CN113590660A - Data self-service query method and device and electronic equipment - Google Patents
Data self-service query method and device and electronic equipment Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24553—Query execution of query operations
- G06F16/24554—Unary operations; Data partitioning operations
- G06F16/24556—Aggregation; Duplicate elimination
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
Abstract
The embodiment of the specification discloses a data self-service query method and device and electronic equipment. The method comprises the following steps: connecting a plurality of data sources, and establishing a data warehouse according to data in the data sources; integrating data in a data warehouse to obtain multi-dimensional effective data; receiving query information of a user; and recombining the multidimensional data in the data warehouse according to the query information, and displaying the recombined query result. The method and the device can effectively reduce the workload of developers and operation and maintenance personnel, reduce the development and operation and maintenance cost, can also widen the query range of users, are not limited by a single system any more, and can perform any dimension query according to the requirements of the users, thereby improving the user experience.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for data self-service query, and an electronic device.
Background
The existing data query is completed manually, that is, a developer firstly obtains the query requirement of a user and establishes an index relation according to the query requirement of the user so that the user can complete a query task according to the index relation, but due to different requirements of different users, the customized development requirement is large in workload, long in delivery cycle, high in resource consumption and the like, and the development efficiency is influenced. Moreover, the user can only inquire data in the current system, for example, only office and human data can be inquired in an OA system, and only financial data can be inquired in a financial system, and data to be inquired by the user often relates to a plurality of systems. On the other hand, if the user does not put forward the requirement in advance and wants to query a certain dimension, the query result cannot be obtained, and the user experience is affected.
Disclosure of Invention
The embodiment of the specification provides a data self-service query method, a data self-service query device and electronic equipment, and is used for solving the following technical problems: in the existing data query method, developers receive customized development requirements, so that development efficiency is influenced by the large workload of the developers, long lead cycle, large resource consumption and the like, and the problem that user experience is influenced by the limited data query range of users and incapability of realizing any dimensionality query is caused.
In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:
the data self-service query method provided by the embodiment of the specification comprises the following steps:
connecting a plurality of data sources, and establishing a data warehouse according to data in the data sources;
integrating the data in the data warehouse to obtain multi-dimensional effective data;
receiving query information of a user;
and recombining the multidimensional data in the data warehouse according to the query information, and displaying the recombined query result.
Optionally, the integrating the data in the data warehouse to obtain the multidimensional valid data includes:
and performing one or more operations of filtering, screening, linkage, drilling down and aggregation on the data in the data warehouse to obtain multi-dimensional effective data.
Optionally, the method further comprises:
packaging the multidimensional valid data to generate a data query module;
and displaying the data query module on a user query interface.
Optionally, the receiving query information of the user includes:
receiving query information input by a user;
or the like, or, alternatively,
receiving the data query module clicked by a user on a user query interface;
or the like, or, alternatively,
and receiving the data query module dragged on the user query interface by the user.
Optionally, after receiving query information of the user, the method further includes:
judging user authority, and screening the multidimensional data in the data warehouse according to the user authority;
and recombining the screened multidimensional data, and displaying the recombined query result to the user.
Optionally, the displaying the re-combined query result includes:
and displaying the query result after the recombination through characters, or displaying the query result after the recombination through a visual chart.
Optionally, the recombining the multidimensional data in the data warehouse according to the query information includes:
analyzing the query information to obtain a dimension identifier of the user query information;
searching corresponding data information in the data warehouse according to the dimension identification;
and sequencing and combining the searched corresponding data information according to a preset rule.
Optionally, the data source comprises:
at least two of an ERP data source, an OA data source, a CRM data source, and a financial data source.
An embodiment of this specification provides a data self-service inquiry unit, includes:
the connection module is used for connecting a plurality of data sources and establishing a data warehouse according to data in the data sources;
the integration module is used for integrating the data in the data warehouse to obtain multi-dimensional effective data;
the receiving module is used for receiving query information of a user;
and the combined display module is used for recombining the multidimensional data in the data warehouse according to the query information and displaying the query result after recombination.
An embodiment of the present specification provides an electronic device, including:
at least one processor; and the number of the first and second groups,
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:
connecting a plurality of data sources, and establishing a data warehouse according to data in the data sources;
integrating the data in the data warehouse to obtain multi-dimensional effective data;
receiving query information of a user;
and recombining the multidimensional data in the data warehouse according to the query information, and displaying the recombined query result.
The embodiment of the specification adopts at least one technical scheme which can achieve the following beneficial effects:
by connecting a plurality of data sources, a data warehouse is established according to data in the data sources, the data in the data warehouse are integrated to obtain multidimensional effective data, query information of a user is received, the multidimensional data in the data warehouse are recombined according to the query information, a query result after recombination is displayed, the workload of developers and operation and maintenance personnel can be effectively reduced, the development and operation and maintenance cost is reduced, the query range of the user can be widened, the limitation of a single system is avoided, any dimensionality query is carried out according to the requirement of the user, and the user experience is improved.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
Fig. 1 is a schematic flow chart of a data self-service query method according to a first embodiment of the present disclosure.
Fig. 2 is a schematic flow chart of another data self-service query method provided in an embodiment of the present specification.
Fig. 3 is a schematic flow chart of another data self-service query method according to an embodiment of the present disclosure.
Fig. 4 is a schematic usage scenario diagram of a data self-service query method according to an embodiment of the present specification.
Fig. 5 is a functional structure diagram of a data self-service query device according to a second embodiment of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any inventive step based on the embodiments of the present disclosure, shall fall within the scope of protection of the present application.
As shown in fig. 1, a first embodiment of the present specification provides a data self-service query method. The execution main body of the embodiment may be a computer or a server or a corresponding data self-service query system, that is, the execution main body may be various and may be set or changed according to actual situations. In addition, a third-party application program can assist the execution main body to execute the embodiment; for example, the server may execute the data self-service query method in this embodiment, and may further install a corresponding application program on a terminal (held by the user) (including but not limited to a mobile phone and a computer), where the server corresponds to the application program, and data transmission may be performed between the server and the terminal held by the user, and a page and information presentation or input and output are performed to the user through the application program.
As shown in fig. 1, the data self-service query method in this embodiment includes:
and S101, connecting a plurality of data sources, and establishing a data warehouse according to data in the plurality of data sources.
S102, integrating data in a data warehouse to obtain multi-dimensional effective data;
s103, receiving query information of a user;
and S104, recombining the multidimensional data in the data warehouse according to the query information, and displaying the recombined query result.
The traditional data query is completed manually, namely, a developer firstly obtains the query requirement of a user and establishes an index relation according to the query requirement of the user so that the user can complete a query task according to the index relation, but due to different requirements of different users, the customized development requirement is large in workload, long in delivery cycle, high in resource consumption and the like, and the development efficiency is influenced. Moreover, the data query range of the user is limited, and if the user does not put forward a requirement in advance and wants to query a certain dimension, the query result cannot be obtained, so that the user experience is influenced.
In the embodiment, the data warehouse is established according to the data in the data sources by connecting the data sources, the data in the data warehouse is integrated to obtain the multi-dimensional effective data, the query information of the user is received, the multi-dimensional data in the data warehouse is recombined according to the query information, and the query result after recombination is displayed, so that the workload of developers and operation and maintenance personnel can be effectively reduced, the development and operation and maintenance costs are reduced, the query range of the user can be widened, the limitation of a single system is avoided, the query with any dimension is carried out according to the requirement of the user, and the user experience is improved.
Fig. 2 is a data self-service query method provided in an embodiment of the present specification, and the method is as follows. The execution subject of the data self-service query method can be a server, and the data self-service query method comprises the following steps:
s201, connecting a plurality of data sources, and establishing a data warehouse according to data in the data sources;
in embodiments of the present description, the plurality of data sources include, but are not limited to, the following:
at least two of an ERP data source, an OA data source, a CRM data source, and a financial data source.
And connecting data sources, namely building a bridge between the original data and Wyn Enterprise (embedded business intelligence and report software), and acquiring data scattered in different systems such as ERP, OA, CRM, finance and the like. The method realizes service datamation and solves the problem of limited user query range caused by information isolated island.
S202, performing one or more operations of filtering, screening, linkage, drilling and aggregation on data in the data warehouse to obtain multi-dimensional effective data;
filtering and screening the data in the data warehouse, for example, removing the test data in the data warehouse;
the data warehouse comprises a plurality of forms, data in the data warehouse is subjected to linkage processing, data of other forms are linked with the data, and data recorded in another form or an aggregation table are called in one form.
And performing drilling operation on the data in the data warehouse, and developing the next layer of data from the current data downwards. For example: the classification of certain data is divided into item names below, and is expanded from a classification list to an item name list.
And performing aggregation operation on the data in the data warehouse according to the next level of data, wherein hour data is aggregated by minutes, day data is aggregated by hours, week data and month data is aggregated by days, and year data is aggregated by months.
In the absence of next level data, aggregation may be performed based on the next level data. For example, monthly data cannot be aggregated by weekly data, but only from day to day.
Through the aggregation operation, the calculation amount can be reduced, and any dimension data query of a user can be realized.
And the data warehouse established on the basis of the data source carries out certain integration processing on the data of the dispersed and heterogeneous data sources in the data warehouse to form multidimensional effective data.
S203, packaging the multidimensional valid data to generate a data query module;
in the embodiment of the present specification, the data query module is, for example, a time dimension query module, and is subdivided into an hour dimension query, a day dimension query, a week dimension query, a month dimension query, and a year dimension query; and the region dimension query module is subdivided into province dimension query, city dimension query, district dimension query, county dimension query, town dimension query and the like.
And S204, displaying the data query module on a user query interface.
The data query module is visually displayed on a user query interface for the user to select arbitrarily. In the traditional user query mode, developers need to develop and design index relations according to multiple dimensions respectively, and then query of users on different dimensions can be achieved, so that the development period is long, and the development efficiency is low.
S205, receiving query information of a user;
in the embodiment of the present specification, the query information of the user is received, including but not limited to the following implementation manners:
the first method is as follows: receiving query information input by a user;
the second method comprises the following steps: the data query module receives a click of a user on a user query interface;
the third method comprises the following steps: and receiving a data query module dragged by a user on the user query interface.
Through a plurality of query modes, the quick query of the user can be realized.
S206, judging the user authority, and screening the multidimensional data in the data warehouse according to the user authority;
and configuring the use authority of different data sources for the user according to different departments or different categories, thereby realizing the management of data confidentiality.
And S207, recombining the screened multidimensional data, and displaying the recombined query result to the user.
In this embodiment of the present specification, as shown in fig. 3, the recombining the multidimensional data in the data warehouse according to the query information includes:
s2071, analyzing the query information to obtain the dimension identification of the user query information;
s2072, searching corresponding data information in the data warehouse according to the dimension identification;
and S2073, sequencing and combining the searched corresponding data information according to a preset rule.
In an embodiment of this specification, displaying the query result after the recombining includes:
and displaying the query result after the recombination through characters, or displaying the query result after the recombination through a visual chart.
The data are displayed through a proper visual chart, for example, a more detailed data display analysis report form such as a visual capital daily report, a sales weekly report, a financial monthly report and the statistical analysis of key data is displayed, so that the maximum query data value is realized, and a user can more quickly and deeply know the query data.
The usage scenario of the data self-service query method is shown in fig. 4, firstly, a system administrator provides a usage environment and a query platform, and assigns authority to each service user before the service user logs in the query platform; then, a data engineer supports metadata service, connects a plurality of data sources, establishes a data warehouse and completes service metadata; the data analysis expert analyzes data, the data in the data warehouse are filtered, screened, linked, drilled, aggregated and the like to obtain multi-dimensional effective data, and the multi-dimensional effective data are packaged to generate a data query module so as to facilitate query of a service user; finally, the service user inquires the required data by self as required, and can share the data with other service users, the system administrator also judges the service user authority when the service user logs in the inquiry platform, and screens the multidimensional data in the data warehouse according to the service user authority; the data self-service query method takes a query service as a core, and is designed around the query service, so that the user experience is improved.
In an embodiment of this specification, the method further includes performing data distribution on the query result, for example, distributing the query result to related personnel through an email, a business WeChat, a nail, or other tools.
In the embodiment of the specification, the method further comprises the steps of creating an instrument panel based on data in the data warehouse, displaying the measurement information and the current situation of the key business index to the enterprise through the instrument panel so that a company decision maker or a business worker can view or use the instrument panel and the report, and monitoring in real time, assisting in decision making and the like.
In the embodiment, the data value is released through cross-system and cross-platform data integration processing, data visualization, data distribution and data value release, the data island problem is solved through a data warehouse, and the problems of various display forms are solved through self-service, customized and flexible layout typesetting, so that the self-service query of the data is realized.
As shown in fig. 5, a second embodiment of the present disclosure provides a data self-service query apparatus, including:
a connection module 501, configured to connect multiple data sources, and establish a data warehouse according to data in the multiple data sources;
an integration module 502, configured to integrate data in the data warehouse to obtain multidimensional valid data;
a receiving module 503, configured to receive query information of a user;
and the combined display module 504 is configured to recombine the multidimensional data in the data warehouse according to the query information, and display a query result after the recombination.
Optionally, the data sources in the connection module 501 include: at least two of an ERP data source, an OA data source, a CRM data source, and a financial data source.
Optionally, the integrating module 502 is further configured to:
and performing one or more operations of filtering, screening, linkage, drilling down and aggregation on the data in the data warehouse to obtain the multi-dimensional effective data.
Optionally, the integrating module 502 is further configured to:
receiving query information input by a user;
or the like, or, alternatively,
the data query module receives a click of a user on a user query interface;
or the like, or, alternatively,
and receiving a data query module dragged by a user on the user query interface.
In an embodiment of this specification, the apparatus further includes:
and the packaging module 505 is configured to package the multidimensional valid data to generate a data query module, so as to display the data query module on a user query interface.
The judging module 506 is used for judging the user authority and screening the multidimensional data in the data warehouse according to the user authority; and recombining the screened multidimensional data, and displaying the recombined query result to a user.
In an embodiment of this specification, displaying the query result after the recombining includes:
and displaying the query result after the recombination through characters, or displaying the query result after the recombination through a visual chart.
In an embodiment of this specification, the recombining, according to query information, the multidimensional data in the data warehouse includes:
analyzing the query information to obtain a dimension identifier of the user query information;
searching corresponding data information in the data warehouse according to the dimension identification;
and sequencing and combining the searched corresponding data information according to a preset rule.
A third embodiment of the present specification provides an electronic apparatus including:
at least one processor;
and the number of the first and second groups,
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:
connecting a plurality of data sources, and establishing a data warehouse according to data in the data sources;
integrating the data in the data warehouse to obtain multi-dimensional effective data;
receiving query information of a user;
and recombining the multidimensional data in the data warehouse according to the query information, and displaying the recombined query result.
The above embodiments may be used in combination.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the apparatus, the electronic device, and the nonvolatile computer storage medium, since they are substantially similar to the embodiments of the method, the description is simple, and the relevant points can be referred to the partial description of the embodiments of the method.
The apparatus, the electronic device, the nonvolatile computer storage medium and the method provided in the embodiments of the present description correspond to each other, and therefore, the apparatus, the electronic device, and the nonvolatile computer storage medium also have similar advantageous technical effects to the corresponding method.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the various elements may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.
As will be appreciated by one skilled in the art, the present specification embodiments may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data optimization apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data optimization apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data optimization apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data optimization apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A data self-service query method is characterized by comprising the following steps:
connecting a plurality of data sources, and establishing a data warehouse according to data in the data sources;
integrating the data in the data warehouse to obtain multi-dimensional effective data;
receiving query information of a user;
and recombining the multidimensional data in the data warehouse according to the query information, and displaying the recombined query result.
2. The self-service data query method according to claim 1, wherein the integrating the data in the data warehouse to obtain the multidimensional valid data comprises:
and performing one or more operations of filtering, screening, linkage, drilling down and aggregation on the data in the data warehouse to obtain multi-dimensional effective data.
3. The self-service data query method according to claim 1 or 2, further comprising:
packaging the multidimensional valid data to generate a data query module;
and displaying the data query module on a user query interface.
4. The self-service data query method according to claim 3, wherein the receiving of query information of a user comprises:
receiving query information input by a user;
or the like, or, alternatively,
receiving the data query module clicked by a user on a user query interface;
or the like, or, alternatively,
and receiving the data query module dragged on the user query interface by the user.
5. The self-service data query method according to claim 1, wherein after receiving query information of a user, the method further comprises:
judging user authority, and screening the multidimensional data in the data warehouse according to the user authority;
and recombining the screened multidimensional data, and displaying the recombined query result to the user.
6. The self-service data query method according to claim 1 or 5, wherein the displaying the recombined query results comprises:
and displaying the query result after the recombination through characters, or displaying the query result after the recombination through a visual chart.
7. The self-service data query method according to claim 1 or 5, wherein the recombining the multidimensional data in the data warehouse according to the query information comprises:
analyzing the query information to obtain a dimension identifier of the user query information;
searching corresponding data information in the data warehouse according to the dimension identification;
and sequencing and combining the searched corresponding data information according to a preset rule.
8. The self-service data query method of claim 1, wherein the data source comprises:
at least two of an ERP data source, an OA data source, a CRM data source, and a financial data source.
9. A data self-service query device, the device comprising:
the connection module is used for connecting a plurality of data sources and establishing a data warehouse according to data in the data sources;
the integration module is used for integrating the data in the data warehouse to obtain multi-dimensional effective data;
the receiving module is used for receiving query information of a user;
and the combined display module is used for recombining the multidimensional data in the data warehouse according to the query information and displaying the query result after recombination.
10. An electronic device, comprising:
at least one processor; and the number of the first and second groups,
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:
connecting a plurality of data sources, and establishing a data warehouse according to data in the data sources;
integrating the data in the data warehouse to obtain multi-dimensional effective data;
receiving query information of a user;
and recombining the multidimensional data in the data warehouse according to the query information, and displaying the recombined query result.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114817338A (en) * | 2022-06-28 | 2022-07-29 | 杭州湖畔网络技术有限公司 | Data processing method and device, electronic equipment and computer readable storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069690A (en) * | 2015-06-11 | 2015-11-18 | 国家电网公司 | Power grid operating data processing method |
| US20150379080A1 (en) * | 2014-06-30 | 2015-12-31 | Tektronix, Inc. | Dynamic selection of source table for db rollup aggregation and query rewrite based on model driven definitions and cardinality estimates |
| CN110674228A (en) * | 2019-09-23 | 2020-01-10 | 阿里巴巴集团控股有限公司 | Data warehouse model construction and data query method, device and equipment |
| CN111966709A (en) * | 2020-09-08 | 2020-11-20 | 支付宝(杭州)信息技术有限公司 | Data query method and device and electronic equipment |
| CN112287015A (en) * | 2020-10-14 | 2021-01-29 | 北京易观智库网络科技有限公司 | Image generation system, image generation method, electronic device, and storage medium |
-
2021
- 2021-07-23 CN CN202110834833.8A patent/CN113590660A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150379080A1 (en) * | 2014-06-30 | 2015-12-31 | Tektronix, Inc. | Dynamic selection of source table for db rollup aggregation and query rewrite based on model driven definitions and cardinality estimates |
| CN105069690A (en) * | 2015-06-11 | 2015-11-18 | 国家电网公司 | Power grid operating data processing method |
| CN110674228A (en) * | 2019-09-23 | 2020-01-10 | 阿里巴巴集团控股有限公司 | Data warehouse model construction and data query method, device and equipment |
| CN111966709A (en) * | 2020-09-08 | 2020-11-20 | 支付宝(杭州)信息技术有限公司 | Data query method and device and electronic equipment |
| CN112287015A (en) * | 2020-10-14 | 2021-01-29 | 北京易观智库网络科技有限公司 | Image generation system, image generation method, electronic device, and storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114817338A (en) * | 2022-06-28 | 2022-07-29 | 杭州湖畔网络技术有限公司 | Data processing method and device, electronic equipment and computer readable storage medium |
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