CN111191125A - Data analysis method based on tagging - Google Patents
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Abstract
The invention provides a data analysis method based on tagging in the field of data mining analysis, which comprises the following steps: step S10, acquiring the original data of each entity from the database; step S20, defining a label system, a calculation rule and a logic rule; step S30, labeling each piece of original data based on the label system, the calculation rule and the logic rule to generate label data; step S40, clustering the label data; and step S50, performing self-service multidimensional analysis and display on the clustered label data or the label data of a single entity. The invention has the advantages that: the application range of data analysis is greatly improved, the method is suitable for various industries, self-service multidimensional analysis can be performed on the data, and the application value of the data is obviously improved.
Description
Technical Field
The invention relates to the field of data mining analysis, in particular to a data analysis method based on tagging.
Background
In the field of novel smart cities and smart government affairs, the challenges of the intelligent application of the novel smart cities or the smart government affairs are faced, data resources need to be concentrated, big data are important strategic resources, data drive becomes the core characteristics of the novel smart cities and the smart government affairs, and therefore the requirements of data analysis are generated, and the same requirements exist in other fields.
To realize intelligent application, it is important to have a fine management and data enabling, and the concept of image analysis is also developed accordingly. The portrait analysis refers to a process of cleaning, clustering and analyzing mass data information in a big data era, abstracting data into labels, and materializing business entity images by using the labels, so that the portrait analysis can help users to better develop targeted services and supervision.
Although some platforms or systems in the market currently describe target objects by tags, such as user portrait analysis, advertisement marketing, enterprise portrait analysis, and the like, these platforms or systems have the disadvantages that they can only analyze a single business object and display the business object according to a specific template, the application range is narrow, and cross-application and self-service multidimensional analysis cannot be realized.
Therefore, how to provide a data analysis method based on tagging to achieve the purpose of improving the application range of data analysis, which is applicable to various industries, and which can perform self-service multidimensional analysis on data to further improve the application value of data becomes a technical problem to be solved urgently.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a data analysis method based on tagging, which is suitable for various industries, can improve the application range of data analysis, and can perform self-service multidimensional analysis on data, thereby improving the application value of the data.
The invention is realized by the following steps: a data analysis method based on labeling comprises the following steps:
step S10, acquiring the original data of each entity from the database;
step S20, defining a label system, a calculation rule and a logic rule;
step S30, labeling each piece of original data based on the label system, the calculation rule and the logic rule to generate label data;
step S40, clustering the label data;
and step S50, performing self-service multidimensional analysis and display on the clustered label data or the label data of a single entity.
Further, the step S10 is specifically:
and acquiring the original data of each entity to be analyzed from each database, analyzing and adapting the original data, and converting the original data into data with a uniform format.
Further, the step S20 is specifically:
defining a label system comprising at least one label according to business requirements; each label comprises a label attribute and a label generation rule; setting a label of a previous level, namely a father node, based on the label attribute for each label, and further establishing a label system of a tree structure;
and defining a calculation rule comprising a calculation expression and a logic rule comprising a logic expression, wherein the calculation rule is used for calculating the original data according to the label generation rule.
Further, the label attribute at least comprises a name, a service classification, a storage type, a longitude and latitude and a value range; the service classification at least comprises a fact type, a rule type, a statistic type and a complex type; the storage types comprise numerical values, characters, geographic positions and enumerations; the label generation rule is a rule that each piece of original data corresponds to the label attribute one by one.
Further, the step S30 specifically includes:
step S31, calculating each piece of original data respectively by using a calculation engine based on the calculation rule to generate a first calculation result, and labeling each piece of original data based on the first calculation result and a label generation rule to generate first label data;
respectively calculating each piece of original data by using a rule engine based on the logic rule to generate a second calculation result, and labeling each piece of original data based on the second calculation result and a label generation rule to generate second label data;
step S32, judging whether all the original data are labeled, if so, entering step S40; if not, go to step S33;
and S33, labeling each piece of unlabeled original data based on the neural network model and the label generation rule to generate third label data, and proceeding to S40.
Further, the tagging specifically sets a tag attribute of each piece of original data.
Further, the step S40 is specifically:
and based on a clustering algorithm, clustering the label data by using the label attribute.
Further, the step S50 is specifically:
and searching the grouped label data or the label data of a single entity to be analyzed by utilizing an index technology, setting an analysis dimension according to the label system, counting the searched label data based on a visualization tool and the analysis dimension, and displaying the label data in a chart form.
The invention has the advantages that:
the original data are labeled by self-defining the label system, the calculation rules and the logic rules, so that different types of entities and different rules which are dynamically and flexibly set in different application scenes can be labeled, the application range of data analysis is greatly expanded compared with the traditional method that only a single business object can be analyzed, and the method is suitable for entity data imaging, user behavior analysis, accurate marketing and personalized recommendation in different industries; the calculation and labeling are performed by utilizing a calculation engine, a rule engine and a neural network model, so that the labeling capability is greatly improved; by setting the analysis dimensionality and utilizing an index technology and a visualization tool to realize self-service multidimensional analysis on the data, multi-angle fusion analysis can be performed on the service focus, and the application value of the data is remarkably improved.
Drawings
The invention will be further described with reference to the following examples with reference to the accompanying drawings.
FIG. 1 is a flow chart of a data analysis method based on tagging according to the present invention.
FIG. 2 is a schematic diagram of the present invention for obtaining raw data from a database.
FIG. 3 is a second schematic diagram of the present invention for obtaining raw data from a database.
FIG. 4 is a schematic diagram of a compute engine of the present invention.
FIG. 5 is a second schematic diagram of a compute engine according to the present invention.
FIG. 6 is a schematic diagram of a rules engine of the present invention.
FIG. 7 is a second schematic diagram of a rules engine of the present invention.
FIG. 8 is a third schematic diagram of a rules engine of the present invention.
FIG. 9 is a schematic diagram of the geo-clustering configuration of the present invention.
FIG. 10 is a graphical representation of the geo-clustering results of the present invention.
FIG. 11 is a schematic diagram of a conditional clustering arrangement of the present invention.
FIG. 12 is a schematic diagram of a setup interface for self-service multidimensional analysis and display in accordance with the present invention.
FIG. 13 is a schematic diagram of a display interface for self-service multidimensional analysis and display according to the present invention.
Detailed Description
The technical scheme in the embodiment of the application has the following general idea: original data are labeled through a custom label system, a calculation rule and a logic rule, and the application range of data analysis is expanded; self-service multidimensional analysis is carried out on the data by self-defining analysis dimensionality and utilizing an index technology and a visualization tool.
Referring to fig. 1 to 13, a preferred embodiment of a data analysis method based on tagging according to the present invention includes the following steps:
step S10, acquiring the original data of each entity from the database; the entity is an object to be subjected to data analysis, an object to be described in portrait or a theme of portrait, for example, the entity or the theme of the portrait of the population is a natural person; the entities are unified on the conceptual object, but one entity contains various data information of the entity life cycle from the data perspective;
step S20, defining a label system, a calculation rule and a logic rule;
step S30, labeling each piece of original data based on the label system, the calculation rule and the logic rule to generate label data;
step S40, clustering the label data; grouping is to divide all label data into smaller groups with common characteristics through rules, so as to help people to better analyze certain characteristic groups, obtain object information of service focus points more quickly, and perform accurate decision and targeted operation;
and step S50, performing self-service multidimensional analysis and display on the clustered label data or the label data of a single entity.
The label is a highly refined feature identifier obtained by analyzing the information of an Entity, is a description of the attribute feature of the Entity (Entity), represents an abstract expression of a specific objective fact of the described Entity, and is a data modeling method starting from a business view; the value of the tag marks a piece of characteristic information of the entity, which may be a numerical value or an enumerated value.
The label system is used for solving the association problem among data, and often involves complex structural design of a plurality of dimensions and a large number of labels, and the label system is generally flat.
The step S10 specifically includes:
and acquiring the original data of each entity to be analyzed from each database, analyzing and adapting the original data, and converting the original data into data with a uniform format.
Specifically, the database connection is constructed by using the connection attributes of each database and the jdbc package, wherein the connection attributes comprise an ip address, a user name, a password and the like.
After the connection of the database, the metadata of the database is inquired through a database jdbc packet or a database operation api and a name of a specified target data instance or writing an sql statement, and the inquired metadata information is subjected to field analysis and stored in a uniform format, so that the metadata of the related database can be obtained.
After the metadata is obtained through analysis, according to the required fields selected by the user, sql statements, such as select f1, f2, f3 from table1, are spliced, wherein f1, f2, f3 are the metadata fields selected by the user, and finally, the jdbc package is utilized to obtain the final original data through query by executing the sql statements.
The step S20 specifically includes:
defining a label system comprising at least one label according to business requirements; each label comprises a label attribute and a label generation rule; setting a label of a previous level, namely a father node, based on the label attribute for each label, and further establishing a label system of a tree structure, namely a hierarchical label system;
and defining a calculation rule comprising a calculation expression and a logic rule comprising a logic expression, wherein the calculation rule is used for calculating the original data according to the label generation rule.
The label attribute at least comprises a name, a service classification, a storage type, longitude and latitude and a value range; the service classification at least comprises a fact type, a rule type, a statistic type and a complex type; the storage types comprise numerical values, characters, geographic positions and enumerations; the label generation rule is a rule that each piece of original data corresponds to the label attribute one by one.
The step S30 specifically includes:
step S31, calculating each piece of original data respectively by using a calculation engine based on the calculation rule to generate a first calculation result, and labeling each piece of original data based on the first calculation result and a label generation rule to generate first label data; the calculation engine supports the label generation of statistical type original data; the calculation engine is preferably SPARK;
respectively calculating each piece of original data by using a rule engine based on the logic rule to generate a second calculation result, and labeling each piece of original data based on the second calculation result and a label generation rule to generate second label data; the rule engine supports the label generation of the original data of the fact type and the rule type; the rule engine is developed by the inference engine, is a component embedded in an application program, realizes the separation of a business decision from an application program code, and writes the business decision by using a predefined semantic module; receiving data input, explaining a business rule, and making a business decision according to the business rule;
step S32, judging whether all the original data are labeled, if so, entering step S40; if not, go to step S33;
s33, labeling each piece of unlabeled original data based on the neural network model and the label generation rule to generate third label data, and entering the step S40; the neural network model supports label generation of complex raw data.
Conventionally, data tagging is actually needed to edit codes, namely, the corresponding relation between the data and the tags is set through editing the codes, and business personnel do not know how to edit the codes to combine the data and the tags.
The labeling is specifically to set the label attribute of each piece of original data.
The step S40 specifically includes:
based on a clustering algorithm, clustering each label data by using the label attribute, for example, clustering each label data based on the longitude and latitude; clustering can accelerate exposure and early warning of population characteristics.
The step S50 specifically includes:
searching the grouped label data or the label data of a single entity to be analyzed by utilizing an index technology, setting an analysis dimension according to the label system, counting the searched label data based on a visualization tool and the analysis dimension, and displaying the label data in a chart form; for example in the form of a line graph, bar graph, pie graph, radar chart, bubble chart, word cloud, or table; the analysis dimensionality can be set according to the label attributes, for example, label data with longitude and latitude values within the Beijing range and the value range of 1 to 10 is analyzed.
The label data are displayed in a chart by utilizing an index technology and a visualization tool, namely, the label data can be displayed in a pull-down menu or a simple form filling mode, the work of editing the codes is visualized and simplified, the work of displaying the chart can be automatically completed by simply explaining the service staff, programming knowledge is not needed, the service staff can develop services, and great convenience is provided for leadership report work.
In summary, the invention has the advantages that:
the original data are labeled by self-defining the label system, the calculation rules and the logic rules, so that different types of entities and different rules which are dynamically and flexibly set in different application scenes can be labeled, the application range of data analysis is greatly expanded compared with the traditional method that only a single business object can be analyzed, and the method is suitable for entity data imaging, user behavior analysis, accurate marketing and personalized recommendation in different industries; the calculation and labeling are performed by utilizing a calculation engine, a rule engine and a neural network model, so that the labeling capability is greatly improved; by setting the analysis dimensionality and utilizing an index technology and a visualization tool to realize self-service multidimensional analysis on the data, multi-angle fusion analysis can be performed on the service focus, and the application value of the data is remarkably improved.
Although specific embodiments of the invention have been described above, it will be understood by those skilled in the art that the specific embodiments described are illustrative only and are not limiting upon the scope of the invention, and that equivalent modifications and variations can be made by those skilled in the art without departing from the spirit of the invention, which is to be limited only by the appended claims.
Claims (8)
1. A data analysis method based on labeling is characterized in that: the method comprises the following steps:
step S10, acquiring the original data of each entity from the database;
step S20, defining a label system, a calculation rule and a logic rule;
step S30, labeling each piece of original data based on the label system, the calculation rule and the logic rule to generate label data;
step S40, clustering the label data;
and step S50, performing self-service multidimensional analysis and display on the clustered label data or the label data of a single entity.
2. The tagging-based data analysis method of claim 1, wherein: the step S10 specifically includes:
and acquiring the original data of each entity to be analyzed from each database, analyzing and adapting the original data, and converting the original data into data with a uniform format.
3. The tagging-based data analysis method of claim 1, wherein: the step S20 specifically includes:
defining a label system comprising at least one label according to business requirements; each label comprises a label attribute and a label generation rule; setting a label of a previous level, namely a father node, based on the label attribute for each label, and further establishing a label system of a tree structure;
and defining a calculation rule comprising a calculation expression and a logic rule comprising a logic expression, wherein the calculation rule is used for calculating the original data according to the label generation rule.
4. A tagging-based data analysis method according to claim 3, wherein: the label attribute at least comprises a name, a service classification, a storage type, longitude and latitude and a value range; the service classification at least comprises a fact type, a rule type, a statistic type and a complex type; the storage types comprise numerical values, characters, geographic positions and enumerations; the label generation rule is a rule that each piece of original data corresponds to the label attribute one by one.
5. A tagging-based data analysis method according to claim 3, wherein: the step S30 specifically includes:
step S31, calculating each piece of original data respectively by using a calculation engine based on the calculation rule to generate a first calculation result, and labeling each piece of original data based on the first calculation result and a label generation rule to generate first label data;
respectively calculating each piece of original data by using a rule engine based on the logic rule to generate a second calculation result, and labeling each piece of original data based on the second calculation result and a label generation rule to generate second label data;
step S32, judging whether all the original data are labeled, if so, entering step S40; if not, go to step S33;
and S33, labeling each piece of unlabeled original data based on the neural network model and the label generation rule to generate third label data, and proceeding to S40.
6. The tagging-based data analysis method of claim 5, wherein: the labeling is specifically to set the label attribute of each piece of original data.
7. A tagging-based data analysis method according to claim 3, wherein: the step S40 specifically includes:
and based on a clustering algorithm, clustering the label data by using the label attribute.
8. The tagging-based data analysis method of claim 1, wherein: the step S50 specifically includes:
and searching the grouped label data or the label data of a single entity to be analyzed by utilizing an index technology, setting an analysis dimension according to the label system, counting the searched label data based on a visualization tool and the analysis dimension, and displaying the label data in a chart form.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111752997A (en) * | 2020-07-01 | 2020-10-09 | 浪潮云信息技术股份公司 | Basic library data label analysis system and method |
| CN111782736A (en) * | 2020-07-20 | 2020-10-16 | 浪潮卓数大数据产业发展有限公司 | Data classification management method and system |
| CN112131475A (en) * | 2020-09-25 | 2020-12-25 | 重庆邮电大学 | Interpretable and interactive user portrait method and device |
| CN112434096A (en) * | 2020-11-30 | 2021-03-02 | 上海天旦网络科技发展有限公司 | Service analysis system and method based on intelligent label |
| CN113779343A (en) * | 2021-09-18 | 2021-12-10 | 北京锐安科技有限公司 | Mass data processing method, device, medium and electronic equipment |
| CN113821703A (en) * | 2020-06-18 | 2021-12-21 | 广州汽车集团股份有限公司 | Internet of vehicles user portrait generation method and system |
| CN114238699A (en) * | 2021-12-16 | 2022-03-25 | 浪潮卓数大数据产业发展有限公司 | Data processing method based on label rule |
| CN115983270A (en) * | 2022-12-02 | 2023-04-18 | 重庆邮电大学 | Intelligent extraction method for E-commerce commodity attributes |
| CN116244369A (en) * | 2023-05-12 | 2023-06-09 | 山东云力信息科技有限公司 | Data mining method and system based on big data visualization |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105183917A (en) * | 2015-10-15 | 2015-12-23 | 国家电网公司 | Multi-dimensional analysis method for multi-level storage data |
| US20170330220A1 (en) * | 2016-05-13 | 2017-11-16 | Zeta Global Corp. | Adaptive lead generation for marketing |
| CN108416620A (en) * | 2018-02-08 | 2018-08-17 | 杭州浮云网络科技有限公司 | A kind of intelligent social advertisement launching platform of the representation data based on big data |
| CN109447126A (en) * | 2018-09-27 | 2019-03-08 | 长威信息科技发展股份有限公司 | A kind of method and apparatus of entity and entity attribute dynamic aggregation construction personage's portrait |
| CN109522333A (en) * | 2018-11-23 | 2019-03-26 | 北京锐安科技有限公司 | Data analysing method, device, equipment and medium |
| CN109635029A (en) * | 2018-12-07 | 2019-04-16 | 深圳前海微众银行股份有限公司 | Data processing method, device, equipment and medium based on label index system |
| CN110263021A (en) * | 2019-06-25 | 2019-09-20 | 浪潮卓数大数据产业发展有限公司 | A kind of theme library generating method based on personalized labels system |
-
2019
- 2019-12-24 CN CN201911344104.3A patent/CN111191125A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105183917A (en) * | 2015-10-15 | 2015-12-23 | 国家电网公司 | Multi-dimensional analysis method for multi-level storage data |
| US20170330220A1 (en) * | 2016-05-13 | 2017-11-16 | Zeta Global Corp. | Adaptive lead generation for marketing |
| CN108416620A (en) * | 2018-02-08 | 2018-08-17 | 杭州浮云网络科技有限公司 | A kind of intelligent social advertisement launching platform of the representation data based on big data |
| CN109447126A (en) * | 2018-09-27 | 2019-03-08 | 长威信息科技发展股份有限公司 | A kind of method and apparatus of entity and entity attribute dynamic aggregation construction personage's portrait |
| CN109522333A (en) * | 2018-11-23 | 2019-03-26 | 北京锐安科技有限公司 | Data analysing method, device, equipment and medium |
| CN109635029A (en) * | 2018-12-07 | 2019-04-16 | 深圳前海微众银行股份有限公司 | Data processing method, device, equipment and medium based on label index system |
| CN110263021A (en) * | 2019-06-25 | 2019-09-20 | 浪潮卓数大数据产业发展有限公司 | A kind of theme library generating method based on personalized labels system |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113821703A (en) * | 2020-06-18 | 2021-12-21 | 广州汽车集团股份有限公司 | Internet of vehicles user portrait generation method and system |
| CN113821703B (en) * | 2020-06-18 | 2023-12-08 | 广州汽车集团股份有限公司 | Internet of vehicles user portrait generation method and system thereof |
| CN111752997A (en) * | 2020-07-01 | 2020-10-09 | 浪潮云信息技术股份公司 | Basic library data label analysis system and method |
| CN111782736A (en) * | 2020-07-20 | 2020-10-16 | 浪潮卓数大数据产业发展有限公司 | Data classification management method and system |
| CN111782736B (en) * | 2020-07-20 | 2022-07-26 | 浪潮卓数大数据产业发展有限公司 | Data classification management method and system |
| CN112131475A (en) * | 2020-09-25 | 2020-12-25 | 重庆邮电大学 | Interpretable and interactive user portrait method and device |
| CN112131475B (en) * | 2020-09-25 | 2023-10-10 | 重庆邮电大学 | Interpretable and interactive user portrayal method and device |
| CN112434096B (en) * | 2020-11-30 | 2023-05-23 | 上海天旦网络科技发展有限公司 | Intelligent tag-based service analysis system and method |
| CN112434096A (en) * | 2020-11-30 | 2021-03-02 | 上海天旦网络科技发展有限公司 | Service analysis system and method based on intelligent label |
| CN113779343A (en) * | 2021-09-18 | 2021-12-10 | 北京锐安科技有限公司 | Mass data processing method, device, medium and electronic equipment |
| CN114238699A (en) * | 2021-12-16 | 2022-03-25 | 浪潮卓数大数据产业发展有限公司 | Data processing method based on label rule |
| CN115983270A (en) * | 2022-12-02 | 2023-04-18 | 重庆邮电大学 | Intelligent extraction method for E-commerce commodity attributes |
| CN115983270B (en) * | 2022-12-02 | 2024-05-03 | 芽米科技(广州)有限公司 | Intelligent extraction method for e-commerce commodity attribute |
| CN116244369A (en) * | 2023-05-12 | 2023-06-09 | 山东云力信息科技有限公司 | Data mining method and system based on big data visualization |
| CN116303832A (en) * | 2023-05-17 | 2023-06-23 | 鹏城实验室 | Method and related device for gathering multi-source data in evaluable manner |
| CN116303832B (en) * | 2023-05-17 | 2024-04-09 | 鹏城实验室 | Method and related device for gathering multi-source data in evaluable manner |
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Application publication date: 20200522 |