterized in that include the following steps：
Step S1, the data source to collecting data are analyzed, are recognized according to the public credibility of the body release of data source and masses
Trust degree divides multiple grades, including：Authoritative grade, government's grade and generally acknowledged grade；
Step S2, according to target domain or the rank of target dimension data source public trust, to every data of data source according to it
Grade in step sl, it is automatic to carry out public letter scoring and add public letter level identification；
Step S3, according to data source public letter appraisal result and public letter level identification, calculate the dimension or the FIELD Data
Public trust integrated value, wherein grade score value q2* governments of public trust value Q=authority grade score value q1* authority grade weight w 1+ governments grade power
Value w2+ generally acknowledges that grade score value q3* generally acknowledges grade weight w 3.
2. big data public trust analysis method as described in claim 1, which is characterized in that in the step S1, the number
It is divided into three grades according to the public credibility and public approval degree of belief of the body release in source：
Authoritative grade：The publicity information of authoritative institution or tissue publication；
Government's grade：The publicity information of governments at all levels' publication；
Generally acknowledge grade：Generally acknowledge the publicities information such as degree high organization, business entity.
3. big data public trust analysis method as described in claim 1, which is characterized in that in the step S2, according to number
According to the grade height of the body release in source corresponding public letter scoring is distributed to the data source.
4. big data public trust analysis method as described in claim 1, which is characterized in that in the step S2, by with
Lower setting is automatically performed identification and the mark of data public affairs letter rank：
The addresses URL in input data source, setting domain name identifies, content of pages identifies, the identification of PV values；Setting domain name identification content,
PV values rank, content of pages identification content, data source public affairs letter score value and identification field values fill content.
5. big data public trust analysis method as described in claim 1, which is characterized in that in the step S3,
Data volume/data count the * 100 in authoritative grade score value q1=authority's level data source；
Data volume/data count the * 100 in government's grade score value q2=government's level data source；
Generally acknowledge that grade score value q3=generally acknowledges data volume/data count * 100 of data source.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810442991.7A CN108629034A (en) | 2018-05-10 | 2018-05-10 | A kind of big data public trust analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810442991.7A CN108629034A (en) | 2018-05-10 | 2018-05-10 | A kind of big data public trust analysis method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108629034A true CN108629034A (en) | 2018-10-09 |
Family
ID=63692595
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810442991.7A Pending CN108629034A (en) | 2018-05-10 | 2018-05-10 | A kind of big data public trust analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108629034A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110675648A (en) * | 2019-08-20 | 2020-01-10 | 中国平安财产保险股份有限公司 | Method, system and server for data source acquisition and data deduplication acquisition of parking lot |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105260849A (en) * | 2015-10-21 | 2016-01-20 | 内蒙古科技大学 | Scientific researcher evaluation method across social networks |
| US20160070732A1 (en) * | 2014-09-05 | 2016-03-10 | Gravity Ltd. | Systems and methods for analyzing and deriving meaning from large scale data sets |
| CN106485585A (en) * | 2016-09-29 | 2017-03-08 | 上海陆家嘴国际金融资产交易市场股份有限公司 | Method and system for ranking |
| CN106709792A (en) * | 2017-01-22 | 2017-05-24 | 博元森禾信息科技（北京）有限公司 | Evaluation method and evaluation device for online drug transaction credibility |
-
2018
- 2018-05-10 CN CN201810442991.7A patent/CN108629034A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160070732A1 (en) * | 2014-09-05 | 2016-03-10 | Gravity Ltd. | Systems and methods for analyzing and deriving meaning from large scale data sets |
| CN105260849A (en) * | 2015-10-21 | 2016-01-20 | 内蒙古科技大学 | Scientific researcher evaluation method across social networks |
| CN106485585A (en) * | 2016-09-29 | 2017-03-08 | 上海陆家嘴国际金融资产交易市场股份有限公司 | Method and system for ranking |
| CN106709792A (en) * | 2017-01-22 | 2017-05-24 | 博元森禾信息科技（北京）有限公司 | Evaluation method and evaluation device for online drug transaction credibility |
Non-Patent Citations (4)
| Title |
|---|
| 倪自银: "食品企业公信度评价体系构建与模型", 《中国流通经济》 * |
| 元界研究院: ""金融机构外部风险数据的标准评价体系"", 《HTTPS://WWW.WEIYANGX.COM/284189.HTML》 * |
| 周宇博: ""数据新闻来源的信度评估"", 《中国广播电视学刊》 * |
| 张燕华 等: ""论大学排名体系的公信度问题"", 《教育基本理论》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110675648A (en) * | 2019-08-20 | 2020-01-10 | 中国平安财产保险股份有限公司 | Method, system and server for data source acquisition and data deduplication acquisition of parking lot |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Subramanian et al. | Increases in COVID-19 are unrelated to levels of vaccination across 68 countries and 2947 counties in the United States | |
| Lewallen et al. | Cataract surgical coverage remains lower in women | |
| Shi et al. | Cross‐reference system for translating between genetic soil classification of China and soil taxonomy | |
| Skovrlj et al. | Neurosurgery residency websites: a critical evaluation | |
| Guerra et al. | Mapping the global extent of malaria in 2005 | |
| Wylie et al. | Identification of networks of sexually transmitted infection: a molecular, geographic, and social network analysis | |
| Ackland | Mapping the US political blogosphere: Are conservative bloggers more prominent? | |
| CN104699715B (en) | Main index of patients plateform system construction method | |
| Vaughan et al. | Equal representation by search engines? A comparison of websites across countries and domains | |
| He et al. | Using the Baidu search index to predict the incidence of HIV/AIDS in China | |
| Griffiths et al. | The sustainable suburban high street: a review of themes and approaches | |
| Mohatt et al. | A menu of options: Resources for preventing veteran suicide in rural communities. | |
| CN105844093A (en) | Social data based actuarial system and method | |
| Li et al. | What is the UK public searching for? A correlation analysis of *** trends search terms and cosmetic surgery in the UK | |
| CN108629034A (en) | A kind of big data public trust analysis method | |
| Rahmatillah et al. | The Reflection of Racism Towards African American in Selma Movie | |
| Gao | An in‐depth examination of China's advertising regulation system | |
| CN108335053A (en) | A kind of application study type military research Task application evaluation method | |
| Rakotonanahary et al. | Qualitative evaluation of HIV/AIDS IEC activities in Madagascar | |
| US20060190718A1 (en) | Method and system of providing political campaign material | |
| Ahmad et al. | Using levels of evidence to compare clinical impact from research | |
| Ovalle-Perandones et al. | Hubs and authorities in a spanish co-authorship network | |
| CN105991780A (en) | Internet IP address positioning data-based IP address positioning system and method | |
| CN105373535B (en) | A kind of data extraction method of water quality benchmark | |
| Burhansstipanov et al. | Online evaluation programs: Benefits and limitations |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181009 |