CN110795470A - Associated data acquisition method, device, equipment and storage medium - Google Patents
Associated data acquisition method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for acquiring associated data. The method comprises the following steps: processing the original data to obtain effective data, and storing the effective data in a data warehouse; and extracting the associated data from the data warehouse according to preset parameters, wherein the preset parameters comprise associated fields or item numbers. According to the embodiment of the invention, the effective data is obtained by processing the original data, the effective data is stored in the data warehouse, and the associated data can be extracted from the data warehouse according to the preset parameters, so that the associated data can be effectively obtained from the big data, and the experience effect of a user is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a method, a device, equipment and a storage medium for acquiring associated data.
Background
With the explosive growth and accumulation of information, big data is increasingly applied in people's lives. The basic characteristics of big data are large data volume, various types, low value density, high speed and high efficiency. Therefore, it is important to research to obtain effective value from the raw materials.
In the process of implementing the invention, the inventor finds that the prior art has the following defects: the mass data often contain key data targets which bring potential values or risks to user decisions and enterprise operations, and the association relationship between the data targets is very complex and generally hidden, so that the associated data cannot be effectively obtained from the large data at present, and the experience effect of users is influenced.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for acquiring associated data, which are used for extracting the associated data in original data.
In a first aspect, an embodiment of the present invention provides a method for acquiring associated data, where the method includes: processing the original data to obtain effective data, and storing the effective data in a data warehouse; and extracting the associated data from the data warehouse according to preset parameters, wherein the preset parameters comprise associated fields or item numbers.
In a second aspect, an embodiment of the present invention further provides an associated data acquiring apparatus, including: the effective data acquisition module is used for processing the original data to obtain effective data, and the effective data storage module is used for storing the effective data in a data warehouse; and the associated data extraction module is used for extracting associated data from the data warehouse according to preset parameters, wherein the preset parameters comprise associated fields or item numbers.
In a third aspect, an embodiment of the present invention further provides an apparatus, where the apparatus includes: one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the associated data acquisition method according to any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements an associated data acquisition method according to any embodiment of the present invention.
The embodiment of the invention provides a method, a device, equipment and a storage medium for acquiring associated data.
Drawings
The above and other features, advantages and aspects of various embodiments of the present invention will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
Fig. 1(a) is a flowchart of an associated data obtaining method according to an embodiment of the present invention;
fig. 1(b) is a schematic view of an application scenario of a method for acquiring associated data according to an embodiment of the present invention;
FIG. 1(c) is a schematic diagram of the underlying architecture of an enterprise information-based data platform provided by an embodiment of the present invention;
FIG. 1(d) is a diagram illustrating a relationship between a user, a money return fact and a department according to an embodiment of the present invention;
fig. 2 is a flowchart of an associated data obtaining method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an associated data acquiring apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus according to a fourth embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and the embodiments of the present invention are illustrative only and are not intended to limit the scope of the present invention.
It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.
Example one
Fig. 1(a) is a flowchart of a method for acquiring associated data according to an embodiment of the present invention, and the embodiment of the present invention is applicable to a case of extracting associated data from big data, and fig. 1(b) is a schematic view of an application scenario according to the embodiment of the present invention. The method can be executed by the associated data acquisition device provided by the embodiment of the invention, and the associated data acquisition device can be realized in a software and/or hardware manner and can be generally integrated in equipment. The method of the embodiment of the invention specifically comprises the following steps:
Optionally, processing the raw data to obtain valid data may include: cleaning the original data to obtain cleaned data; classifying the cleaned data according to the real-time requirement to obtain effective data, wherein the effective data comprises: real-time data and non-real-time data.
Specifically, the raw data in this embodiment is mainly obtained based on the enterprise information data platform, and the specific details about the underlying architecture of the enterprise information data platform are shown in fig. 1 (c). After the raw data is acquired through the enterprise information data platform, because some noise data, for example, data with a non-conforming format or missing information, may exist in the raw data, the raw data needs to be cleaned, so as to eliminate noise in the raw data, and specifically, an open-source ETL tool button may be used for cleaning, and of course, other manners may also be used, and the manner of cleaning and the specific tool are not limited in the embodiments of the present application. The cleaned data can be classified according to the real-time requirement to obtain effective data, the effective data in the embodiment includes real-time data and non-real-time data, and the real-time requirement specifically includes: real-time calculations may or may not be required.
Optionally, the data warehouse comprises: a real-time data caching structure, a data warehouse structure and an active decision engine structure.
Optionally, storing the valid data in the data warehouse may include: storing the real-time data in a real-time data cache structure and an active decision engine structure; and carrying out batch loading processing on the data stored in the real-time data cache structure and storing the data in a stored data warehouse structure.
Optionally, storing the valid data in the data warehouse may include: the non-real-time data is saved in an active decision engine structure.
Specifically, as shown in fig. 1(b), the data warehouse in the embodiment of the present application specifically includes a real-time data caching structure, a data warehouse, and an active decision engine structure, and may specifically store real-time data in the data caching structure and the active decision engine structure by means of incremental extraction. Because the real-time data cache structure is mainly applied to the caching process of real-time data, in order to ensure that the real-time data cache structure always keeps enough storage space to complete the caching process, the data in the real-time data cache structure can be stored in the data warehouse structure after being subjected to batch loading processing. Thus, while the real-time data cache structure and the data types maintained by the data warehouse structure are real-time data, the real-time data cache structure has a higher real-time requirement for the data than the data warehouse structure.
And 103, extracting the associated data from the data warehouse according to preset parameters.
Optionally, obtaining the associated data from the data warehouse according to the preset parameter may include: extracting data from the active decision engine structure according to the associated fields to obtain first associated data matched with the associated fields; and extracting data from the data warehouse structure according to the item number to obtain second associated data matched with the item number.
Specifically, for data stored in the active decision engine, data extraction can be performed according to the association fields, and first association data matched with the association fields is obtained. For example, if it is determined that the valid data includes related data corresponding to a payment item, the preset field is determined as follows: the user (user), the money return fact (sales fact) and the department (dept) extract the associated data from the initiative decision engine according to the relationship diagram shown in fig. 1(d) among the user, the money return fact and the department.
It should be noted that the first association data may be extracted from the active decision engine according to the preset field relationship listed in fig. 1 (d). However, fig. 1(d) only shows the relationship between the user, the fact of the refund and the department, but in practical application, other fields, such as the local place (location) and the time (time), are usually included for the refund item, so the embodiment is only an example and does not limit the specific type of the key field.
Specifically, data extraction can be directly performed on data stored in the data warehouse structure according to the item number to obtain second associated data matched with the item number, for example, if it is determined that valid data includes a repayment item number 1, an engineering item number 2 and a sales item number 3, the second associated data corresponding to the item number can be extracted according to the number 1. And the association relationship within the data is more complex for the first associated data relative to the second associated data.
The embodiment of the invention provides a method for acquiring associated data, wherein the method comprises the steps of processing original data to obtain effective data, storing the effective data in a data warehouse, and extracting the associated data from the data warehouse according to preset parameters, so that the associated data can be effectively acquired from big data, and the experience effect of a user is improved.
Example two
Fig. 2 is a flowchart of an associated data obtaining method according to a second embodiment of the present invention. The embodiment of the present invention may be combined with each alternative in the foregoing implementation, and in the embodiment of the present invention, after obtaining the associated data from the data warehouse according to the preset parameter, the method may further include: and displaying the associated data through the application program.
As shown in fig. 2, the method of the embodiment of the present invention specifically includes:
And step 204, displaying the associated data through the application program.
Specifically, in the embodiment, after the first associated data or the second associated data is obtained, a report may be used to display the first associated data; and for the more complex second associated data, a three-dimensional stereogram of dimension analysis can be adopted for display, so that a user can more intuitively acquire effective information contained in the associated data, and task decision and risk judgment can be conveniently carried out according to the associated data. Of course, the embodiments of the present application are merely examples, and do not limit the specific display form of the related data.
The embodiment of the invention provides a method for acquiring associated data, wherein the method comprises the steps of processing original data to obtain effective data, storing the effective data in a data warehouse, and extracting the associated data from the data warehouse according to preset parameters, so that the associated data can be effectively acquired from big data, and the experience effect of a user is improved. And the associated data is displayed through the application program, so that the user can more intuitively acquire effective information contained in the associated data, and task decision and risk judgment can be conveniently carried out according to the associated data, thereby further improving the experience effect of the user.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an associated data acquiring apparatus according to an embodiment of the present invention. The apparatus may be implemented in software and/or hardware and may generally be integrated in a computer device. As shown in fig. 3, the apparatus includes: an effective data acquisition module 301, an effective data storage module 302 and an associated data extraction module 303.
The system comprises an effective data acquisition module 301 for processing original data to obtain effective data, and an effective data storage module 302 for storing the effective data in a data warehouse; the associated data extracting module 303 is configured to extract associated data from the data warehouse according to preset parameters, where the preset parameters include associated fields or item numbers.
The apparatus for querying a database according to the embodiment of the present invention is the same as the method for querying a database according to the embodiments, and technical details that are not described in detail in the embodiments of the present invention may be referred to the embodiments, and the embodiments of the present invention have the same beneficial effects as the embodiments.
Example four
Fig. 4 is a schematic structural diagram of an apparatus according to an embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary computer device 412 suitable for use in implementing embodiments of the present invention. The computer device 412 shown in FIG. 4 is only one example and should not impose any limitations on the functionality or scope of use of embodiments of the present invention.
As shown in fig. 4, the computer device 412 is in the form of a general purpose computing device. Components of computer device 412 may include, but are not limited to: one or more processors 416, a memory 428, and a bus 418 that couples the various system components (including the memory 428 and the processors 416).
The memory 428 is used to store instructions. Memory 428 can include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)430 and/or cache memory 432. The computer device 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 434 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 418 by one or more data media interfaces. Memory 428 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 440 having a set (at least one) of program modules 442 may be stored, for instance, in memory 428, such program modules 442 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 442 generally perform the functions and/or methodologies of the described embodiments of the invention.
The computer device 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, display 424, etc.), with one or more devices that enable a user to interact with the computer device 412, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 412 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 422. Also, computer device 412 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) through network adapter 420. As shown, network adapter 420 communicates with the other modules of computer device 412 over bus 418. It should be appreciated that although not shown in FIG. 4, other hardware and/or software modules may be used in conjunction with the computer device 412, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processor 416 performs various functional applications and data processing by executing instructions stored in the memory 428, such as performing the following: processing the original data to obtain effective data, and storing the effective data in a data warehouse; and extracting the associated data from the data warehouse according to preset parameters, wherein the preset parameters comprise associated fields or item numbers.
EXAMPLE five
Fifth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the associated data obtaining method provided in any embodiment of the present invention.
Namely: processing the original data to obtain effective data, and storing the effective data in a data warehouse; and extracting the associated data from the data warehouse according to preset parameters, wherein the preset parameters comprise associated fields or item numbers.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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 the context of this document, 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, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for acquiring associated data is characterized by comprising the following steps:
the raw data is processed to obtain valid data,
storing the valid data in a data repository;
and extracting associated data from the data warehouse according to preset parameters, wherein the preset parameters comprise associated fields or item numbers.
2. The method of claim 1, wherein processing the raw data to obtain valid data comprises:
cleaning the original data to obtain cleaned data;
classifying the cleaned data according to the real-time requirement to obtain the valid data, wherein the valid data comprises: real-time data and non-real-time data.
3. The method of claim 2, wherein the data warehouse comprises: a real-time data caching structure, a data warehouse structure and an active decision engine structure.
4. The method of claim 3, wherein said saving said valid data in a data repository comprises:
storing the real-time data in the real-time data cache structure and the proactive decision engine structure;
and carrying out batch loading processing on the data stored in the real-time data cache structure and then storing the data in the data warehouse structure.
5. The method of claim 3, wherein said saving said valid data in a data repository comprises:
saving the non-real-time data in the proactive decision engine structure.
6. The method of claim 3, wherein the obtaining the associated data from the data warehouse according to preset parameters comprises:
extracting data from the active decision engine structure according to the association fields to obtain first association data matched with the association fields;
and extracting data from the data warehouse structure according to the item number to obtain second associated data matched with the item number.
7. The method according to any one of claims 1 to 6, wherein after obtaining the associated data from the data warehouse according to the preset parameters, the method further comprises:
and displaying the associated data through an application program.
8. An associated data acquisition apparatus, characterized by comprising:
an effective data acquisition module for processing the original data to obtain effective data,
the valid data storage module is used for storing the valid data in a data warehouse;
and the associated data extraction module is used for extracting associated data from the data warehouse according to preset parameters, wherein the preset parameters comprise associated fields or item numbers.
9. An apparatus, characterized in that the apparatus comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the association data acquisition method as recited in any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the association data acquisition method as claimed in any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111464596A (en) * | 2020-03-17 | 2020-07-28 | 石化盈科信息技术有限责任公司 | Data processing system, method, apparatus, computer device and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104572939A (en) * | 2014-12-30 | 2015-04-29 | 北京锐安科技有限公司 | Data inquiry method for intra-industry heterogeneous data exchange |
| US20190042887A1 (en) * | 2017-08-04 | 2019-02-07 | Fair Ip, Llc | Computer System for Building, Training and Productionizing Machine Learning Models |
| CN109784736A (en) * | 2019-01-21 | 2019-05-21 | 成都乐超人科技有限公司 | A kind of analysis and decision system based on big data |
| CN110175714A (en) * | 2019-05-23 | 2019-08-27 | 深圳前海微众银行股份有限公司 | Data processing method, device and system |
-
2019
- 2019-10-30 CN CN201911048147.7A patent/CN110795470A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104572939A (en) * | 2014-12-30 | 2015-04-29 | 北京锐安科技有限公司 | Data inquiry method for intra-industry heterogeneous data exchange |
| US20190042887A1 (en) * | 2017-08-04 | 2019-02-07 | Fair Ip, Llc | Computer System for Building, Training and Productionizing Machine Learning Models |
| CN109784736A (en) * | 2019-01-21 | 2019-05-21 | 成都乐超人科技有限公司 | A kind of analysis and decision system based on big data |
| CN110175714A (en) * | 2019-05-23 | 2019-08-27 | 深圳前海微众银行股份有限公司 | Data processing method, device and system |
Non-Patent Citations (2)
| Title |
|---|
| 孙鑫鸽等: "基于数据挖掘的分布式入侵检测系统", 《电子技术应用》 * |
| 杨琪: ""电力能源实时数据仓库系统研究与实现"", 《中国优秀硕士论文全文数据库 信息科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111464596A (en) * | 2020-03-17 | 2020-07-28 | 石化盈科信息技术有限责任公司 | Data processing system, method, apparatus, computer device and storage medium |
| CN111464596B (en) * | 2020-03-17 | 2023-08-25 | 石化盈科信息技术有限责任公司 | Data processing system, method, apparatus, computer device, and storage medium |
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