CN116127143A

CN116127143A - Data query method, device, electronic equipment and readable storage medium

Info

Publication number: CN116127143A
Application number: CN202310125449.XA
Authority: CN
Inventors: 李锐
Original assignee: Hangzhou Hikvision System Technology Co Ltd
Current assignee: Hangzhou Hikvision System Technology Co Ltd
Priority date: 2023-02-03
Filing date: 2023-02-03
Publication date: 2023-05-16

Abstract

The application discloses a data query method, a data query device, electronic equipment and a readable storage medium, wherein the method comprises the following steps: after receiving at least one semantic query statement, obtaining mapping information of a metadata map body, wherein the mapping information comprises a mapping relation between map attribute classes used for describing a target data source data structure in the metadata map body and configuration file information of a storage file in the target data source; inquiring to obtain configuration file information corresponding to the semantic query statement according to the mapping relation and the map attribute class corresponding to the semantic query statement; extracting a target storage file corresponding to the configuration file information from a target data source; converting the target storage file into a target data set based on the metadata map body; and carrying out semantic computation on the target data set to obtain a query result corresponding to the semantic query statement. The method and the device solve the technical problems that the quantity of the data islands in the prior art is increased greatly, the management workload of data is overlarge, and the data management efficiency is reduced.

Description

Data query method, device, electronic equipment and readable storage medium

Technical Field

The present disclosure relates to the field of computer information technologies, and in particular, to a data query method, a data query device, an electronic device, and a readable storage medium.

Background

Along with the continuous advancement of informatization, in the process of the convergence of information technology and production and life, data has the characteristics of burst growth and mass aggregation. Therefore, the number of data islands of an enterprise organization is greatly increased, most data and analysis teams have technical liabilities due to the rising integration difficulty, namely, a series of problems caused by different types of data of various storage systems distributed in a hybrid cloud/multi-cloud environment (or different data centers) and the continuous increase of the diversity, the dispersity, the scale and the complexity of data assets, so that the management workload of the data is overlarge, and the data management efficiency is reduced.

Disclosure of Invention

The main purpose of the present application is to provide a data query method, a device, an electronic device and a computer readable storage medium, which aim to solve the technical problems that in the prior art, the number of data islands is increased greatly, resulting in overlarge workload for data management and reduced data management efficiency.

In order to achieve the above object, the present application provides a data query method, including:

After receiving at least one semantic query statement, obtaining mapping information of a metadata map body, wherein the mapping information comprises a mapping relation between map attribute classes used for describing a target data source data structure in the metadata map body and configuration file information of a storage file in a target data source;

inquiring to obtain configuration file information corresponding to the semantic query statement according to the mapping relation and the map attribute class corresponding to the semantic query statement;

extracting a target storage file corresponding to the configuration file information from the target data source;

converting the target storage file into a target data set based on the metadata map body;

and carrying out semantic computation on the target data set to obtain a query result corresponding to the semantic query statement.

Optionally, the step of querying to obtain the configuration file information corresponding to the semantic query statement according to the mapping relationship and the map attribute class corresponding to the semantic query statement includes:

analyzing the semantic query statement to obtain a map attribute class corresponding to the semantic query statement;

and inquiring the mapping relation based on the map attribute class to obtain configuration file information corresponding to the map attribute class.

Optionally, the step of extracting the target storage file corresponding to the configuration file information from the target data source includes:

acquiring resource identification information of a target storage file according to the configuration file information;

and extracting a corresponding target storage file from the target data source according to the resource identification information.

Optionally, the step of converting the target storage file into a target data set based on the metadata map body includes:

converting the target storage file into triple structure data based on the metadata map body;

and mapping the triple structure data to an elastic distributed data set to obtain a target data set.

Optionally, the step of performing semantic computation on the target data set to obtain a query result corresponding to the semantic query statement includes:

carrying out structuring treatment on the semantic query statement to obtain a structured query language corresponding to a preset distributed computing engine;

based on the structured query language, converting the target data set through a preset distributed computing engine to obtain a result data set corresponding to the semantic query statement;

And generating a corresponding query result according to the result data set.

Optionally, before the step of acquiring the mapping information of the metadata atlas body after receiving at least one semantic query statement, the data query method further comprises:

acquiring metadata information of a target data source and configuration file information of a storage file in the target data source;

abstracting the metadata information to obtain a metadata map body corresponding to the target data source, wherein the metadata map body comprises a map attribute class for describing a data structure of the target data source;

and determining the mapping information of the metadata map body according to the map attribute class and the configuration file information.

Optionally, the step of abstracting the metadata information to obtain a metadata map body corresponding to the target data source includes:

acquiring data attribute classes in at least one data structure body of the target data source and attributes and relations among the data attribute classes according to the metadata information;

generating a corresponding map attribute class according to the data attribute class;

according to the attribute and the relation between the data attribute classes, connecting the map attribute classes to form a map body corresponding to the data structure body;

And generating a metadata map body corresponding to the target data source according to the map body.

Optionally, the profile information includes entity attributes of the storage file, and the step of determining mapping information of the metadata map body according to the map attribute class and the profile information includes;

matching the map attribute class with the entity attribute of the storage file to obtain a matching result;

and generating mapping information of the metadata map body according to the matching result.

The application also provides a data query device, which is applied to data query equipment, and comprises:

the acquisition module is used for acquiring mapping information of a metadata map body after receiving at least one semantic query statement, wherein the mapping information comprises a mapping relation between map attribute types used for describing a data structure of a target data source in the metadata map body and configuration file information of a storage file in the target data source;

the query module is used for querying to obtain configuration file information corresponding to the semantic query statement according to the mapping relation and the map attribute class corresponding to the semantic query statement;

The extraction module is used for extracting a target storage file corresponding to the configuration file information from the target data source;

the conversion module is used for converting the target storage file into a target data set based on the metadata map body;

and the calculating module is used for carrying out semantic calculation on the target data set to obtain a query result corresponding to the semantic query statement.

Optionally, the query module is further configured to:

Optionally, the extracting module is further configured to:

Optionally, the conversion module is further configured to:

Optionally, the computing module is further configured to:

and generating a corresponding query result according to the result data set.

Optionally, the data query device further includes a map body construction module, and the map body construction module is configured to:

Optionally, the atlas ontology construction module is further configured to:

The application also provides an electronic device, which is an entity device, and includes: the data query method comprises a memory, a processor and a program of the data query method stored in the memory and capable of running on the processor, wherein the program of the data query method can realize the steps of the data query method when being executed by the processor.

The present application also provides a computer-readable storage medium having stored thereon a program for implementing a data query method, which when executed by a processor implements the steps of the data query method as described above.

The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of a data query method as described above.

The application provides a data query method, a data query device, electronic equipment and a readable storage medium, wherein after receiving at least one semantic query statement, mapping information of a metadata map body is obtained, and the mapping information comprises a mapping relation between map attribute types used for describing a target data source data structure in the metadata map body and configuration file information of storage files in a target data source. Therefore, the description of the data structure of the target data source is carried out through the map attribute class in the metadata map body, so that the universality of the metadata map body among the target data sources is realized, the description of the target data source still can be carried out through the metadata map body, the difference of metadata among storage and computing systems of different data analysis technology stacks is shielded, various metadata knowledge maps are not required to be constructed, and the construction workload and efficiency of the metadata knowledge maps are effectively improved. In addition, the mapping relation between the metadata map body and the target data source is established as the mapping information of the metadata map body by adopting the map attribute type of the metadata map body and the configuration file information in the target data source for mapping, so that on one hand, the corresponding storage file can be extracted from the target data source through the mapping information, metadata information of the target data source is not required to be subjected to metadata information redundancy of the target data source, and the defect that the metadata map body is consistent with the changed metadata information can be overcome. Compared with the prior art that the metadata map needs to be reconstructed when the metadata information is changed, otherwise, the defect that the previously constructed metadata map is inconsistent with the changed metadata information is caused. Further, the application queries to obtain configuration file information corresponding to the semantic query statement according to the mapping relation and the map attribute class corresponding to the semantic query statement; extracting a target storage file corresponding to the configuration file information from the target data source; converting the target storage file into a target data set based on the metadata map body; and carrying out semantic computation on the target data set to obtain a query result corresponding to the semantic query statement. Therefore, the efficiency of searching and extracting files in the target data source is effectively improved. The method and the device effectively reduce the workload of metadata map body construction and maintenance, so that the data query is performed on the target data source based on the metadata map body and the mapping information, and the efficiency of searching and extracting the files in the target data source is improved. Therefore, the management work of the data integration infrastructure, namely the knowledge graph of the enterprise organization, can be simplified, the management workload of the data is reduced, and the data management efficiency is improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 is a flowchart of a first embodiment of a data query method according to the present application;

FIG. 2 is a query scenario diagram of a first embodiment of a data query method of the present application;

FIG. 3 is a flowchart of a second embodiment of a data query method according to the present application;

FIG. 4 is an overview of metadata schema ontology in the data query method of the present application;

FIG. 5 is a schematic diagram of a data directory in the metadata schema body of FIG. 4;

fig. 6 is a schematic device structure diagram of a hardware running environment related to a data query method in an embodiment of the present application.

The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed Description

In order to make the above objects, features and advantages of the present application more comprehensible, the following description will make the technical solutions of the embodiments of the present application clear and complete with reference to the accompanying drawings of the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, based on the embodiments herein, which are within the scope of the protection of the present application, will be within the purview of one of ordinary skill in the art without the exercise of inventive faculty.

An embodiment of the present application provides a data query method, and in an embodiment of the data query method of the present application, referring to fig. 1, the data query method includes:

step S10, after receiving at least one semantic query statement, mapping information of a metadata map body is obtained, wherein the mapping information comprises mapping relations between map attribute classes used for describing a data structure of a target data source in the metadata map body and configuration file information of a storage file in the target data source;

step S20, inquiring to obtain configuration file information corresponding to the semantic query statement according to the mapping relation and the map attribute class corresponding to the semantic query statement;

Step S30, extracting a target storage file corresponding to the configuration file information from the target data source;

step S40, converting the target storage file into a target data set based on the metadata map body;

and S50, carrying out semantic computation on the target data set to obtain a query result corresponding to the semantic query statement.

In this embodiment, it should be noted that the target data source may be at least one data analysis technology stack, and the data analysis technology stack may be a data warehouse, a data lake, a lake-warehouse, or the like.

In addition, it should be further noted that the metadata map body may be stored in a storage form of an attribute map or an RDF (Resource Description-Framework) map, which is also called a triplet map. The metadata map body comprises a data structure body for describing a target data source and a map data class of data attribute classes forming the data structure body, namely the metadata map body is used for describing the data structure of the target data source. The metadata map body is composed of at least one map body, and the map body corresponds to the data structure body of the target data source. The data structure body comprises data attribute classes, the map body comprises map attribute classes, the connection relation between the map attribute classes is determined by the attribute and relation between the data attribute classes of the target data source, and the map attribute classes correspond to the data attribute classes of the target data source.

As an example, steps S10 to S50 include: after receiving at least one semantic query statement, mapping information of a metadata map body is obtained, wherein the mapping information comprises a mapping relation between map attribute classes used for describing a target data source data structure in the metadata map body and configuration file information of a storage file in a target data source. It can be understood that the metadata map body may be configured by using a map attribute class as a node, determining a connection relationship between the map attribute classes according to an attribute and a relationship between the data attribute classes of the target data source, and further connecting the nodes corresponding to the map attribute classes based on the connection relationship to construct a map body of the metadata map body, where the number and types of the map bodies correspond to the data structure body of the target data source. For example, if the data structure of the target data source includes eight types of data structure bodies including data catalogue, data knowledge, data management, data operation, data quality, data change, service data definition and data support, and the body further includes each attribute type and attribute and relationship between attribute types, the corresponding metadata map body may also be composed of eight map bodies including data catalogue, data knowledge, data management, data operation, data quality, data change, service data definition and data support. Because the mapping information includes a mapping relationship between the profile attribute class and profile information for the stored file in the target data source. The configuration file information may include configuration information of the storage file, such as information of a data source type, a data source URL (Uniform Resource Locator, uniform resource locator system), a database name, a data table name, a user name, a password, and the like of the storage file. Therefore, the application queries the configuration file information corresponding to the semantic query statement according to the mapping relation and the map attribute class corresponding to the semantic query statement. Therefore, the target storage file corresponding to the configuration file information can be extracted from the target data source through the configuration file information. And further converting the target storage file into a target data set based on the metadata map body. The target data set is a data set for semantic computation, and because the storage file in the database is original data, the data set cannot be directly used for semantic computation in general, the target storage file needs to be converted into triple structure data through the metadata map body, and then data encapsulation is carried out to obtain the target data set corresponding to the target storage file. The data packaging mode adopted by the target data set is determined according to a calculation engine adopted by semantic calculation. For example, using a distributed computing engine, the target storage file may be packaged as an RDD (Resilient Distributed Datasets, resilient distributed data set) resulting in a target data set. And carrying out semantic computation on the target data set through a distributed computation engine to obtain a query result corresponding to the semantic query statement.

In another embodiment, after the query result is obtained, if an analysis instruction for the query result is received, matching a corresponding analysis algorithm according to the type of the analysis instruction; and analyzing the query result based on the analysis algorithm to obtain an analysis result corresponding to the query result. Wherein the types of analysis instructions include basic traversals, path searches, associative predictions, and the like. Different types of analysis instructions require corresponding analysis algorithms to implement. For example, the type of the analysis instruction is associated prediction, and the corresponding analysis algorithm may be a LinkPrediction algorithm; the type of the analysis instruction is path search, and the corresponding analysis algorithm may be a Dijkstra algorithm, an APSP (All Pairs Shortest Path, all-junction-pair shortest path) path algorithm, or the like. It will be appreciated that the analysis algorithm is not limited to a graph algorithm but may also be a graph deep learning or machine learning algorithm model. For example, a semantic query statement is used for querying 'which friends of Zhang three friends are', and a query result RDD-result (including Lifour, wang five and the like) is obtained. If an analysis instruction of what relationship is between Li IV and Wang V aiming at the query result is received, the type of the analysis instruction can be determined to be associated prediction, and a corresponding analysis algorithm LinkPreaction algorithm (or other connection prediction algorithms) is called to analyze the query result, so that the relationship of Li IV and Wang V is the classics relationship or other relationships can be obtained.

Wherein, the step of querying to obtain the configuration file information corresponding to the semantic query statement according to the mapping relation and the map attribute class corresponding to the semantic query statement comprises the following steps:

s21, analyzing the semantic query statement to obtain a map attribute class corresponding to the semantic query statement;

and S22, inquiring the mapping relation based on the map attribute class to obtain configuration file information corresponding to the map attribute class.

In this embodiment, it should be noted that, the semantic query statement is an instruction for performing semantic computation on a target storage file in the target data source, and it may be understood that, after the semantic query statement is parsed by the semantic parsing service, the profile attribute class of the target storage file that needs to perform semantic computation in the target data source may be determined.

It should be further noted that the target configuration file information includes target resource identification information, where the target resource identification information is used to identify a location where the file is stored, such as a data source type, a data source URL, a database name, a data table name, and the like.

As an example, steps S21 to S22 include: and analyzing the semantic query statement, and analyzing the semantic query statement to obtain a map attribute class corresponding to the semantic query statement. And inquiring the mapping information based on the map attribute class so as to obtain configuration file information corresponding to the map attribute class. According to the method and the device, mapping information of the metadata map body is queried through the semantic query statement, so that configuration file information of the target storage file is obtained based on the map attribute class corresponding to the semantic query statement, and file searching efficiency is effectively improved.

The step of extracting the target storage file corresponding to the configuration file information from the target data source comprises the following steps:

step S31, acquiring resource identification information of a target storage file according to the configuration file information;

and step S32, extracting a corresponding target storage file from the target data source according to the resource identification information.

As an example, steps S31 to S32 include: after the configuration file information corresponding to the map attribute class is obtained, the position of the target storage file can be determined according to the target resource identification information in the target configuration file information, and then the target storage file corresponding to the resource identification information is extracted from the target data source through a Native interface (Native API). According to the embodiment, the resource identification information of the target storage file is obtained through the configuration file information so as to extract the target storage file from the target data source, and the file extraction efficiency is effectively improved.

Wherein the step of converting the target storage file into a target data set based on the metadata map ontology comprises:

step S41, converting the target storage file into triple structure data based on the metadata map body;

And step S42, mapping the triplet structure data to an elastic distributed data set to obtain a target data set.

In this embodiment, the triplet structure data is a structure of two nodes and a relationship between them, that is, (node 1, relationship, node 2). Such as: (Zhang three, nationality, china).

It will be appreciated that prior to step S41, the metadata map body and the metadata information may be stored in a distributively expandable data structure for facilitating subsequent distributed computations.

As an example, step S41 to step S42 include: and converting the target storage file into triple structure data based on the metadata map body so as to facilitate subsequent semantic computation. And mapping the triple structure data to an elastic distributed data set to obtain a target data set corresponding to the semantic query statement. Therefore, semantic computation can be carried out on the target data set through the distributed computation engine, the advantages of the distributed computation are fully utilized, the query performance of the large-scale graph is improved, and the query efficiency is improved.

The step of performing semantic computation on the target data set to obtain a query result corresponding to the semantic query statement includes:

Step S51, carrying out structuring treatment on the semantic query statement to obtain a structured query language corresponding to a preset distributed computing engine;

step S52, converting the target data set through a preset distributed computing engine based on the structured query language to obtain a result data set corresponding to the semantic query statement;

step S53, corresponding query results are generated according to the result data set.

In this embodiment, it should be noted that the preset distributed computing engine may be spark, flink, presto or the like. It will be appreciated that the transformation operations are used to create RDD, which can only be created using transformation operations, while a number of methods of operation are provided, including map, filter, groupBy, join, etc., with which RDD generates new RDD (i.e., RDD-t), and final iterative computation results in RDD-result.

As an example, steps S51 to S53 include: because the distributed computing engine cannot directly identify the semantic query statement, the semantic query statement can be subjected to structuring processing to obtain a structured query language corresponding to the preset distributed computing engine. For example: the semantic query statement is a SPARQL (Protocol and RDF Query Language ) that is converted to SQL (Structured Query Language, structured query language) of the preset distributed computing engine. Therefore, the target data set can be converted through a preset distributed computing engine based on the structured query language, and a result data set corresponding to the semantic query statement can be obtained. The result data set may be directly output as a corresponding query result, or may be converted into a chart form according to the result data set and then used as a query result. Therefore, the method and the device can utilize the advantages of memory calculation and distributed calculation of the distributed calculation engine to improve the query performance of the large-scale graph.

As an example, fig. 2 is a query scene diagram of a first embodiment of the data query method of the present application. After at least one semantic query statement is received through a web page interface or a client interface, the semantic query statement can be parsed through a semantic parsing service to obtain a graph connected by circles, pentagrams, hexagons and heptagons on the right side in the graph, wherein the circles, pentagrams, hexagons and heptagons in the graph respectively refer to the graph attribute types of the target storage files in the target data source (namely the data analysis technical stack of the lowest RDBMS, noSQL, a data warehouse, a data lake and the like in FIG. 2). And further obtaining mapping information of the metadata map ontology, so that the adapter queries and obtains configuration file information corresponding to the semantic query statement and a target storage file corresponding to the configuration file information in the target data source based on the mapping information. The target storage file is then mapped into the elastic distributed data set through data encapsulation, encapsulated into target data sets RDD1, RDD2, RDD3 … …. And carrying out semantic computation on the target data set through a distributed computation engine, and sequentially obtaining corresponding RDD-t1, RDD-t2 and RDD-t3 after iterative computation of RDD1, RDD2 and RDD3 to finally obtain a query result RDD-result corresponding to the semantic query statement.

The embodiment of the application provides a data query method, after receiving at least one semantic query statement, mapping information of a metadata map body is obtained, wherein the mapping information comprises a mapping relation between map attribute types used for describing a target data source data structure in the metadata map body and configuration file information of a storage file in a target data source. Therefore, the description of the data structure of the target data source is carried out through the map attribute class in the metadata map body, so that the universality of the metadata map body among the target data sources is realized, the description of the target data source still can be carried out through the metadata map body, the difference of metadata among storage and computing systems of different data analysis technology stacks is shielded, various metadata knowledge maps are not required to be constructed, and the construction workload and efficiency of the metadata knowledge maps are effectively improved. In addition, the mapping relation between the metadata map body and the target data source is established as the mapping information of the metadata map body by adopting the map attribute type of the metadata map body and the configuration file information in the target data source for mapping, so that on one hand, the corresponding storage file can be extracted from the target data source through the mapping information, metadata information of the target data source is not required to be subjected to metadata information redundancy of the target data source, and the defect that the metadata map body is consistent with the changed metadata information can be overcome. Compared with the prior art that the metadata map needs to be reconstructed when the metadata information is changed, otherwise, the defect that the previously constructed metadata map is inconsistent with the changed metadata information is caused. Further, the application queries to obtain configuration file information corresponding to the semantic query statement according to the mapping relation and the map attribute class corresponding to the semantic query statement; extracting a target storage file corresponding to the configuration file information from the target data source; converting the target storage file into a target data set based on the metadata map body; and carrying out semantic computation on the target data set to obtain a query result corresponding to the semantic query statement. Therefore, the efficiency of searching and extracting files in the target data source is effectively improved. The method and the device effectively reduce the workload of metadata map body construction and maintenance, so that the data query is performed on the target data source based on the metadata map body and the mapping information, and the efficiency of searching and extracting the files in the target data source is improved. Therefore, the management work of the data integration infrastructure, namely the knowledge graph of the enterprise organization, can be simplified, the management workload of the data is reduced, and the data management efficiency is improved.

Further, referring to fig. 3, in another embodiment of the present application, the same or similar contents as those of the above embodiment may be referred to the above description, and will not be repeated. On this basis, before the step of acquiring the mapping information of the metadata atlas body after receiving at least one semantic query statement, the data query method further includes:

step A10, metadata information of a target data source and configuration file information of a storage file in the target data source are obtained;

step A20, abstracting the metadata information to obtain a metadata map body corresponding to the target data source, wherein the metadata map body comprises a map attribute class for describing a data structure of the target data source;

and step A30, determining the mapping information of the metadata map body according to the map attribute class and the configuration file information.

In this embodiment, it should be noted that the target data source may be at least one data analysis technology stack, and the data analysis technology stack may be a data warehouse, a data lake, a lake storehouse, or the like. The metadata information is data describing a data structure within the target data source. The data structure comprises a data structure body of the target data source, and the data structure body comprises data attribute classes and attributes and relations among the data attribute classes. Illustratively, the attributes corresponding to the data attribute class category are themetaxonomy, hasPart, dataset, service, catalog, record and the like. The attribute between the data attribute class category and the data attribute class ConceptSchema is the metaxonome, the attribute between the data attribute class category and the data attribute class Dataset is the Dataset, and the attribute between the data attribute class category and the data attribute class DataService is the service. An aggregate relationship exists between the data attribute class Dataset and the data attribute class Resource. An aggregate relationship also exists between the data attribute class DataService and the attribute class Resource.

As an example, steps a10 to a30 include: and determining a data structure of the target data source by acquiring metadata information of the target data source and configuration file information of a storage file in the target data source, wherein the data structure comprises a data structure body of the target data source and data attribute classes forming the data structure body. And abstracting the metadata information to obtain a data structure body corresponding to the target data source, and attributes and relations between data attribute classes forming the data structure body and the data attribute classes. Then generating a corresponding map attribute class according to the data attribute class; and connecting the map attribute classes according to the attribute and the relation among the data attribute classes to form a map body. And generating a metadata map body corresponding to the target data source according to the map body. The configuration file information comprises entity attributes of storage files in a target data source, namely which data attribute class the storage files belong to, and the data attribute class and the map attribute class have corresponding relations. Therefore, the mapping relation between the map attribute class and the configuration file information can be determined according to the mapping relation between the data attribute class and the configuration file information, and the mapping information of the metadata map body is obtained. Therefore, the metadata information of the target data source is abstracted to obtain the metadata map body for describing the data structure of the target data source, so that the difference of metadata between storage and computing systems in different data analysis technical stacks can be shielded, various metadata knowledge maps are not required to be constructed, and the data query efficiency is effectively improved.

As an example, referring to fig. 4, fig. 4 is an overview schematic diagram of a metadata map body in the data query method of the present application. The business application is an application desiring to perform data query, and the data structure body recorded in the metadata information of the target data source related to the business application comprises data knowledge, data catalogue, data change, data quality, data management, data operation, business data definition and data support as the eight data structure bodies. Thus, the eight schema bodies corresponding to the data knowledge, the data catalogue, the data change, the data quality, the data management, the data operation, the service data definition and the data support connected with the service application in fig. 4 together form the metadata schema body of the service application. Wherein the attribute between the business application and the data knowledge is "instantiation business type determination (Instantiates BusinessType)", the attribute between the data quality is "quality policy", the attribute between the data operation is "maintenance instance (Maintains instances of)", the attribute between the data directory is "weaving directory/data set/" DataService "device (hascatalyst/dataset/DataService)", the attribute between the business data definition is "coding map (Code mapping from)", the attribute between the data change is "influencing application (affectsApplication)", the attribute between the data administration is "applicable to (application to)", the attribute between the data support is "recording/common problem solution/training (Has Document/FAQ/trace). It will of course be appreciated that more or fewer profile bodies may be included in the metadata profile body.

As an example, referring to fig. 5, fig. 5 is a schematic diagram of a structure of a data directory in the metadata map body of fig. 4. The attribute between the map attribute class "category" and the map attribute class "Agent" in fig. 5 is "publisher"; the attribute between the map attribute class "Agent" and the map attribute class "Source" is "publisher or creator"; the attribute between the map attribute class "Source" and the map attribute class "Relationship" is qualified Relation and Relationship; the attribute between the map attribute class "catalyst" and the map attribute class "ConceptSchema" is the memeTaxonoom, the attribute between the map attribute class "ConceptSchema" and the map attribute class "Concept" is the inSchema, and the attribute between the map attribute class "Source" and the map attribute class "Concept" is the me. The attribute between the map attribute class 'catalyst' and the map attribute class 'catalyst record' is record, the attribute between the map attribute class 'catalyst record' and the map attribute class 'Source' is primary Topic, and the attribute between the map attribute class 'catalyst' and the map attribute class 'Source' is hasPart. The attribute between the map attribute class "catalyst" and the map attribute class "Dataset" is Dataset, the attribute between the map attribute class "Dataset" and the map attribute class "Datadistribution" is distribution, the attribute between the map attribute class "DataService" and the map attribute class "Catalog" is service, and the attribute between the map attribute class "DataDistribution" and the map attribute class "DataService" is accessService. The implementation relationship exists between the map attribute class "catalyst" and the map attribute class "Dataset", and the implementation relationship exists between the two parts of the map attribute class "DataService" and the map attribute class "Dataset" and the "Source".

The step of abstracting the metadata information to obtain the metadata map body corresponding to the target data source includes:

step B10, acquiring data attribute classes in at least one data structure body of the target data source and attributes and relations among the data attribute classes according to the metadata information;

step B20, generating a corresponding map attribute class according to the data attribute class;

step B30, connecting the map attribute classes according to the attribute and the relation among the data attribute classes to form a map body corresponding to the data structure body;

In this embodiment, it should be noted that the metadata information is used to describe a data structure of the target data source, where the data structure includes a data structure body, and attributes and relationships between data attribute classes that form the data structure body.

As an example, steps B10 to B40 include: according to the metadata information, the data attribute class in at least one data structure body of the target data source and the attribute and relationship among the data attribute classes can be obtained. Then, according to the data attribute class, generating a corresponding map attribute class; and determining the connection relation between the map attribute classes according to the attributes and the relation between the data attribute classes. And then respectively connecting the map attribute classes based on the connection relation to form a map body corresponding to the data structure body. And when the number of the data structure bodies is one, the corresponding map body is used as the metadata map body corresponding to the target data source. And when the number of the data structure bodies is greater than one, connecting the corresponding map bodies according to the attribute relationship among the data structure bodies to form the metadata map bodies corresponding to the target data sources. It is understood that the number and type of map ontologies may correspond to the data structure ontologies of the target data source. According to the metadata information, the data attribute class in at least one data structure body of the target data source and the attribute and relation among the data attribute classes are determined, so that a metadata map body corresponding to the data structure of the target data source is generated and is used for describing the data structure of the target data source universally.

Wherein the profile information comprises entity attributes of the storage file, and the step of determining mapping information of the metadata profile body according to the profile attribute class and the profile information comprises the steps of;

step C10, matching the map attribute class with the entity attribute of the storage file to obtain a matching result;

and step C20, generating mapping information of the metadata map body according to the matching result.

It should be noted that, the configuration file information includes an entity attribute of the storage file, where the entity attribute includes an entity and an attribute thereof, the entity is the storage file (such as a data table), and the attribute is used to describe a feature of the storage file. For example, the data table is a table in a data attribute class category of the target data source, and then the entity attribute of the storage file is matched with the data attribute class category.

As an example, steps C10 to C20 include: because the configuration file information comprises entity attributes of the storage file in the target data source, namely which data attribute class the storage file belongs to, and the map attribute class in the metadata map body characterizes the data attribute class in the target data source. And if the map attribute class is matched with the entity attribute of the storage file, determining the corresponding relation between the storage file and the data attribute class through the entity attribute of the storage file, thereby determining the corresponding relation (namely the matching result) between the storage file and the map attribute class. Therefore, according to the matching result, the mapping relation between the map attribute class in the metadata map body and the configuration file information can be determined, and the mapping relation is used as the mapping information of the metadata map body.

The embodiment of the application provides a construction method of a metadata map body, namely, a data structure body of a target data source and data attribute classes forming the data structure body are obtained by obtaining metadata information of the target data source and configuration file information of a storage file in the target data source; and abstracting the metadata information to obtain a metadata map body corresponding to the target data source, wherein the metadata map body comprises a map attribute class for describing a data structure of the target data source. Therefore, the metadata information of the target data source is abstracted to obtain the metadata map body for describing the data structure of the target data source, so that the metadata map body can be used for describing the target data source no matter how many different data analysis technical stacks the target data source comprises, the difference of metadata stored in different data analysis technical stacks and among computing systems is shielded, and therefore, various metadata knowledge maps are not required to be constructed, and the construction efficiency and the data query efficiency of the metadata knowledge maps are effectively improved. And further determining mapping information between the metadata map body and the target data source according to the map attribute class and the configuration file information. Therefore, even if the content in the storage file in the target data source is changed, a new metadata knowledge graph does not need to be reconstructed, and the workload of constructing and maintaining the metadata graph is effectively reduced.

The embodiment of the application also provides a data query device, which is applied to data query equipment, and comprises:

Optionally, the query module is further configured to:

Optionally, the extracting module is further configured to:

Optionally, the conversion module is further configured to:

Optionally, the computing module is further configured to:

and generating a corresponding query result according to the result data set.

Optionally, the atlas ontology construction module is further configured to:

The data query device provided by the application adopts the data query method in the embodiment, and solves the technical problem of low model inference accuracy of the object detection inference model. Compared with the prior art, the beneficial effects of the data query device provided by the embodiment of the present application are the same as those of the data query method provided by the above embodiment, and other technical features of the data query device are the same as those disclosed by the method of the above embodiment, which are not described in detail herein.

The embodiment of the application provides electronic equipment, the electronic equipment includes: 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, so that the at least one processor can execute the data query method in the first embodiment.

Referring now to fig. 6, a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.

As shown in fig. 6, the electronic device may include a processing means (e.g., a central processing unit, a graphic processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage means into a Random Access Memory (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM and RAM are connected to each other via a bus. An input/output (I/O) interface is also connected to the bus.

In general, the following systems may be connected to the I/O interface: input devices including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including, for example, liquid Crystal Displays (LCDs), speakers, vibrators, etc.; storage devices including, for example, magnetic tape, hard disk, etc.; a communication device. The communication means may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While electronic devices having various systems are shown in the figures, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from ROM. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by a processing device.

The electronic equipment provided by the application adopts the data query method in the embodiment, so that the technical problem of low model reasoning accuracy of the object detection reasoning model is solved. Compared with the prior art, the electronic device provided by the embodiment of the present application has the same beneficial effects as the data query method provided by the above embodiment, and other technical features in the electronic device are the same as the features disclosed by the method of the above embodiment, which are not described in detail herein.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.

The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

The present embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon for performing the method of the data query method in the first embodiment described above.

The computer readable storage medium provided by the embodiments of the present application may be, for example, a usb disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared system or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having 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. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

The above-described computer-readable storage medium may be contained in an electronic device; or may exist alone without being assembled into an electronic device.

The computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to: after receiving at least one semantic query statement, obtaining mapping information of a metadata map body, wherein the mapping information comprises a mapping relation between map attribute classes used for describing a target data source data structure in the metadata map body and configuration file information of a storage file in a target data source; inquiring to obtain configuration file information corresponding to the semantic query statement according to the mapping relation and the map attribute class corresponding to the semantic query statement; converting the target storage file into a target data set based on the metadata map body; and carrying out semantic computation on the target data set to obtain a query result corresponding to the semantic query statement.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present disclosure may be implemented in software or hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.

The computer readable storage medium provided by the application is stored with the computer readable program instructions for executing the data query method, and solves the technical problem of low model reasoning accuracy of the object detection reasoning model. Compared with the prior art, the beneficial effects of the computer readable storage medium provided in the embodiment of the present application are the same as those of the data query method provided in the above embodiment, and are not described herein.

The computer program product provided by the application solves the technical problem of low model reasoning accuracy of the object detection reasoning model. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as those of the data query method provided by the above embodiment, and are not described herein.

The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the present application, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields, are included in the scope of the present application.

Claims

1. A data query method, the data query method comprising:

2. The data query method as claimed in claim 1, wherein said step of querying to obtain profile information corresponding to said semantic query statement according to said mapping relationship and a profile attribute class corresponding to said semantic query statement comprises:

3. The data query method of claim 1, wherein the step of extracting the target storage file corresponding to the profile information from the target data source comprises:

4. The data query method of claim 1, wherein the step of converting the target storage file into a target data set based on the metadata schema body comprises:

5. The data query method of claim 4, wherein the step of performing semantic computation on the target data set to obtain a query result corresponding to the semantic query statement comprises:

and generating a corresponding query result according to the result data set.

6. The data query method of claim 1, wherein, prior to the step of obtaining mapping information for a metadata schema ontology after receiving at least one semantic query statement, the data query method further comprises:

7. The data query method of claim 6, wherein the step of abstracting the metadata information to obtain a metadata schema body corresponding to the target data source comprises:

8. The data query method of claim 6, wherein the profile information includes entity attributes of the stored file, and wherein the step of determining mapping information of the metadata schema body based on the schema attribute class and the profile information includes;

9. A data query device, the data query device comprising:

10. An electronic device, the electronic device comprising:

at least one processor; the method comprises the steps of,

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 steps of the data query method of any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for realizing a data query method, the program for realizing the data query method being executed by a processor to realize the steps of the data query method according to any one of claims 1 to 8.