CN117827930A - User data analysis method and system based on data center - Google Patents
User data analysis method and system based on data center Download PDFInfo
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Abstract
The invention provides a user data analysis method and system based on a data center. The method and the system collect original data from various data sources, perform data cleaning, data conversion and data integration, store and manage the preprocessed data in a data center, perform deep analysis on the data on the basis of the data center by using machine learning and statistical analysis technology, and finally present the data analysis result to decision makers and system users in an intuitively and easily understood mode. The method and the system not only improve the efficiency of data analysis, but also improve the depth and breadth of data analysis, so that enterprises can better understand user behaviors, optimize products and services, and improve user satisfaction and business effects.
Description
Technical Field
The invention relates to the technical field of data center application, in particular to a user data analysis method and system based on a data center.
Background
With the development and popularization of big data and cloud computing technology, data has become a core asset for enterprises. User data, which is an important component of the data, provides an important basis for enterprises to understand user behaviors, demands and preferences in depth, thereby helping the enterprises optimize products and services and improving user satisfaction and competitive advantages. However, the management and analysis of user data is a complex task. Because of the variety of sources, the complex format, and the frequent existence of noise and missing values, how to effectively collect, clean, integrate, and analyze user data has become an important technical challenge. In addition, due to the high and complex dimensions of user data, conventional data analysis techniques often fail to meet the needs of deep understanding of user behavior and requirements, requiring deep analysis using machine learning and statistical analysis techniques. In this context, the data center has been developed as a new data structure. The data center platform is a platform for centralized management and service of data, can collect information from multi-source data, perform data cleaning and preprocessing, support deep data analysis and generate user-friendly data products. The application of the data center provides a new solution for the management and analysis of user data. However, how to design and implement a data center-based user data analysis method and system that is effective, efficient, and has a good user experience remains a current technical challenge.
Disclosure of Invention
The invention aims to provide a user data analysis method and a system based on a data center, which can effectively integrate user data from different sources, provide efficient and accurate data analysis, realize deep understanding of user behaviors, optimize products and services and improve user satisfaction and business effects.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a user data analysis method and system based on a data center station comprises the following steps:
s1, data acquisition: user data is collected from a plurality of data sources including, but not limited to, user behavior data, user attribute data, user interaction data, and the like.
S2, data processing: preprocessing the collected user data, including data cleaning, data conversion, data integration and the like, so as to improve the quality and the integrity of the data.
S3, data center processing: the preprocessed user data is integrated into a data center, and the data center is a unified data management and service platform, so that unified storage, unified management and unified service of the data can be realized. User data is analyzed on a data center table, including association rule analysis algorithms, clustering algorithms, data modeling methods and the like.
S4, data analysis: user data is analyzed on the data staging platform, including data aggregation, data mining, data model construction, and the like, to extract valuable information from the user data.
S5, outputting data: and outputting the data analysis result to a user, wherein the output form can be a data report, a data chart, a data instrument panel and the like so as to realize visual display of the data.
In a further technical solution, step S1 includes:
data sources may include, but are not limited to:
a1, database: this may be a relational database (e.g., mySQL), a non-relational database (e.g., mongo db), or a distributed database system. The database may contain basic information of the user, interaction history, purchase records, etc.
A2, log file: this includes server logs, application logs, event logs, and the like. The log file may contain behavioral data of the user, such as page access, clicks, searches, etc.
A3, AP I interface: such as data obtained from social media platforms, payment gateways, third party applications, and the like.
In collecting data, factors such as the frequency of the data (real-time, daily, weekly, etc.), the format of the data (structured, semi-structured, unstructured), etc. need to be considered.
In a further technical solution, step S2 includes:
the collected data is often "dirty" and not suitable for direct analysis. The data preprocessing stage comprises three steps of data cleaning, data conversion and data integration:
b1, data cleaning: this step includes removing duplicate data, processing missing values, correcting erroneous values, identifying and processing outliers, etc.
B2, data conversion: this includes normalization (e.g., scaling all data to a particular range), normalization (e.g., converting data to a standard normal distribution), discretization (e.g., converting continuous variables to discrete variables), and the like.
B3, data integration: if the data comes from multiple sources, it is necessary to integrate the data into a unified view. This may involve issues of entity identification (determining which records are about the same entity), conflict resolution (resolving inconsistencies between different source data), etc.
In a further technical solution, step S3 includes:
the preprocessed data is stored in a data staging platform in preparation for subsequent data analysis. The data center can realize unified data management and service, such as data inquiry, data statistics, data mining and the like.
In a further technical solution, step S4 includes:
this stage uses mainly statistical and machine learning techniques to perform deep analysis on the data. Analyses that may be involved include descriptive analysis (e.g., computing sums, averages, medians, frequencies, etc.), predictive analysis (e.g., time series analysis, regression analysis, machine learning predictive models, etc.), and normative analysis (e.g., optimization models, decision trees, rules engines, etc.).
In a further technical solution, step S5 includes:
the analysis results need to be presented to the decision maker and system user in an easy to understand manner. Data visualization is an important part of this phase, including charts, dashboards, reports, etc. Different data products can be designed according to different requirements. For example:
c1, report: such as daily, weekly, or monthly user behavior reports, sales reports, performance reports, and the like. Reports typically include an overview of some key indicators, and explanations and suggestions of these indicators.
C2, instrument panel: the method is a view updated in real time, and shows the data of key service indexes, performance indexes and the like. Dashboards are commonly used to monitor and manage business operations.
The beneficial effects of the invention are as follows:
(1) The invention can realize the deep analysis of the user data, provide accurate and comprehensive user data analysis results, and is beneficial to improving the efficiency and effect of data driving decision;
(2) The invention can effectively improve the utilization efficiency and the value of the data, help enterprises or organizations to better understand the user behaviors, optimize products and services, and improve the user satisfaction and business effect.
Drawings
Fig. 1 is a block diagram of a system of the present invention.
Fig. 2 is a flow chart of the operation of the data acquisition module.
Fig. 3 is a flowchart of the operation of the data preprocessing module.
Fig. 4 is an architecture diagram of a data center table.
Fig. 5 is a flowchart of the operation of the data analysis module.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Examples:
as shown in fig. 1, the invention provides a user data analysis method and system based on a data center, and the invention comprises the following modules:
s1, a data acquisition module: is responsible for collecting user data from various data sources, and supports various data sources and various data formats, including structured data and unstructured data.
S2, a data preprocessing module: preprocessing the collected user data, including data cleaning, data conversion, data integration and the like, and improving the quality and the integrity of the data.
S3, data center: and the unified data management and service platform supports unified storage, unified management and unified service of the data. The data center may include a data storage layer, a data service layer, and a data interface layer.
S4, a data analysis module: user data is analyzed on a data center including data aggregation, data mining, data model construction, and the like. The data analysis module may contain a variety of data analysis algorithms and models that support machine learning and deep learning.
S5, a data output module: and outputting the data analysis result to a user, supporting various output forms including a data report, a data chart, a data dashboard and the like, and realizing the visual display of the data.
Through the modules, the invention can realize the deep analysis of the user data, provide accurate and comprehensive user data analysis results, and is beneficial to improving the efficiency and effect of data driving decision.
The various modules of the user data analysis system of the present invention are further described below in conjunction with fig. 2-5.
Fig. 2 is a flow chart of the operation of the data acquisition module. The data collection module supports collection of user data from a variety of data sources, including databases, log files, data burial point APIs, and the like. The data acquisition module can acquire data at regular time or in real time, and support various data formats, including structured data such as CSV, XML, JSON and the like; the data acquisition module can use open source logstack to collect data into the first data buffering middleware, and the data preprocessing module can use open source software Kafka as the data buffering layer middleware to process the data.
Fig. 3 is a flowchart of the operation of the data preprocessing module. The data preprocessing module preprocesses the acquired user data so as to improve the quality and the integrity of the data. Data cleansing includes removing extraneous data, filling in missing data, correcting erroneous data, and the like. Data conversion includes data format conversion, data normalization, data encoding, and the like. Data integration is the processing of data from different data sources and writing to a data warehouse in a data center.
Fig. 4 is an architecture diagram of a data center table. The data center is a unified data management and service platform, and supports unified storage, unified management and unified service of data. The data center includes a data storage layer, a data service layer and a data interface layer. The data storage layer is used for storing the preprocessed user data, the data service layer is used for providing data service for the outside, and the data interface layer is used for carrying out data interaction with other modules.
Fig. 5 is a flowchart of the operation of the data analysis module. The data analysis module analyzes the user data on the data center table, including data aggregation, data mining, data model construction and the like. The data analysis module may contain a variety of data analysis algorithms and models, including statistical analysis, machine learning, deep learning, and the like.
The user data analysis system also comprises a data output module which outputs the data analysis result to the user and supports various output forms including data report, data chart, data instrument panel and the like, thereby realizing the visual display of the data.
Through the steps and the modules, the invention can realize the deep analysis of the user data, provide accurate and comprehensive user data analysis results, and is beneficial to improving the efficiency and effect of data driving decision.
The following is a detailed description of some specific embodiments of the invention in order to provide a better understanding of the invention and its implementation.
Example 1: the system is applied to an e-commerce website with the aim of understanding consumer behavior and improving sales and customer satisfaction.
(1) And a data acquisition module: first, this module collects user behavior data (e.g., purchasing behavior, browsing behavior, searching behavior, etc.), user information (e.g., age, gender, geographic location, etc.), merchandise information (e.g., price, type, rating, etc.) from different data sources such as a database, log file, AP I interface, etc. of the e-commerce platform. For example, personal information and purchase records of the user are obtained from a database, web browsing and search logs of the user are extracted from a log file, and commodity information and inventory information are obtained from an AP I interface.
(2) And a data preprocessing module: the data preprocessing module cleans, integrates and converts the data. For example, the purge function may remove invalid or erroneous data, such as invalid log-in records, duplicate purchase records, and the like. The conversion function may process unstructured user behavior log data, such as converting the original behavior log into structured user purchase path data. The integration function can integrate different types of data such as personal information, user behaviors, commodity information and the like to form a complete user portrait.
(3) Data center: these processed data are transmitted to the data center station. On the data staging, the data analysis module uses machine learning algorithms to perform a deep analysis on the data. For example, the FP-Growth library may be used to analyze the relationship between the purchase records, the browse records, and the collection records of the user, thereby implementing personalized recommendation. The method comprises the following specific steps: screening the data of purchase records, browse records and collection records from the data provided in the step (2); using an FP-Growth algorithm to find out frequent item sets and association rules; extracting characteristics of user purchase preference and browsing preference according to data such as user purchase records and browsing records; and calculating the similarity between users, and recommending commodities related to the user records according to the characteristics and the similarity of the users and by combining frequent item sets and association rules.
(4) And a data output module: the data output module presents the purchase analysis results to the merchant in the form of a chart or report. Merchants can know the buying habits and preferences of users through the analysis results, and formulate more effective sales strategies and marketing campaigns; meanwhile, personalized commodities can be recommended according to the user portrait, and user satisfaction and purchase conversion rate are improved.
In the embodiment, the electronic commerce website can better understand the behaviors and demands of consumers through the user data analysis method and system based on the data center, and improves the sales effect and the customer satisfaction, so that the commercial competitiveness is enhanced.
Example 2: the system is applied to an online educational platform to help teachers and students better understand and improve the learning process.
(1) Firstly, the data acquisition module collects learning behavior data (such as learning time, learning frequency, answer records and the like), achievement data, course data and the like of students from different data sources such as a database, a log file, an API (application program interface) and the like of the platform. For example, personal information and performance records of students may be obtained from a database, learning behavior logs may be extracted from log files, course content and structure information may be obtained from an API interface.
(2) The data preprocessing module then cleans, integrates and converts the acquired data. For example, the purge function may remove invalid or erroneous data, such as invalid log records, duplicate performance records, and the like. The conversion function may process unstructured learning behavior log data, such as extracting key features of learning behavior, and convert the original behavior log into structured learning path data. The integration function can integrate different types of data such as personal information, learning behavior, achievement data and the like to form a complete learning portrait.
(2) These processed data are then transmitted to the data center. On the data staging, the data analysis module uses a machine learning algorithm to perform a deep analysis on the learning data. For example, according to the learning time, learning frequency and other data of students, the students are clustered by using a clustering algorithm such as a K-Means algorithm, so as to find out the learning characteristics and problems of the students in different groups. The method comprises the following specific steps: using a data set containing student learning time and learning frequency; k students are selected as center points; calculating distances between other students and K center points by using the learning time and the learning frequency, and distributing each student to the nearest center point; updating the central point of each cluster by using the average value of all learning time and learning frequency in each cluster; the whole process is iterated until the clusters are not changed any more; and finally obtaining cluster area data.
(4) Finally, the data output module presents the learning analysis results to the teacher and the students in the form of charts or reports. Through the analysis results, teachers can know the learning condition and the problem of students and put forward targeted teaching suggestions; students can also learn own learning weaknesses and advantages through the analysis results, and learning efficiency is improved.
Through the system, the learning data of the online education platform can be deeply analyzed, so that the teaching quality and the learning effect are improved.
Claims (9)
1. A method for analyzing user data based on a data center, comprising the steps of:
step S1, collecting user data from a plurality of data sources, wherein the user data comprises user behavior data, user attribute data and user interaction data;
s2, preprocessing the acquired user data;
step S3, integrating the preprocessed user data into a data center, wherein the data center is a unified data management and service platform and can realize unified storage, unified management and unified service of the data; analyzing the user data on a data center table, wherein the analysis is descriptive analysis, predictive analysis or normative analysis;
and S4, outputting the data analysis result to a user.
2. The method of claim 1, wherein the data source in step S1 includes a database, a log file, and a data burial point API interface.
3. The method for analyzing user data based on a data center according to claim 1, wherein the data preprocessing in step S2 includes data cleansing, data conversion, and data integration; the data cleaning comprises the steps of removing repeated data, processing missing values, correcting error values, converting the data into standardization, normalization or discretization, and data integration comprises entity identification and conflict resolution.
4. The method of claim 1, wherein the data center includes a data storage layer, a data service layer, and a data interface layer; the data storage layer is used for storing the preprocessed user data, the data service layer is used for providing data service for the outside, and the data interface layer is used for carrying out data interaction with other modules.
5. The method for analyzing user data based on a data center according to claim 1, wherein the step S3 of analyzing the user data comprises the steps of:
using an FP-Growth algorithm to find out frequent item sets and association rules of the user data; extracting user features from the user data; and calculating the similarity between the users.
6. The method for analyzing user data based on a data center according to claim 1, wherein the user data is student data, and the analyzing the student data in step S3 includes the steps of:
k students are selected as central points by using a K-Means algorithm; calculating distances between other students and K center points by using the learning time and the learning frequency, and distributing each student to the nearest center point; updating the central point of each cluster by using the average value of all learning time and learning frequency in each cluster; the whole process is iterated until the cluster is no longer changing.
7. A method of analyzing user data based on a data center as claimed in claim 1, wherein the step S4 is performed in the form of a chart, a dashboard or reporting these visualizations.
8. A user data analysis system based on the method of any one of claims 1-7, comprising a data acquisition module, a data preprocessing module, a data center, a data output module;
the data acquisition module is used for acquiring user data from a plurality of data sources, wherein the user data comprises user behavior data, user attribute data and user interaction data;
the data preprocessing module is used for preprocessing the acquired user data, and comprises data cleaning, data conversion and data integration;
the data center is used for carrying out deep analysis on the preprocessed data by utilizing machine learning and statistical analysis technology;
the data output module is used for outputting the data analysis result to the user in a visual form.
9. The system of claim 8, wherein the data center includes a data storage layer, a data service layer, and a data interface layer; the data storage layer is used for storing the preprocessed user data, the data service layer is used for providing data service for the outside, and the data interface layer is used for carrying out data interaction with other modules.
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