CN116166708B - An operation platform management system based on big data analytics - Google Patents
An operation platform management system based on big data analyticsInfo
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- CN116166708B CN116166708B CN202211099743.XA CN202211099743A CN116166708B CN 116166708 B CN116166708 B CN 116166708B CN 202211099743 A CN202211099743 A CN 202211099743A CN 116166708 B CN116166708 B CN 116166708B
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Abstract
本发明公开了一种基于大数据分析的运营平台管理系统，包括数据集成模块、数据挖掘模块，所述数据集成模块用于对异构数据进行专项化集成，所述数据挖掘模块用于实现数据并行挖掘，所述数据集成模块包括异构数据筛选模块、局部特征提取模块、全局特征库创建模块、特征映射模块，所述异构数据筛选模块用于对数据集成过程中的异构数据进行筛选，所述局部特征提取模块用于对待处理的异构数据进行局部特征提取，所述全局特征库创建模块用于将提取出的局部特征映射为全局特征并保存到库中，所述特征映射模块用于创建局部特征到全局特征的映射规则，本发明，具有集成精度高和挖掘效率高的特点。This invention discloses an operation platform management system based on big data analysis, including a data integration module and a data mining module. The data integration module is used for specialized integration of heterogeneous data, and the data mining module is used for parallel data mining. The data integration module includes a heterogeneous data filtering module, a local feature extraction module, a global feature library creation module, and a feature mapping module. The heterogeneous data filtering module is used to filter heterogeneous data during the data integration process. The local feature extraction module is used to extract local features from the heterogeneous data to be processed. The global feature library creation module is used to map the extracted local features to global features and save them to the library. The feature mapping module is used to create mapping rules from local features to global features. This invention features high integration accuracy and high mining efficiency.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an operation platform management system based on big data analysis.
Background
The advent of the big data age has led to the beginning of industries that place importance on the mining of big data. Big data are hidden with huge commercial value, and blowout type development in recent years, relative to the growth speed of data, the analysis capability of people on the big data can not meet the requirements, and the situation that the data stock is large but information is still deficient exists. The traditional data mining method can not process data semantic conflict caused by heterogeneous data, so that the integration precision is reduced, the semantic conflict is continuously expanded along with the increase of the data quantity, the data integration efficiency is seriously influenced, the traditional data mining can be influenced by single machine performance, the mining depth depends on the calculation power of a server, the efficiency in processing mass data is low, and therefore, an operation platform management system based on large data analysis, which is high in integration precision and mining efficiency, is very necessary.
Disclosure of Invention
The invention aims to provide an operation platform management system based on big data analysis so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the technical scheme that the operation platform management system based on big data analysis comprises a data integration module and a data mining module, wherein the data integration module is used for performing specialized integration on heterogeneous data, and the data mining module is used for realizing data parallel mining.
According to the technical scheme, the data integration module comprises a heterogeneous data screening module, a local feature extraction module, a global feature library creation module and a feature mapping module, wherein the heterogeneous data screening module is used for screening heterogeneous data in the data integration process, the local feature extraction module is used for extracting local features of heterogeneous data to be processed, the global feature library creation module is used for mapping the extracted local features into global features and storing the global features into the library, the feature mapping module is used for creating a mapping rule from the local features to the global features, and the heterogeneous data screening module is connected with the local feature extraction module, the global feature library creation module and the feature mapping module in a closed loop mode.
According to the technical scheme, the data mining module comprises a classification training module, an association clustering module, a prediction rule making module, a result evaluation module and a visual operation module, wherein the classification training module is used for performing classification training according to different characteristics of data, the association clustering module is used for forming a data cluster by aggregating the characteristics with association, the prediction rule making module is used for making a prediction rule to conduct characteristic prediction on an unknown data set by analyzing a training data set, the result evaluation module is used for evaluating a data mining result and screening out data with high importance, and the visual operation module is used for achieving visual display operation with a data owner. Different characteristics of data are divided into different classes, the classes can be created according to a training data set, association rules are determined by finding dependency relations among different data objects, and functional characteristics of the data in one class are extracted as characteristic descriptions through association clustering.
According to the technical scheme, the operation method of the data integration module comprises the following steps:
step A, screening a data source to obtain heterogeneous data with heterogeneous characteristics, wherein the data source is used as the heterogeneous data source because a large amount of heterogeneous data exists;
step B, extracting local features of the heterogeneous data;
step C, creating a global feature library according to the local features;
and D, establishing a feature mapping rule, and mapping the local features into a global feature library.
According to the above technical scheme, in the step a, the method for screening heterogeneous data includes:
step S1, performing preliminary screening according to the surface structure difference of the data, wherein the surface structure difference comprises the physical storage difference of the data, the difference of an operating system and the data format difference;
Step S2, further screening, namely tracing the data sources, judging the logic model of the data sources, and screening according to the logic model, wherein the data are stored and maintained in different business logics, even the same data have logic performance isomerism, and the data can be traced back to the original application scene after tracing, so that logic isomerism screening is carried out;
and step S3, classifying according to the screening result, specifically screening the data with the heterogeneous characteristics.
According to the above technical scheme, in the step B, the local feature extraction method of the heterogeneous data includes:
Establishing a feature tree, and traversing all data of the heterogeneous data source in the feature tree;
Arranging the data features appearing in the traversal process into a feature tree according to the data size;
taking the characteristic with the largest data quantity in the characteristic tree as the representative characteristic of one section of data;
The method comprises the steps of performing simulation operation on data with extracted local characteristics, recording the minimum editing times n i of the operation, converting a certain section of data into data with the same characteristics through character replacement, insertion or deletion, and recording the required minimum editing times;
and an acquisition operation step of converting the minimum editing times into a distance value len (n i) according to a distance conversion algorithm. The editing times can not be consulted and calculated in the system, the editing times can be converted into distance values more conveniently to participate in calculation, the distance conversion algorithm can convert the editing times into the distance values according to specific operation steps, the calculation is convenient, the preparation is carried out for subsequent global mapping, specific conversion rules are the steps for defining each operation, and the steps are related to the character change degree generated by the operation and are in a proportional relation.
According to the above technical solution, in the step D, the method for establishing the feature mapping rule includes:
traversing all local features in the feature tree, calculating distance values, and establishing a distance value array { len (n 1)、len(n2)……len(ni) };
Step b, calculating a stored characteristic distance mean value Ave (n) in the characteristic tree, wherein:
wherein i is the number of local features in the feature tree, and i is not less than 1;
The calculated characteristic distance average value Ave (n) is used as a mapping comparison amplitude, the characteristic distance average value Ave (n) reflects the average characteristic of the heterogeneous data cluster, and the upper limit and the lower limit of the characteristic mapping process can be ensured to be controllable as the mapping comparison amplitude;
step d, establishing a variance operation algorithm, traversing all data in the feature tree again on the basis of the determination of the mapping comparison amplitude to generate a result, wherein the result of the variance operation algorithm displays the difference between the feature distance value and the average value of the data, and screening the data after determining a screening rule;
and e, carrying out numerical limitation on a result generated by a contrast algorithm, storing data lower than the mapping contrast amplitude into a global feature library, and keeping the data higher than the mapping contrast amplitude in a feature tree to enter the integration of the next period. The data with the characteristic distance value lower than the mapping contrast amplitude is the data with the characteristic distance value lower than the average value, the characteristic complexity of the data is lower, the probability of misjudgment is lower, the data can be directly screened out and stored in a global characteristic library, the characteristic complexity of the data with the characteristic distance value higher than the mapping contrast amplitude is higher, and the data is continuously remained in the characteristic tree to carry out new round of integrated screening with the data of the next round.
According to the technical scheme, in the data integration module, after data is mapped into the global feature library, the feature distance value with the largest proportion is used as the main feature, and classification screening is carried out, wherein the basis of classification screening is the data feature extracted in the manual formulation and big data integration process.
According to the above technical scheme, the mining module includes a method for making a prediction rule, specifically:
Carrying out association clustering on data with the same characteristic type to generate a data cluster with characteristic values, wherein the characteristic values are characteristic distance average values of all data in a series of data clusters;
and comparing the data cluster with the characteristic value with the characteristic tree generated in the data integration process, screening out the data with the same characteristic value and marking. In the data integration process, the data reserved on the feature tree and the generated data cluster with the feature values have conflicts, the same feature values have conflicts generated among different semantics, the conflicts need to be screened out, and the computational waste caused by redundant operation and semantic conflicts in the prediction process is reduced.
Compared with the prior art, the method has the advantages that the feature mapping module is arranged, in the data integration process, the semantic defect generated by heterogeneous data is difficult to overcome by the traditional data integration method, after local features of the heterogeneous data are extracted, the global feature library is created by creating a mapping rule from the local features to global features, then feature comparison of the heterogeneous data is carried out, the heterogeneous data are integrated, different features of the data are divided into different classes by the classification training module, the classes can be created according to a training data set, the association rule is determined by finding dependency relations among different data objects, and the functional features of the data in one class are extracted through association clustering to be used as feature description.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of the system module composition of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a technical scheme that an operation platform management system based on big data analysis comprises a data integration module and a data mining module, wherein the data integration module is used for performing specialized integration on heterogeneous data, and the data mining module is used for realizing data parallel mining. The traditional data integration method can not process semantic conflict caused by heterogeneous data, a large amount of redundant data which cannot be integrated due to semantic conflict can be generated in the data processing process, the data processing efficiency is low along with the increase of the data quantity, and the data mining method can perform distributed computation through parallelization of a data mining algorithm and a programming model in the data mining process, so that the efficiency of the data mining method in processing massive data is improved.
The data integration module comprises a heterogeneous data screening module, a local feature extraction module, a global feature library creation module and a feature mapping module, wherein the heterogeneous data screening module is used for screening heterogeneous data in the data integration process, the local feature extraction module is used for carrying out local feature extraction on heterogeneous data to be processed, the global feature library creation module is used for mapping the extracted local features into global features and storing the global features into the library, the feature mapping module is used for creating a mapping rule from the local features to the global features, and the heterogeneous data screening module is connected with the local feature extraction module, the global feature library creation module and the feature mapping module in a closed loop mode. In the data integration process, the semantic defect generated by the heterogeneous data is difficult to overcome by the traditional data integration method, so that after the local features of the heterogeneous data are extracted, the global feature library is created by creating a mapping rule from the local features to the global features, and then the feature comparison of the heterogeneous data is carried out, so that the integration of the heterogeneous data is realized.
The data mining module comprises a classification training module, an association clustering module, a prediction rule making module, a result evaluation module and a visual operation module, wherein the classification training module is used for carrying out classification training according to different characteristics of data, the association clustering module is used for forming data clusters by means of aggregation on the characteristics with association, the prediction rule making module is used for making a prediction rule to conduct characteristic prediction on an unknown data set by analyzing a training data set, the result evaluation module is used for evaluating a data mining result, the data with high importance are screened out, and the visual operation module is used for realizing visual display operation with a data owner. Different characteristics of data are divided into different classes, the classes can be created according to a training data set, association rules are determined by finding dependency relations among different data objects, and functional characteristics of the data in one class are extracted as characteristic descriptions through association clustering.
The operation method of the data integration module comprises the following steps:
step A, screening a data source to obtain heterogeneous data with heterogeneous characteristics, wherein the data source is used as the heterogeneous data source because a large amount of heterogeneous data exists;
step B, extracting local features of the heterogeneous data;
step C, creating a global feature library according to the local features;
and D, establishing a feature mapping rule, and mapping the local features into a global feature library.
In the step A, the method for screening the heterogeneous data comprises the following steps:
step S1, performing preliminary screening according to the surface structure difference of the data, wherein the surface structure difference comprises the physical storage difference of the data, the difference of an operating system and the data format difference;
Step S2, further screening, namely tracing the data sources, judging the logic model of the data sources, and screening according to the logic model, wherein the data are stored and maintained in different business logics, even the same data have logic performance isomerism, and the data can be traced back to the original application scene after tracing, so that logic isomerism screening is carried out;
and step S3, classifying according to the screening result, specifically screening the data with the heterogeneous characteristics.
In the step B, the local feature extraction method of the heterogeneous data comprises the following steps:
Establishing a feature tree, and traversing all data of the heterogeneous data source in the feature tree;
Arranging the data features appearing in the traversal process into a feature tree according to the data size;
taking the characteristic with the largest data quantity in the characteristic tree as the representative characteristic of one section of data;
The method comprises the steps of performing simulation operation on data with extracted local characteristics, recording the minimum editing times n i of the operation, converting a certain section of data into data with the same characteristics through character replacement, insertion or deletion, and recording the required minimum editing times;
and an acquisition operation step of converting the minimum editing times into a distance value len (n i) according to a distance conversion algorithm. The editing times can not be consulted and calculated in the system, the editing times can be converted into distance values more conveniently to participate in calculation, the distance conversion algorithm can convert the editing times into the distance values according to specific operation steps, the calculation is convenient, the preparation is carried out for subsequent global mapping, specific conversion rules are the steps for defining each operation, and the steps are related to the character change degree generated by the operation and are in a proportional relation.
In the step D, the method for establishing the feature mapping rule comprises the following steps:
traversing all local features in the feature tree, calculating distance values, and establishing a distance value array { len (n 1)、len(n2)……len(ni) };
Step b, calculating a stored characteristic distance mean value Ave (n) in the characteristic tree, wherein:
wherein i is the number of local features in the feature tree, and i is not less than 1;
The calculated characteristic distance average value Ave (n) is used as a mapping comparison amplitude, the characteristic distance average value Ave (n) reflects the average characteristic of the heterogeneous data cluster, and the upper limit and the lower limit of the characteristic mapping process can be ensured to be controllable as the mapping comparison amplitude;
step d, establishing a variance operation algorithm, traversing all data in the feature tree again on the basis of the determination of the mapping comparison amplitude to generate a result, wherein the result of the variance operation algorithm displays the difference between the feature distance value and the average value of the data, and screening the data after determining a screening rule;
and e, carrying out numerical limitation on a result generated by a contrast algorithm, storing data lower than the mapping contrast amplitude into a global feature library, and keeping the data higher than the mapping contrast amplitude in a feature tree to enter the integration of the next period. The data with the characteristic distance value lower than the mapping contrast amplitude is the data with the characteristic distance value lower than the average value, the characteristic complexity of the data is lower, the probability of misjudgment is lower, the data can be directly screened out and stored in a global characteristic library, the characteristic complexity of the data with the characteristic distance value higher than the mapping contrast amplitude is higher, and the data is continuously remained in the characteristic tree to carry out new round of integrated screening with the data of the next round.
In the data integration module, after data is mapped into a global feature library, the feature distance value with the largest proportion is used as a main feature, and classification screening is carried out, wherein the classification screening is based on the data features extracted in the manual formulation and big data integration process.
The mining module comprises a method for formulating a prediction rule, which specifically comprises the following steps:
Carrying out association clustering on data with the same characteristic type to generate a data cluster with characteristic values, wherein the characteristic values are characteristic distance average values of all data in a series of data clusters;
and comparing the data cluster with the characteristic value with the characteristic tree generated in the data integration process, screening out the data with the same characteristic value and marking. In the data integration process, the data reserved on the feature tree and the generated data cluster with the feature values have conflicts, the same feature values have conflicts generated among different semantics, the conflicts need to be screened out, and the computational waste caused by redundant operation and semantic conflicts in the prediction process is reduced.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the above-mentioned embodiments are merely preferred embodiments of the present invention, and the present invention is not limited thereto, but may be modified or substituted for some of the technical features thereof by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The operation platform management system based on big data analysis comprises a data integration module and a data mining module, and is characterized in that the data integration module is used for performing specialized integration on heterogeneous data, and the data mining module is used for realizing data parallel mining;
the operation method of the data integration module comprises the following steps:
step A, screening a data source to screen heterogeneous data with heterogeneous characteristics;
step B, extracting local features of the heterogeneous data;
step C, creating a global feature library according to the local features;
step D, establishing a feature mapping rule, and mapping the local features into a global feature library;
In the step B, the local feature extraction method of the heterogeneous data comprises the following steps:
Establishing a feature tree, and traversing all data of the heterogeneous data source in the feature tree;
Arranging the data features appearing in the traversal process into a feature tree according to the data size;
taking the characteristic with the largest data quantity in the characteristic tree as the representative characteristic of one section of data;
Performing simulation operation on the data with the local features extracted, and recording the minimum editing times n i of the operation;
an acquisition operation step of converting the minimum editing times into a distance value len (n i) according to a distance conversion algorithm;
in the step D, the method for establishing the feature mapping rule includes:
traversing all local features in the feature tree, calculating distance values, and establishing a distance value array { len (n 1)、len(n2)……len(ni) };
Step b, calculating a stored characteristic distance mean value Ave (n) in the characteristic tree, wherein:
wherein i is the number of local features in the feature tree, and i is not less than 1;
c, taking the calculated characteristic distance mean value Ave (n) as a mapping contrast amplitude;
step d, establishing a variance operation algorithm, traversing all data in the feature tree again on the basis of determining the mapping comparison amplitude, and generating a result;
And e, carrying out numerical limitation on a result generated by a contrast algorithm, storing data lower than the mapping contrast amplitude into a global feature library, and keeping the data higher than the mapping contrast amplitude in a feature tree to enter the integration of the next period.
2. The operation platform management system based on big data analysis of claim 1, wherein the data integration module comprises a heterogeneous data screening module, a local feature extraction module, a global feature library creation module and a feature mapping module, the heterogeneous data screening module is used for screening heterogeneous data in a data integration process, the local feature extraction module is used for carrying out local feature extraction on heterogeneous data to be processed, the global feature library creation module is used for mapping the extracted local features into global features and storing the global features into a library, the feature mapping module is used for creating a mapping rule of the local features to the global features, and the heterogeneous data screening module is connected with the local feature extraction module, the global feature library creation module and the feature mapping module in a closed loop mode.
3. The operation platform management system based on big data analysis according to claim 2, wherein the data mining module comprises a classification training module, an association clustering module, a prediction rule making module, a result evaluation module and a visual operation module, wherein the classification training module is used for performing classification training according to different characteristics of data, the association clustering module is used for forming data clusters by aggregating the characteristics with association, the prediction rule making module is used for making a prediction rule to conduct characteristic prediction on unknown data sets by analyzing training data sets, the result evaluation module is used for evaluating the result of data mining and screening out data with high importance, and the visual operation module is used for achieving visual display operation with a data owner.
4. The operation platform management system based on big data analysis of claim 3, wherein in the step A, the method for screening heterogeneous data is as follows:
step S1, performing primary screening, wherein the specific screening method is to perform primary screening according to the surface structure difference of the data;
step S2, further screening, namely tracing the data source by a specific screening method, judging a logic model of the data source, and screening according to the logic model;
and step S3, classifying according to the screening result, specifically screening the data with the heterogeneous characteristics.
5. The operation platform management system based on big data analysis of claim 4, wherein in the data integration module, after data is mapped into a global feature library, a feature distance value with the largest proportion is used as a main feature, and classification screening is performed, wherein the classification screening is based on data features extracted in the manual formulation and big data integration process.
6. The operation platform management system based on big data analysis of claim 5, wherein the data mining module comprises a method for making prediction rules, and the method is specifically as follows:
Carrying out association clustering on data with the same characteristic type to generate a data cluster with characteristic values, wherein the characteristic values are characteristic distance average values of all data in a series of data clusters;
And comparing the data cluster with the characteristic value with the characteristic tree generated in the data integration process, screening out the data with the same characteristic value and marking.
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| CN101083656A (en) * | 2007-07-05 | 2007-12-05 | 上海交通大学 | Data stream technique based multi-source heterogeneous data integrated system |
| CN102473298A (en) * | 2009-07-01 | 2012-05-23 | 日本电气株式会社 | System and method for extracting representative features |
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| CN113656480B (en) * | 2021-08-19 | 2024-09-24 | 山东勤成健康科技股份有限公司 | Data integration method and system of heterogeneous data sources |
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| CN101083656A (en) * | 2007-07-05 | 2007-12-05 | 上海交通大学 | Data stream technique based multi-source heterogeneous data integrated system |
| CN102473298A (en) * | 2009-07-01 | 2012-05-23 | 日本电气株式会社 | System and method for extracting representative features |
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