CN113987086A

CN113987086A - Data processing method, data processing device, electronic device, and storage medium

Info

Publication number: CN113987086A
Application number: CN202111251602.0A
Authority: CN
Inventors: 王光浩
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-10-26
Filing date: 2021-10-26
Publication date: 2022-01-28

Abstract

The disclosure provides a data processing method, a data processing device, electronic equipment and a storage medium, relates to the technical field of computers, and particularly relates to the technical field of data warehouses, big data and cloud computing. The specific implementation scheme is as follows: acquiring task dimension data corresponding to a task theme, user dimension data corresponding to a user theme and fact data corresponding to facts from a plurality of service data, wherein the task dimension data represents the service data corresponding to the dimension related to the task theme, and the user dimension data represents the service data corresponding to the dimension related to the user theme; based on a business hierarchy relation, obtaining at least one fact data set according to the task dimension data, the user dimension data and the fact data, wherein the business hierarchy relation represents a hierarchy relation of task topics and a hierarchy relation of user topics; and obtaining at least one statistical data set corresponding to each statistical index in the at least one statistical index according to the at least one fact data set.

Description

Data processing method, data processing device, electronic device, and storage medium

Technical Field

The present disclosure relates to the field of computer technology, and more particularly, to data warehouse, big data, and cloud computing technologies. And in particular to a data processing method, a data processing apparatus, an electronic device, and a storage medium.

Background

The data processing operation is executed by utilizing the service data with higher quality, so that the safety and the stability of the project can be improved, and the landing process of the project is promoted.

For example, the annotation data with higher quality can be obtained according to the service data with higher quality. The labeling data with higher quality can be used as a training sample for training the model in the field of artificial intelligence, so that the safety and the stability of the relevant items based on the model are improved.

Disclosure of Invention

The present disclosure provides a data processing method, a data processing apparatus, an electronic device, and a storage medium.

According to an aspect of the present disclosure, there is provided a data processing method including: acquiring task dimension data corresponding to a task theme, user dimension data corresponding to a user theme and fact data corresponding to facts from a plurality of service data, wherein the task dimension data represents the service data corresponding to the dimension related to the task theme, and the user dimension data represents the service data corresponding to the dimension related to the user theme; based on a business hierarchy relationship, obtaining at least one fact data set according to the task dimension data, the user dimension data and the fact data, wherein the business hierarchy relationship represents a hierarchy relationship of the task theme and a hierarchy relationship of the user theme; and obtaining at least one statistical data set corresponding to each statistical index in the at least one statistical index according to the at least one fact data set.

According to another aspect of the present disclosure, there is provided a data processing apparatus including: the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring task dimension data corresponding to a task theme, user dimension data corresponding to a user theme and fact data corresponding to a fact from a plurality of service data, the task dimension data represents the service data corresponding to the dimension related to the task theme, and the user dimension data represents the service data corresponding to the dimension related to the user theme; a first obtaining module, configured to obtain at least one fact data set according to the task dimension data, the user dimension data, and the fact data based on a business hierarchy relationship, where the business hierarchy relationship represents a hierarchy relationship of the task topic and a hierarchy relationship of the user topic; and the second obtaining module is used for obtaining at least one statistical data set corresponding to each statistical index in the at least one statistical index according to the at least one fact data set.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method as described above.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method as described above.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 schematically illustrates an exemplary system architecture to which the data processing method and apparatus may be applied, according to an embodiment of the present disclosure;

FIG. 2 schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates an example schematic diagram of a data query process according to an embodiment of this disclosure;

FIG. 4 schematically illustrates a flow diagram for storing raw business data to a data warehouse, in accordance with an embodiment of the present disclosure;

fig. 5 schematically illustrates an example schematic diagram of a process of acquiring a plurality of raw service data according to an embodiment of the present disclosure;

FIG. 6 schematically illustrates an example schematic diagram of a process of generating a set of statistical data, in accordance with an embodiment of this disclosure;

FIG. 7 schematically shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure; and

fig. 8 schematically shows a block diagram of an electronic device adapted to implement a data processing method according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In order to obtain the annotation data with higher quality, it is necessary to provide more efficient service data. In the process of obtaining more efficient service data, the calculation of the service data is involved. The service data can be stored in the database for implementation after the structured query statement is executed by using the script. For example, after a structured query statement is executed by a PHP (Hypertext Preprocessor), the business data is stored in a database. The above treatment method has low treatment efficiency.

Therefore, the embodiment of the present disclosure provides a data processing scheme based on a data warehouse, that is, task dimension data corresponding to a task topic, user dimension data corresponding to a user topic, and fact data corresponding to a fact are obtained from a plurality of business data. The task dimension data represents business data corresponding to the dimension related to the task theme, and the user dimension data represents business data corresponding to the dimension related to the user theme. And based on the business hierarchy relationship, obtaining at least one fact data set according to the task dimension data, the user dimension data and the fact data. The business hierarchy relationship represents the hierarchy relationship of the task theme and the hierarchy relationship of the user theme. And obtaining at least one statistical data set corresponding to each statistical index in the at least one statistical index according to the at least one fact data set, so that the business data corresponding to the statistical index is constructed more quickly, and the data processing efficiency is improved. To facilitate the understanding that follows, concepts related to embodiments of the present disclosure are first explained.

The data warehouse is a structured data environment for decision support systems and online analysis application data sources. Data warehouses research and solve the problem of obtaining information from databases. Data warehouses are characterized by theme-oriented, integrated, stable, and time-varying properties.

A topic may refer to an abstraction that integrates and categorizes business data at a higher level and makes analytical use of it. Each topic corresponds to a macroscopic field of analysis.

A dimension may refer to a selection and measure of facts, which may be viewed as a perspective of analyzing business data.

Facts may refer to data that needs attention, i.e. metric values extracted from traffic data. The metric values may include accumulated metric values and non-accumulated metric values.

Fig. 1 schematically shows an exemplary system architecture to which the data processing method and apparatus may be applied, according to an embodiment of the present disclosure.

It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, a system architecture 100 according to this embodiment may include a business system 110, a data warehouse system 120, and an application layer system 130. Data warehouse system 120 may be communicatively coupled to business system 110 and application layer system 130, respectively, via a network. A network may be the medium that provides the communication links. The network may include various connection types, such as at least one of wired communication links, wireless communication links, and so forth.

Business system 110 may include database 111, log system 112, event bus dotting system 113, and cloud storage 114.

Data Warehouse system 120 may include metadata management 121, Data quality monitoring 122, an raw Data Store (ODS) layer 123, a Data Warehouse (DW) layer 124, and an Application Data Service (ADS) layer 125. Data Warehouse tier 124 may include a Data Warehouse Detail (DWD) tier 1240, a common dimension Summary (i.e., DIM) tier 1241, and a Data Warehouse Summary (DWS) tier 1242.

The data warehouse system 120 may be a Server providing various services, for example, the Server may be a cloud Server, which is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, which solves the defects of large management difficulty and weak service extensibility in a conventional physical host and VPS service (VPS). The server may also be a server of a distributed system, or a server incorporating a blockchain.

The application layer 130 may include a reporting platform 131, an experiment platform 132, and a statistics application interface 133 and a business module 134.

Data warehouse system 120 may obtain raw business data from business system 110. Data warehouse system 120 may obtain task dimensional data corresponding to task topics, user dimensional data corresponding to user topics, and fact data corresponding to facts from a plurality of business data. The task dimension data represents business data corresponding to the dimension related to the task theme, and the user dimension data represents business data corresponding to the dimension related to the user theme. And based on the business hierarchy relationship, obtaining at least one fact data set according to the task dimension data, the user dimension data and the fact data. The business hierarchy relationship represents the hierarchy relationship of the task theme and the hierarchy relationship of the user theme. And obtaining at least one statistical data set corresponding to each statistical index in the at least one statistical index according to the at least one fact data set.

The application layer system 130 may generate a query request, the query request including query conditions. The generation request is sent to data warehouse system 120. Data warehouse system 120 may determine, in response to the query request, data query results matching the query condition from the at least one set of statistical data corresponding to each of the at least one statistical indicator based on the query condition.

It should be noted that the data processing method provided by the embodiments of the present disclosure may be generally performed by data warehouse system 120. Accordingly, the data processing apparatus provided by the embodiments of the present disclosure may be generally disposed in data warehouse system 120. The data processing methods provided by embodiments of the present disclosure may also be performed by a server or cluster of servers that is different from data warehouse system 120 and that is capable of communicating with data warehouse system 120. Accordingly, the data processing apparatus provided by the embodiments of the present disclosure may also be disposed in a server or a server cluster different from data warehouse system 120 and capable of communicating with data warehouse system 120.

It should be understood that the system architecture in fig. 1 is merely illustrative. Other forms of system architectures are possible, as implementation requires.

Fig. 2 schematically shows a flow chart of a data processing method according to an embodiment of the present disclosure.

As shown in FIG. 2, the method 200 includes operations S210-S230.

In operation S210, task dimensional data corresponding to a task topic, user dimensional data corresponding to a user topic, and fact data corresponding to a fact are acquired from a plurality of business data. The task dimension data represents business data corresponding to the dimension related to the task theme, and the user dimension data represents business data corresponding to the dimension related to the user theme.

In operation S220, at least one fact data set is obtained according to the task dimension data, the user dimension data, and the fact data based on the service hierarchy relationship. The business hierarchy relationship represents the hierarchy relationship of the task theme and the hierarchy relationship of the user theme.

In operation S230, at least one statistical data set corresponding to each of the at least one statistical indicator is obtained according to the at least one fact data set.

According to an embodiment of the present disclosure, the service data may refer to data related to a service. For example, the service data may be operation data generated by a user performing a relevant operation.

According to embodiments of the present disclosure, topics may include task topics and user topics. For each business scenario, it can be analyzed from both the perspective of task topics and user topics. That is, for each business scenario, the angle from which the business scenario is analyzed can be divided into a task topic and a user topic. Task topics may refer to topics that analyze business data from a task perspective. The user topic may refer to a topic that analyzes business data from a user perspective. Both task topics and user topics may be topics having a hierarchical relationship. That is, the task topic may include a plurality of dimensions related to the task topic, and the plurality of dimensions related to the task topic have a hierarchical relationship therebetween. The user theme may include a plurality of user theme related dimensions having a hierarchical relationship therebetween.

According to the embodiment of the disclosure, the business hierarchy relationship can be used for representing the hierarchy relationship of the task topic and the hierarchy relationship of the user topic. Business hierarchy relationships can be used as a basis for building factual data sets. The statistical indicator can be used as a basis for service data aggregation. The statistical indicators may include indicators related to the items, indicators related to the resources, and indicators related to the interactions.

According to the embodiment of the disclosure, after the plurality of business data are obtained, task dimension data corresponding to a task topic, user dimension data corresponding to a user topic, and fact data corresponding to a fact can be obtained from the plurality of business data. Then, the dimension related to the task theme and the dimension related to the user theme can be associated based on the hierarchical relationship of the task theme and the hierarchical relationship of the user theme. And acquiring business data corresponding to the associated dimensionality related to the task theme and the dimensionality related to the user theme from the task dimensionality data, the user dimensionality data and the fact data according to the associated dimensionality related to the task theme and the dimensionality related to the user theme, and aggregating the associated dimensionality related to the task theme and the business data corresponding to the dimensionality related to the user theme to obtain at least one fact data set. The fact data set may be a fact data table. Finally, at least one statistical indicator may be determined. For each statistical index of the at least one statistical index, the business data corresponding to the statistical index may be obtained from the at least one fact data set. And obtaining at least one statistical data set corresponding to the statistical index according to the service data corresponding to the statistical index. The set of statistics may be a table of statistics.

According to the embodiment of the disclosure, at least one fact data set is obtained according to the task dimension data, the user dimension data and the fact data based on the business hierarchy relationship, and at least one statistical data set corresponding to the statistical index is obtained according to the at least one fact data set, so that the business data corresponding to the statistical index is constructed more quickly, and the data processing efficiency is improved.

The data processing method according to the embodiment of the disclosure is further described with reference to fig. 3 to fig. 6.

According to an embodiment of the present disclosure, operation S210 may include the following operations.

And determining the characteristics corresponding to the task theme, the characteristics corresponding to the user theme and the characteristics corresponding to the fact according to the characteristic association relation. And acquiring task dimension data corresponding to the task theme from the plurality of service data according to the characteristics corresponding to the task theme. And acquiring user dimension data corresponding to the user theme from the plurality of service data according to the characteristics corresponding to the user theme. From the plurality of service data, fact data corresponding to the fact is acquired according to the feature corresponding to the fact.

According to an embodiment of the present disclosure, the feature association relationship may refer to an association relationship between an analysis angle and a feature related to the analysis angle. The analysis angle may include a topic and a fact. Topics may include task topics and user topics.

According to the embodiment of the disclosure, the feature corresponding to the analysis angle can be determined according to the incidence relation between the analysis angle and the feature related to the analysis angle. After the feature corresponding to the analysis angle is determined, the business data corresponding to the analysis angle is acquired from the plurality of business data according to the feature corresponding to the analysis angle.

For example, if the analysis perspective is a task topic, the features to which the task topic relates may include features corresponding to dimensions related to the task topic. For example, features corresponding to dimensions related to task topics can include features corresponding to page dimensions, features corresponding to task dimensions, features corresponding to batch dimensions, and features corresponding to project dimensions.

For example, if the analysis perspective is a user topic, the features to which the user topic relates may include features corresponding to dimensions related to the user topic. For example, features corresponding to dimensions related to a user's theme may include features corresponding to a user dimension, features corresponding to a guild dimension, and features corresponding to an agent dimension.

For example, if the analysis angle is a fact, the features related to the fact may include the correct answer rate, the answer time, and the like.

According to the embodiment of the disclosure, the task topic comprises a plurality of dimensions which have a hierarchical relationship and are related to the task topic, and the user comprises a plurality of dimensions which have a hierarchical relationship and are related to the user topic.

According to an embodiment of the present disclosure, operation S220 may include the following operations.

And associating the plurality of dimensions with the hierarchical relationship and related to the task subject with the plurality of dimensions with the hierarchical relationship and related to the user subject to obtain a plurality of associated dimensions with the hierarchical relationship. And based on a plurality of associated dimensions with hierarchical relationship, performing aggregation processing on the task dimension data, the user dimension data and the fact data to obtain at least one fact data set.

According to embodiments of the present disclosure, a task topic may include a plurality of dimensions related to the task topic. Multiple dimensions related to task topics may have a hierarchical relationship. The user theme may include a plurality of dimensions related to the user theme. Multiple dimensions related to a user's topic may have a hierarchical relationship. A dimension may include at least one sub-dimension.

For example, dimensions related to task topics may include a page dimension, a task dimension, a batch dimension, and a project dimension. The hierarchy of page dimensions, task dimensions, batch dimensions, and project dimensions are sequentially increased. The dimensions related to the user's theme may include a user dimension, a guild dimension, and an agent dimension. The hierarchy of user dimensions, guild dimensions, and agent dimensions increases in order.

According to embodiments of the present disclosure, the relevance dimension may characterize a dimension that is relevant to both the dimension that is relevant to the task topic and the dimension that is relevant to the user topic. The plurality of association dimensions may be obtained by associating a plurality of task topic-related dimensions having a hierarchical relationship with a plurality of user topic-related dimensions having a hierarchical relationship. Multiple associated dimensions may have a hierarchical relationship.

For example, a user topic may include M dimensions related to the user topic, namely, a 1 st dimension related to the user topic, a 2 nd dimension related to the user topic, … …, an ith dimension related to the user topic, … …, an (M-1) th dimension related to the user topic, and an Mth dimension related to the user topic. i ∈ {1, 2, … …, M-1, M }. M may be an integer greater than or equal to 2.

For example, a task topic may include N task topic-related dimensions, namely, a 1 st task topic-related dimension, a 2 nd task topic-related dimension, … …, a j th task topic-related dimension, … …, an (N-1) th task topic-related dimension, and an Nth task topic-related dimension. N may be an integer greater than or equal to 2. j ∈ {1, 2, … …, N-1, N }. The associated dimensions may include the ith dimension related to the user's topic-the jth dimension related to the task's topic.

According to the embodiment of the disclosure, a plurality of dimensions related to task topics with a hierarchical relationship and a plurality of dimensions related to user topics with a hierarchical relationship can be associated to form a plurality of associated dimensions with a hierarchical relationship. After determining a plurality of associated dimensions with a hierarchical relationship, business data related to the associated dimensions can be obtained from the task dimension data, the user dimension data and the fact data, and the business data related to the associated dimensions are subjected to aggregation processing to obtain at least one fact data set.

According to an embodiment of the present disclosure, operation S230 may include the following operations.

At least one statistical indicator is determined according to the business demand rules. And for each statistical index in the at least one statistical index, associating at least one fact data set corresponding to the statistical index to obtain at least one statistical data set corresponding to the statistical index.

According to embodiments of the present disclosure, the business requirement rules may be used as rules for determining statistical indicators. At least one statistical indicator corresponding to the business demand rule may be determined according to the business demand rule. After determining at least one statistical index, according to each statistical index, finding a fact data set related to the statistical index from at least one fact data set, and associating the fact data set related to the statistical index, thereby obtaining one or more statistical data sets corresponding to the statistical index.

For example, the business requirement may be a requirement to count role information of the user. The statistical indicators determined according to the business requirement rules include type, time, and participating items. After determining the three statistical indicators, a fact data set associated with each of the three statistical indicators may be looked up from the at least one fact data set, resulting in at least one statistical data set corresponding to each statistical indicator.

According to an embodiment of the present disclosure, the data processing method may further include the following operations.

And responding to the query request, and determining a data query result matched with the query condition from at least one statistical data set corresponding to each statistical index in at least one statistical index according to the query condition included in the query request.

According to an embodiment of the present disclosure, a query request may refer to a request for processing a query and a query condition included in the query request. The query request may be generated upon detecting that a query operation for a query box is triggered. The query box may be used for entering query conditions or for selecting query conditions. The query operation may include a click operation, a slide operation, or a voice-triggered operation. For example, in the case where it is detected that a query operation for the query box is triggered, a query condition input in the query box by the operator is acquired. And generating a query request according to the query condition. The query condition may refer to a condition that needs to be satisfied by the service data that needs to be acquired.

According to an embodiment of the disclosure, a query request may be obtained, and the query request may include a query condition. And responding to the query request, determining the business data matched with the query condition from at least one statistical data set according to the query condition, and determining the business data matched with the query condition as a data query result.

According to an embodiment of the present disclosure, in response to a query request, determining a data query result matching the query condition from at least one statistical data set corresponding to each of at least one statistical index according to the query condition included in the query request may include the following operations.

In response to the query request, a data warehouse interface is invoked. And determining a data query result matched with the query condition from at least one statistical data set corresponding to each statistical index in at least one statistical index according to the query condition included in the query request by using the data warehouse interface.

According to embodiments of the present disclosure, a data warehouse interface may be configured. A data warehouse interface may refer to an interface for obtaining data query results that match query conditions included with a query request. The data warehouse interface may have RESTful style specifications. REST (Representational State Transfer) characterizes the presentation level State transitions of internet resources. RESTful is an internet software architecture.

According to the embodiment of the disclosure, the data warehouse interface can be called in response to the query request, then the data warehouse interface is utilized to query the business data matched with the query condition from at least one statistical data set, and the data query result is obtained according to the business data matched with the query condition.

According to the embodiment of the present disclosure, the data warehouse system may further access a Dashboard (i.e., data visualization) module and a Showx module to adapt to business requirements.

According to the embodiment of the disclosure, after the data query result is obtained, the data query result can be displayed in a form of a business report. The presentation form may include at least one of a bar graph, a line graph, a progress graph, and a table.

And processing the data query result to obtain a data processing result.

According to the embodiment of the disclosure, after the data query result is obtained, subsequent data processing can be performed by using the data query result. For example, data annotation can be performed by using the data query result to obtain a data annotation result. For example, for data labeling in the field of artificial intelligence, a data tag can be determined according to model requirement information. And carrying out data annotation on the data query result according to the data tag to obtain a data annotation result.

According to the embodiment of the disclosure, other calculations are performed by using the statistical data set corresponding to the statistical indexes based on the construction, so that the processing efficiency can be improved, and the short-time generation requirement of mass data can be met.

FIG. 3 schematically shows an example schematic of a data query process according to an embodiment of the disclosure.

As shown in FIG. 3, in 300, a data warehouse interface 302 is invoked in response to a query request. Using the data warehouse interface 302, according to the query condition included in the query request, in the data warehouse 301, a data query result 303 matching the query condition is determined from at least one statistical data set corresponding to each of the at least one statistical indicator.

And carrying out data cleaning on the plurality of original service data to obtain a plurality of service data.

According to the embodiment of the disclosure, data cleaning can be used for filtering out the service data which do not meet the preset requirements and reserving the service data which meet the preset requirements. The type of the service data requiring data cleansing may include at least one of incomplete service data, erroneous service data, and repetitive service data.

According to the embodiment of the disclosure, a plurality of original service data from a plurality of data sources can be subjected to data cleaning to obtain a plurality of service data. The original traffic data of each data may include one or more.

According to the embodiment of the disclosure, the data quality of the service data is improved by performing data cleaning on a plurality of original data.

FIG. 4 schematically illustrates a flow diagram for storing raw business data to a data warehouse, in accordance with an embodiment of the present disclosure.

As shown in fig. 4, the method 400 includes operations S401 to S402.

In operation S401, a storage policy of raw business data corresponding to each data source is determined based on a data structure specification of the data warehouse and a data structure of each data source of the plurality of data sources.

In operation S402, the raw business data is stored to the data warehouse according to the storage policy of the raw business data corresponding to each data source.

According to an embodiment of the present disclosure, the data warehouse has a corresponding data structure specification. A data structure specification for a data warehouse may refer to a data structure that is used to specify the data that is to be stored to the data warehouse. Each data source has a data structure corresponding to the data source. The data sources may include at least one of a summary level database, a business configuration data source, a fine-grained distributed database, a system service, a system log, and a system bus.

According to an embodiment of the present disclosure, the storage policy may refer to a policy for how to implement that, in a case where the original business data corresponding to each data source is stored in the data warehouse, the data structure of the original business data corresponding to each data source is a data structure matching the data structure specification of the data warehouse, that is, the storage policy may refer to storing the business data corresponding to each data source according to the data structure specification of the data warehouse, so that the data structure of the original business data of the data source stored in the data warehouse matches the data structure corresponding to the data structure specification.

According to the embodiment of the disclosure, for each of a plurality of data sources, a storage policy of raw business data corresponding to the data source, that is, a storage policy for storing the raw business data corresponding to the data source, may be determined according to a data structure specification of a data warehouse and a data structure of the data source. In the case that the storage policy of the original service data corresponding to the data source is determined, the original service data corresponding to the data source may be stored in the data warehouse according to the storage policy of the original service data corresponding to the data source.

According to an embodiment of the present disclosure, storing the raw business data corresponding to the data source to the data warehouse according to the storage policy of the raw business data corresponding to the data source may include: raw service data from the data source is obtained. And storing the original business data to a data warehouse according to the storage strategy of the original business data corresponding to the data source so that the data structure of the original business data corresponding to the data source stored to the data warehouse is matched with the data structure corresponding to the data structure specification of the data warehouse. Storing raw business data corresponding to the data source to a data repository may include: and storing the original business data corresponding to the data source to an original data layer of the data warehouse.

According to the embodiment of the disclosure, the storage strategy of the original service data corresponding to each data source is determined by using the data structure specification based on the data warehouse and the data structure of each data source in the plurality of data sources, and the original service data corresponding to each data source is stored in the data warehouse, so that the data structure of the original service data from different data sources can be effectively ensured to be matched with the data structure corresponding to the data structure specification of the data warehouse, the problem of heterogeneity of the data structure is solved, and the development complexity is reduced.

Operation S402 may include the following operations according to an embodiment of the present disclosure.

And determining a data import tool matched with the storage strategy according to the storage strategy of the original service data corresponding to each data source. And storing the original business data to a data warehouse by using a data import tool matched with the storage strategy.

According to the embodiment of the disclosure, for each of a plurality of data sources, after determining the storage policy of the original service data corresponding to the data source, a data import tool matching the service requirement and the storage policy may be determined according to the service requirement and the storage policy of the original service data corresponding to the data source, so that the original service data from the data source is acquired by using the data import tool. And storing the original business data to a data warehouse according to the storage strategy of the original business data corresponding to the data source so that the data structure of the original business data corresponding to the data source stored to the data warehouse is matched with the data structure corresponding to the data structure specification of the data warehouse and the business requirement can be met.

By adapting to different service requirements, development costs can be reduced. The traffic demand may include at least one of a real-time demand, a data volume demand, and a data storage characteristic demand. For example, for the real-time requirement, if the original service data has a high real-time requirement, a data import tool with a function of supporting faster synchronization or a data import tool with a function of supporting connection import may be selected to perform the storage operation. For example, a Data import tool with a function to support faster synchronization may include a DTS (Data Transmission Service). The data import tool with the function of supporting connection import may include Flink. For the quantity requirement and the data storage characteristic requirement, if the data quantity of the original service data is large and the data storage is scattered, a data import tool supporting the connection aggregation function can be selected to execute the storage operation. For example, a data import tool with support for connection aggregation may include SPARK.

Fig. 5 schematically illustrates an example schematic diagram of a process of acquiring a plurality of raw service data according to an embodiment of the present disclosure.

As shown in fig. 5, in 500, the data sources include a summary level database, a traffic configuration data source, a fine-grained distributed database, a system service, a system log, and a system bus.

For the summary level database, the original service data in the summary level database can be synchronized to the original data layer in the data warehouse by using a data import tool DTS according to the configuration information. For example, the Stream Load mechanism provided by Doris may be invoked using DTS to synchronize raw business data in the summary level database to the raw data tier in the data warehouse. In addition, the data import tool Doris can be utilized to store the raw business data in the summary level database to the raw data layer in the data warehouse based on Mapping mechanism of Doris. Doris may be an interactive SQL (Structured Query Language) data warehouse based on MPP (Massively Parallel Processor) architecture.

For the service configuration data source, the data import tool Doris may be utilized to store the original service data in the service configuration data source to the original data layer in the data warehouse based on Mapping (i.e., Mapping) mechanism of Doris.

For the fine-grained distributed database, the system service and the system log, a data import tool SPARK can be utilized to execute a connection script, and original business data in the fine-grained distributed database, the system service and the system log are stored to an original data layer in a data warehouse based on a connection (i.e. Connector) mechanism.

For the system bus, at least one of a data import tool Flink and a data import tool Doris may be utilized to store the raw business data in the system bus to a raw data layer in the data warehouse. For example, raw business data in the system bus may be stored to a raw data layer in a data warehouse based on a Flink-based connection (i.e., Connector) mechanism or a Doris-based route Load mechanism.

Fig. 6 schematically illustrates an example schematic diagram of a process of generating a set of statistical data according to an embodiment of this disclosure.

As shown in fig. 6, at 600, a plurality of raw business data is stored at a raw data layer of a data warehouse. For example, the plurality of original service data are service data related to the answering task. The plurality of original service data may include original task information, original user information, … …, original page answer records, original page correct rate records, original man-hour records, and the like.

And the data detail layer of the data warehouse carries out data cleaning on the plurality of original business data to obtain a plurality of business data. For example, the plurality of business data may include task information, user information, … …, page answer records, page correctness records, and man-hour records, etc.

And determining the characteristics corresponding to the task topics and the characteristics corresponding to the user topics by a public dimension data layer of the data warehouse according to the characteristic incidence relation. And acquiring task dimension data corresponding to the task theme from the plurality of service data according to the characteristics corresponding to the task theme. And acquiring user dimension data corresponding to the user theme from the plurality of service data according to the characteristics corresponding to the user theme. For example, a task topic may include 4 dimensions related to the task topic, namely, a page dimension, a task dimension, a batch dimension, and a project dimension, respectively. The hierarchy of page dimensions, task dimensions, batch dimensions, and project dimensions are sequentially increased. The user theme may include 4 user theme related dimensions, namely, a user dimension, a guild dimension, and an agent dimension, respectively. The hierarchy of user dimensions, guild dimensions, and agent dimensions increases in order. The task dimension may include task state, task type, task phase and participation type, and the like. The user dimension may include guild association information, agents, attribute information, time information, and the like.

The data summarization layer of the data warehouse can acquire fact data corresponding to the fact from a plurality of business data according to the characteristics corresponding to the fact. And associating the plurality of dimensions with the hierarchical relationship and related to the task subject with the plurality of dimensions with the hierarchical relationship and related to the user subject to obtain a plurality of associated dimensions with the hierarchical relationship. And based on a plurality of associated dimensions with hierarchical relationship, performing aggregation processing on the task dimension data, the user dimension data and the fact data to obtain at least one fact data set. For example, the associated dimensions may include user-task, user-batch, guild-task, guild-project, agent-project, task progress, project progress, and the like. In fig. 6, the part has "→" characteristic hierarchical relationship, "→" left side characteristic hierarchical low association dimension, "→" back side characteristic hierarchical high association dimension. The at least one factual data set may include user-task workload, task accuracy, user-task man-hours, guild-task workload, user-batch workload, task progress, guild-project workload, agent-project workload, project progress, and the like. Guild-task workload, user-batch workload, and task progress may be obtained based on user-task workload. Guild-project workloads and agent-project workloads may be derived from guild-task workloads. Project progress can be obtained according to task progress.

The data application layer of the data warehouse may determine at least one statistical indicator based on the business demand rules. And for each statistical index in the at least one statistical index, associating at least one fact data set corresponding to the statistical index to obtain at least one statistical data set corresponding to the statistical index. For example, the at least one set of statistical data may include an object capability label, an object activity within 7 days, … …, an object efficiency, an object average capacity, an object ergonomics, and the like.

The above is only an exemplary embodiment, but is not limited thereto, and other data processing methods known in the art may be included as long as more efficient data calculation can be achieved.

In the technical scheme of the present disclosure, the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the related users all conform to the regulations of the related laws and regulations, and do not violate the good custom of the public order.

Fig. 7 schematically shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure.

As shown in fig. 7, the data processing apparatus 700 may include an obtaining module 710, a first obtaining module 720, and a second obtaining module 730.

The obtaining module 710 is configured to obtain task dimension data corresponding to a task topic, user dimension data corresponding to a user topic, and fact data corresponding to a fact from a plurality of service data. The task dimension data represents business data corresponding to the dimension related to the task theme, and the user dimension data represents business data corresponding to the dimension related to the user theme.

A first obtaining module 720, configured to obtain at least one fact data set according to the task dimension data, the user dimension data, and the fact data based on the service hierarchy relationship. The business hierarchy relationship represents the hierarchy relationship of the task theme and the hierarchy relationship of the user theme.

The second obtaining module 730 is configured to obtain at least one statistical data set corresponding to each statistical indicator of the at least one statistical indicator according to the at least one fact data set.

According to an embodiment of the present disclosure, the data processing apparatus 700 may further include a third obtaining module.

And the third obtaining module is used for carrying out data cleaning on the plurality of original service data to obtain a plurality of service data.

According to an embodiment of the present disclosure, the data processing apparatus 700 may further include a first determining module and a storing module.

The data structure specification module is used for specifying a data structure specification of the data warehouse and a data structure of each data source in the plurality of data sources.

And the storage module is used for storing the original service data to the data warehouse according to the storage strategy of the original service data corresponding to each data source.

According to an embodiment of the present disclosure, a storage module may include a first determination unit and a storage unit.

And the first determining unit is used for determining a data import tool matched with the storage strategy according to the storage strategy of the original service data corresponding to each data source.

And the storage unit is used for storing the original service data to the data warehouse by using the data import tool matched with the storage strategy.

According to an embodiment of the present disclosure, the acquisition module may include a second determination unit, a first acquisition unit, a second acquisition unit, and a third acquisition unit.

And the second determining unit is used for determining the characteristics corresponding to the task theme, the characteristics corresponding to the user theme and the characteristics corresponding to the fact according to the characteristic association relation.

The first obtaining unit is used for obtaining task dimension data corresponding to the task theme from the plurality of service data according to the characteristics corresponding to the task theme.

And the second acquisition unit is used for acquiring user dimension data corresponding to the user theme from the plurality of service data according to the characteristics corresponding to the user theme.

And a third obtaining unit configured to obtain fact data corresponding to the fact from the plurality of service data according to the feature corresponding to the fact.

According to the embodiment of the disclosure, the task topic comprises a plurality of dimensions which have a hierarchical relationship and are related to the task topic, and the user comprises a plurality of dimensions which have a hierarchical relationship and are related to the user topic;

the first obtaining module 720 may include a first obtaining unit and a second obtaining unit.

The first obtaining unit is used for associating the plurality of dimensionalities which have the hierarchical relationship and are related to the task theme with the plurality of dimensionalities which have the hierarchical relationship and are related to the user theme to obtain a plurality of associated dimensionalities which have the hierarchical relationship.

And the second obtaining unit is used for carrying out aggregation processing on the task dimensional data, the user dimensional data and the fact data based on a plurality of associated dimensions with hierarchical relations to obtain at least one fact data set.

According to an embodiment of the present disclosure, the second obtaining module may include a third determining unit and a third obtaining unit.

And the third determining unit is used for determining at least one statistical index according to the business demand rule.

And a third obtaining unit, configured to associate, for each statistical index of the at least one statistical index, at least one fact data set corresponding to the statistical index, so as to obtain at least one statistical data set corresponding to the statistical index.

According to an embodiment of the present disclosure, the data processing apparatus 700 may further include a second determining module.

And the second determining module is used for responding to the query request, and determining a data query result matched with the query condition from at least one statistical data set corresponding to each statistical index in at least one statistical index according to the query condition included in the query request.

According to an embodiment of the present disclosure, the second determination module may include a calling unit and a fourth determination unit.

And the calling unit is used for responding to the query request and calling the data warehouse interface.

And the fourth determining unit is used for determining a data query result matched with the query condition from at least one statistical data set corresponding to each statistical index in the at least one statistical index according to the query condition included in the query request by using the data warehouse interface.

According to an embodiment of the present disclosure, the data processing apparatus 700 may further include a fourth obtaining module.

And the fourth obtaining module is used for processing the data query result to obtain a data processing result.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.

According to an embodiment of the present disclosure, a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method as described above.

According to an embodiment of the disclosure, a computer program product comprising a computer program which, when executed by a processor, implements the method as described above.

Fig. 8 schematically shows a block diagram of an electronic device adapted to implement a data processing method according to an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 8, the electronic device 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic apparatus 800 can also be stored. The calculation unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.

A number of components in the electronic device 800 are connected to the I/O interface 805, including: an input unit 806, such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the electronic device 800 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.

Computing unit 801 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The calculation unit 801 executes the respective methods and processes described above, such as the data processing method. For example, in some embodiments, the data processing method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto device 800 via ROM 802 and/or communications unit 809. When loaded into RAM 803 and executed by the computing unit 801, a computer program may perform one or more steps of the data processing method described above. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the data processing method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method of data processing, comprising:

task dimension data corresponding to a task theme, user dimension data corresponding to a user theme and fact data corresponding to facts are obtained from a plurality of business data, wherein the task dimension data represent the business data corresponding to the dimension related to the task theme, and the user dimension data represent the business data corresponding to the dimension related to the user theme;

based on a business hierarchical relationship, obtaining at least one fact data set according to the task dimensional data, the user dimensional data and the fact data, wherein the business hierarchical relationship represents a hierarchical relationship of the task subject and a hierarchical relationship of the user subject; and

and obtaining at least one statistical data set corresponding to each statistical index in at least one statistical index according to the at least one fact data set.

2. The method of claim 1, further comprising:

and carrying out data cleaning on a plurality of original service data to obtain a plurality of service data.

3. The method of claim 2, further comprising:

determining a storage strategy of original business data corresponding to each data source based on the data structure specification of the data warehouse and the data structure of each data source in the plurality of data sources; and

and storing the original service data to the data warehouse according to the storage strategy of the original service data corresponding to each data source.

4. The method of claim 3, wherein the storing the raw business data to the data repository according to a storage policy of raw business data corresponding to each data source comprises:

determining a data import tool matched with the storage strategy according to the storage strategy of the original service data corresponding to each data source; and

and storing the original business data to the data warehouse by using a data import tool matched with the storage strategy.

5. The method according to any one of claims 1 to 4, wherein the acquiring task dimension data corresponding to a task topic, user dimension data corresponding to a user topic and fact data corresponding to a fact from a plurality of business data comprises:

determining a feature corresponding to the task theme, a feature corresponding to the user theme and a feature corresponding to the fact according to the feature association relation;

according to the characteristics corresponding to the task theme, task dimension data corresponding to the task theme are obtained from the plurality of service data;

according to the characteristics corresponding to the user theme, user dimension data corresponding to the user theme are obtained from the plurality of service data; and

and acquiring fact data corresponding to the fact from the plurality of service data according to the characteristics corresponding to the fact.

6. The method according to any one of claims 1-5, wherein the task topic comprises a plurality of dimensions related to the task topic in a hierarchical relationship, and the user comprises a plurality of dimensions related to the user topic in a hierarchical relationship;

the obtaining at least one fact data set according to the task dimension data, the user dimension data and the fact data based on the business hierarchy relationship includes:

associating the plurality of dimensions with hierarchical relationship and related to the task subject with the plurality of dimensions with hierarchical relationship and related to the user subject to obtain a plurality of associated dimensions with hierarchical relationship; and

and performing aggregation processing on the task dimension data, the user dimension data and the fact data based on the plurality of associated dimensions with the hierarchical relationship to obtain the at least one fact data set.

7. The method according to any one of claims 1 to 6, wherein the obtaining at least one statistical data set corresponding to each statistical indicator of at least one statistical indicator according to the at least one fact data set comprises:

determining the at least one statistical indicator according to a business requirement rule; and

and for each statistical index in the at least one statistical index, associating at least one fact data set corresponding to the statistical index to obtain at least one statistical data set corresponding to the statistical index.

8. The method of any of claims 1-7, further comprising:

and responding to a query request, and determining a data query result matched with the query condition from at least one statistical data set corresponding to each statistical index in the at least one statistical index according to the query condition included in the query request.

9. The method of claim 8, wherein the determining, in response to a query request, a data query result matching the query condition from at least one statistical data set corresponding to each of the at least one statistical indicator according to the query condition included in the query request comprises:

invoking a data warehouse interface in response to the query request; and

and determining a data query result matched with the query condition from at least one statistical data set corresponding to each statistical index in the at least one statistical index according to the query condition included in the query request by using the data warehouse interface.

10. The method of claim 8 or 9, further comprising:

and processing the data query result to obtain a data processing result.

11. A data processing apparatus comprising:

the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring task dimension data corresponding to a task theme, user dimension data corresponding to a user theme and fact data corresponding to a fact from a plurality of service data, the task dimension data represents the service data corresponding to the dimension related to the task theme, and the user dimension data represents the service data corresponding to the dimension related to the user theme;

a first obtaining module, configured to obtain at least one fact data set according to the task dimension data, the user dimension data, and the fact data based on a business hierarchy relationship, where the business hierarchy relationship represents a hierarchy relationship of the task topic and a hierarchy relationship of the user topic; and

and the second obtaining module is used for obtaining at least one statistical data set corresponding to each statistical index in at least one statistical index according to the at least one fact data set.

12. The apparatus of claim 11, further comprising:

and the third obtaining module is used for carrying out data cleaning on the plurality of original service data to obtain the plurality of service data.

13. The apparatus of claim 12, further comprising:

the data structure specification module is used for specifying a data structure specification of a data warehouse and a data structure of each data source in a plurality of data sources; and

14. The apparatus of claim 13, wherein the storage module comprises:

the data import tool comprises a first determining unit, a second determining unit and a data import tool, wherein the first determining unit is used for determining a data import tool matched with a storage strategy according to the storage strategy of original service data corresponding to each data source; and

and the storage unit is used for storing the original service data to the data warehouse by using a data import tool matched with the storage strategy.

15. The apparatus of any one of claims 11-14, wherein the acquisition module comprises:

the second determining unit is used for determining the characteristics corresponding to the task theme, the characteristics corresponding to the user theme and the characteristics corresponding to the facts according to the characteristic association relation;

the first acquisition unit is used for acquiring task dimensional data corresponding to the task theme from the plurality of service data according to the characteristics corresponding to the task theme;

a second obtaining unit, configured to obtain, according to a feature corresponding to the user theme, user dimension data corresponding to the user theme from the multiple pieces of service data; and

a third obtaining unit, configured to obtain fact data corresponding to the fact from the plurality of service data according to a feature corresponding to the fact.

16. The device according to any one of claims 11-15, wherein the task subject comprises a plurality of dimensions related to the task subject in a hierarchical relationship, and the user comprises a plurality of dimensions related to the user subject in a hierarchical relationship;

the first obtaining module includes:

the first obtaining unit is used for associating the plurality of dimensions with the hierarchical relationship and related to the task subject with the plurality of dimensions with the hierarchical relationship and related to the user subject to obtain a plurality of associated dimensions with the hierarchical relationship; and

a second obtaining unit, configured to perform aggregation processing on the task dimension data, the user dimension data, and the fact data based on the multiple associated dimensions with the hierarchical relationship, so as to obtain the at least one fact data set.

17. The apparatus of any of claims 11-16, wherein the second obtaining module comprises:

a third determining unit, configured to determine, according to a service demand rule, the at least one statistical indicator: and

a third obtaining unit, configured to associate, for each statistical indicator of the at least one statistical indicator, at least one fact data set corresponding to the statistical indicator, so as to obtain at least one statistical data set corresponding to the statistical indicator.

18. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

19. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any of claims 1-10.

20. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 10.