CN110647527B - Big data-based invalid label clearing method and device, equipment and readable medium - Google Patents
Big data-based invalid label clearing method and device, equipment and readable medium Download PDFInfo
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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
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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- 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
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
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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
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- G06F16/90—Details of database functions independent of the retrieved data types
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Abstract
The invention provides a big data-based invalid label clearing method, a big data-based invalid label clearing device, equipment and a readable medium. The method comprises the following steps: in the label engine system, acquiring the corresponding relation between a user identifier and a held label from label information records held by each user; scanning all labels in the label engine system based on the corresponding relation between the user identification and the held labels, and acquiring the labels in the corresponding relation without each user as invalid labels; and clearing the invalid label. By adopting the technical scheme, the invalid tags in the tag engine system can be completely and automatically cleared, the clearing range covers the whole execution tasks, and the redundant invalid tags can be completely cleared. Compare with the artifical clearance of prior art, not only can effectively save the human cost, whole process is consuming time shorter moreover, compares with the result of artifical clearance, and the accuracy is higher.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of computer application, in particular to a big data-based invalid label clearing method, a big data-based invalid label clearing device, big data-based invalid label clearing equipment and a readable medium.
[ background of the invention ]
In recent years, with the reliance of users on the internet, the magnitude of large data generated based on the internet has been increasing.
In the prior art, in order to facilitate operations such as access, search, management and the like on stored big data, a corresponding tag engine system is established between a user and a storage system for storing the big data, and a corresponding data calculation processing engine is set. The user can set the tag task in the tag engine system, acquire corresponding data from the big data based on the tag task and the attributes of the data in the stored big data, and perform calculation processing through the data calculation processing engine. For example, the tag task of the present embodiment may be directly established based on the data attribute in the big data, or may also be established by analyzing the data attribute in the big data. In the existing label engine system, a single user usually has a self-created independent label task. However, in some scenarios, a sharing service needs to be provided, so that in order to enable normal use of other sharing users, after the user who creates the tag task finishes using the created tag task, the user only makes a stop identifier change in a user mode, and in the tag engine system, the other users can still call the tag, and at this time, the tag task still exists. In addition, some unknown operations of the developer can also result in the insertion of tagging tasks that are not used by the user.
Based on the above, it can be known that in the storage system storing large data in the prior art, there are many redundant invalid tags, which results in a great waste of resources. In order to clear the invalid labels, in the prior art, the invalid labels are cleared by manual intervention in a manual clearing mode, but in the manual clearing process, time and labor are wasted, the accuracy is poor, and 100% of accurate operation is guaranteed due to invalidity.
[ summary of the invention ]
The invention provides a big data-based invalid label removing method, a big data-based invalid label removing device, equipment and a readable medium, which are used for improving invalid label removing efficiency and accuracy.
The invention provides a big data-based invalid tag clearing method, which comprises the following steps:
in a label engine system, acquiring the corresponding relation between a user identifier and a held label from label information records held by each user;
scanning all labels in the label engine system based on the corresponding relation between the user identification and the held labels, and acquiring the labels in the corresponding relation without each user as invalid labels;
and clearing the invalid label.
Further optionally, in the method described above, the clearing the invalid tag includes:
and clearing the invalid label according to a preset clearing strategy.
Further optionally, in the method as described above, the preset clearing policy includes clearing the invalid tag at a preset fixed time, or clearing the invalid tag when detecting that a performance parameter of the tag engine system is smaller than a preset threshold.
Further optionally, in the method as described above, based on a correspondence between the user identifier and a held tag, all tags in the tag engine system are scanned, and a tag without the correspondence is obtained, and after the tag is used as an invalid tag, the method further includes:
storing the invalid tag in a set of invalid tags;
clearing the invalid label, specifically comprising:
and clearing all the tags in the invalid tag set.
The invention provides a big data-based invalid label clearing device, which comprises:
the acquisition module is used for acquiring the corresponding relation between the user identification and the held label from the label information record held by each user in the label engine system;
a scanning module, configured to scan all tags in the tag engine system based on a correspondence between the user identifier and a held tag, and obtain a tag that does not exist in the correspondence of each user as an invalid tag;
and the clearing module is used for clearing the invalid label.
Further optionally, in the apparatus as described above, the clearing module is configured to:
and clearing the invalid label according to a preset clearing strategy.
Further optionally, in the apparatus as described above, the preset clearing policy includes clearing the invalid tag at a preset fixed time, or clearing the invalid tag when detecting that a performance parameter of the tag engine system is smaller than a preset threshold.
Further optionally, in the apparatus as described above, the apparatus further comprises a storage module;
the storage module is used for storing the invalid tag in an invalid tag set;
the clearing module is used for clearing all the tags in the invalid tag set stored in the storage module.
The present invention also provides a computer apparatus, the apparatus comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a big-data based invalid tag cleanup method as described above.
The present invention also provides a computer readable medium having stored thereon a computer program which, when executed by a processor, implements the big-data based invalid tag elimination method as described above.
By adopting the technical scheme, the invalid label in the label engine system can be completely and automatically cleared, the clearing range covers the whole execution task, and redundant invalid labels can be completely cleared. Compare with the artifical clearance of prior art, not only can effectively save the human cost, whole process is consuming time shorter moreover, compares with the result of artifical clearance, and the accuracy is higher. Therefore, the technical scheme of the invention can effectively improve the efficiency and accuracy of invalid label removal. Meanwhile, by adopting the technical scheme of the invention, after the invalid label is cleared, the waste of computing resources and storage resources in the label engine system can be effectively reduced, so that the resource utilization is more effective and the competition becomes more.
[ description of the drawings ]
FIG. 1 is a flowchart of an embodiment of a big data based tag elimination method according to the present invention.
FIG. 2 is a block diagram of a big data based invalid tag clearing apparatus according to a first embodiment of the present invention.
FIG. 3 is a block diagram of a second embodiment of the big data based invalid tag clearing apparatus according to the present invention.
FIG. 4 is a block diagram of an embodiment of a computer device of the present invention.
Fig. 5 is an exemplary diagram of a computer device provided by the present invention.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flowchart of an embodiment of a big data based tag elimination method according to the present invention. As shown in fig. 1, the method for clearing a tag based on big data in this embodiment may specifically include the following steps:
s100, in a label engine system, acquiring a corresponding relation between a user identifier and a held label from label information records held by each user;
the execution subject of the big data based tag removing method of this embodiment may be a big data based tag removing apparatus, which may be an independent electronic entity, or may also be an application using software for short. The device can be in communication connection with the tag engine system and a storage system for storing big data, so that invalid tags in the storage system can be cleared by accessing information in the tag engine system and the storage system.
Specifically, in the tag engine system, when each user creates a tag task, a corresponding relationship between a user identifier and a held tag is correspondingly established, and the corresponding relationship is stored in a tag information record. That is, the correspondence relationship stored in the tag information record is an effective tag held by the user. If the user does not want to continue maintaining the tag after running the task corresponding to the tag, at this time, on the user level, the user can delete the tag from the corresponding relationship in the tag information record. In practical applications, one user can hold multiple active tags at the same time.
In this embodiment, the correspondence between the user identifier of each user and the held tag is obtained by accessing the tag information record held by each user. The tags in the corresponding relationship of each user are all valid tags.
S101, scanning all labels in a label engine system based on the corresponding relation between user identification and held labels, and acquiring labels which do not exist in the corresponding relation of each user as invalid labels;
in practical application, for the task of sharing tags, for example, a creating user of a tag deletes a tag held by the creating user on the user mode level, but the tag may also be used by other sharing users, so that the tag still exists on the system mode level in the tag engine system. The label has no holder, that is, the label is not in the corresponding relationship between the user identifier and the label in the label information record of the user. In addition, some unknown operations performed by the developer may also result in insertion of tags that are not used by the user. Therefore, some labels which do not hold the user exist in the label engine system. In this embodiment, in the tag engine system, in addition to maintaining the tag information records held by each user in the user mode level, the tag engine system is configured to maintain the tag information records held by each user in the user mode level. While maintaining all tags at the system-level. At this time, all tags in the tag engine system may be scanned, and in the scanning process, tags that do not exist in the correspondence between the user identifier and the held tags are obtained as invalid tags based on the correspondence between the user identifier and the held tags.
And S102, clearing the invalid label.
In this embodiment, the invalid label obtained after scanning is finally cleared, and redundancy is removed, so as to save system resources. For example, in this embodiment, the invalid tag may be cleared according to a preset clearing policy. Optionally, the preset clearing policy of this embodiment includes clearing the invalid tag at a preset fixed time, or clearing the invalid tag when it is detected that the throughput of the storage system storing the large data is smaller than a preset threshold.
The preset fixed time can be some valley periods selected according to the time period, so that the overhead of the system can be reduced, and the influence on other normal services in the label clearing process is avoided.
The performance parameter of this embodiment may be throughput, a memory occupation ratio, or other performance parameters, and when it is detected that the performance parameter of the tag engine system is smaller than a preset threshold, it may also be considered as a time when the tag engine system is idle, and at this time, the clearing of the invalid tag may be started, which may also reduce overhead of the system and avoid an influence on other normal services in the tag clearing process.
In addition, optionally, in this embodiment, if the number of invalid tags is large, in order to facilitate management of the invalid tags, in step S101, based on the correspondence between the user identifier and the held tags, all tags in the tag engine system may be scanned, tags in the correspondence that do not exist for each user are obtained, and after being used as invalid tags, the invalid tags may be stored in an invalid tag set. By detecting continuously, all invalid tags in the tag engine system can be detected and stored in the invalid tag set. Finally, the invalid tag is cleared according to step S102 in the above embodiment, which may specifically be: all tags in the invalid set of tags are cleared. At this time, all invalid tags in the invalid tag set can be directly cleared without sequentially deleting the single invalid tag, thereby further improving the clearing efficiency of the invalid tags.
By adopting the technical scheme, the method for clearing the big data-based tag can completely and automatically clear the invalid tag in the tag engine system, clear the execution tasks with the full coverage of the range and completely clear the redundant invalid tag. Compare with the artifical clearance of prior art, not only can effectively save the human cost, whole process is consuming time shorter moreover, compares with the result of artifical clearance, and the accuracy is higher. Therefore, the technical scheme of the embodiment can effectively improve the efficiency and accuracy of invalid label removal. Meanwhile, by adopting the technical scheme of the embodiment, after the invalid label is cleared, the waste of computing resources and storage resources in the label engine system can be effectively reduced, so that the resource utilization is more effective and the competitiveness is more competitive.
FIG. 2 is a block diagram of a big data based invalid tag clearing apparatus according to a first embodiment of the present invention. As shown in fig. 2, the apparatus for clearing invalid tags based on big data in this embodiment may specifically include:
the obtaining module 10 is configured to obtain, in the tag engine system, a corresponding relationship between a user identifier and a tag held by each user from a tag information record held by each user;
the scanning module 11 is configured to scan all tags in the tag engine system based on the correspondence between the user identifier and the held tag acquired by the acquisition module 10, and acquire a tag that does not exist in the correspondence between the users, as an invalid tag;
the clearing module 12 is configured to clear the invalid label scanned by the scanning module 11.
The implementation principle and technical effect of the big data based invalid tag removing apparatus in this embodiment are the same as those of the related method embodiment, and reference may be made to the description of the related method embodiment in detail, which is not described herein again.
FIG. 3 is a block diagram of a second embodiment of the big data based invalid tag clearing apparatus according to the present invention. As shown in fig. 3, the invalid tag clearing apparatus based on big data of this embodiment further describes the technical solution of the present invention in more detail based on the technical solution of the embodiment shown in fig. 2.
In the big data based invalid tag removing apparatus of this embodiment, the removing module 12 is specifically configured to:
and clearing the invalid label according to a preset clearing strategy.
Further optionally, the preset clearing policy includes clearing the invalid tag at a preset fixed time, or clearing the invalid tag when detecting that the performance parameter of the tag engine system is smaller than a preset threshold.
Further optionally, as shown in fig. 3, the invalid tag clearing apparatus based on big data according to this embodiment further includes a storage module 13.
The storage module 13 is used for storing the invalid tags scanned by the scanning module 11 in an invalid tag set;
the clearing module 12 is configured to clear all tags in the invalid tag set stored in the storage module 13.
The implementation principle and technical effect of the big data based invalid tag removing apparatus in this embodiment are the same as those of the related method embodiment, and reference may be made to the description of the related method embodiment in detail, which is not described herein again.
FIG. 4 is a block diagram of an embodiment of a computer device of the present invention. As shown in fig. 4, the computer device of the present embodiment includes: one or more processors 30, and a memory 40, the memory 40 for storing one or more programs, when the one or more programs stored in the memory 40 are executed by the one or more processors 30, cause the one or more processors 30 to implement the big-data based invalid tag cleanup method of the embodiment shown in fig. 1 above. The embodiment shown in fig. 4 is exemplified by including a plurality of processors 30.
For example, fig. 5 is an exemplary diagram of a computer device provided by the present invention. FIG. 5 illustrates a block diagram of an exemplary computer device 12a suitable for use in implementing embodiments of the present invention. The computer device 12a shown in fig. 5 is only an example and should not bring any limitations to the functionality or scope of use of the embodiments of the present invention.
As shown in FIG. 5, computer device 12a is in the form of a general purpose computing device. The components of computer device 12a may include, but are not limited to: one or more processors 16a, a system memory 28a, and a bus 18a that connects the various system components (including the system memory 28a and the processors 16 a).
Bus 18a represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
The system memory 28a may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30a and/or cache memory 32a. Computer device 12a may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34a may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive"). Although not shown in FIG. 5, 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 18a by one or more data media interfaces. System memory 28a may 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 the various embodiments of the invention described above in fig. 1-3.
Program/utility 40a having a set (at least one) of program modules 42a may be stored, for example, in system memory 28a, such program modules 42a 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 include an implementation of a network environment. Program modules 42a generally perform the functions and/or methodologies described above in connection with the various embodiments of fig. 1-3 of the present invention.
The processor 16a executes various functional applications and data processing by executing programs stored in the system memory 28a, for example, implementing the big data based invalid tag removal method shown in the above-described embodiment.
The present invention also provides a computer-readable medium on which a computer program is stored, which when executed by a processor implements the big-data based invalid tag removing method as shown in the above embodiments.
The computer-readable medium of this embodiment may include RAM30a, and/or cache memory 32a, and/or storage system 34a in system memory 28a in the embodiment illustrated in fig. 5 described above.
With the development of technology, the propagation path of computer programs is no longer limited to tangible media, and the computer programs can be directly downloaded from a network or acquired by other methods. Accordingly, the computer-readable medium in the present embodiment may include not only tangible media but also intangible media.
The computer-readable medium of the present embodiments may take any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium 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 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 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).
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An invalid tag clearing method based on big data, which is characterized by comprising the following steps:
in the label engine system, acquiring the corresponding relation between a user identifier and a held label from label information records held by each user;
scanning all labels in the label engine system based on the corresponding relation between the user identification and the held labels, and acquiring the labels in the corresponding relation without each user as invalid labels;
and clearing the invalid label.
2. The method of claim 1, wherein clearing the invalid tag comprises:
and clearing the invalid label according to a preset clearing strategy.
3. The method of claim 2, wherein the preset clearing policy comprises clearing the invalid tag at a preset fixed time or clearing the invalid tag when a performance parameter of the tag engine system is detected to be less than a preset threshold.
4. The method according to claim 1, wherein all tags in the tag engine system are scanned based on the correspondence between the user identifier and the held tag, and after obtaining the tags without the correspondence as invalid tags, the method further comprises:
storing the invalid tag in a set of invalid tags;
clearing the invalid label, specifically comprising:
and clearing all the tags in the invalid tag set.
5. An invalid tag elimination apparatus based on big data, the apparatus comprising:
the acquisition module is used for acquiring the corresponding relation between the user identification and the held label from the label information record held by each user in the label engine system;
a scanning module, configured to scan all tags in the tag engine system based on a correspondence between the user identifier and a held tag, and obtain a tag that does not exist in the correspondence of each user as an invalid tag;
and the clearing module is used for clearing the invalid label.
6. The apparatus of claim 5, wherein the purge module is configured to:
and clearing the invalid label according to a preset clearing strategy.
7. The apparatus of claim 6, wherein the preset clearing policy comprises clearing the invalid tag at a preset fixed time or clearing the invalid tag when a performance parameter of the tag engine system is detected to be less than a preset threshold.
8. The apparatus of claim 5, further comprising a storage module;
the storage module is used for storing the invalid tag in an invalid tag set;
the clearing module is used for clearing all the tags in the invalid tag set stored in the storage module.
9. A computer device, the device comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-4.
10. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-4.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101340464A (en) * | 2008-08-22 | 2009-01-07 | 中国电信股份有限公司 | Method and system for unified network resource management |
| CN103839088A (en) * | 2012-11-22 | 2014-06-04 | 汉朗科技(北京)有限责任公司 | Electronic label system based on near-distance wireless communication technology, and electronic label updating method |
| CN104602042A (en) * | 2014-12-31 | 2015-05-06 | 合一网络技术(北京)有限公司 | User behavior based label setting method |
| CN106326291A (en) * | 2015-07-01 | 2017-01-11 | 羊剑 | Tag-based demand guidance pairing method and system |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5937561B2 (en) * | 2013-10-29 | 2016-06-22 | 富士フイルム株式会社 | Repair information management device, repair information management program, repair information management system, repair information management method |
| CN105684391B (en) * | 2013-11-04 | 2019-06-07 | 伊尔拉米公司 | Access control rule based on label automatically generates |
| CN105808653B (en) * | 2016-02-26 | 2019-08-09 | 广州品唯软件有限公司 | A kind of data processing method and device based on user tag system |
| CN106354755A (en) * | 2016-08-17 | 2017-01-25 | 洑云龙 | Optimizing and processing method for user's portrait |
| CN107491479B (en) * | 2017-07-05 | 2020-11-24 | 上海大学 | Label management method based on ontology library |
| CN109446443B (en) * | 2018-10-18 | 2021-11-09 | 武汉斗鱼网络科技有限公司 | Label updating method, device, equipment and storage medium |
-
2019
- 2019-08-30 CN CN201910813433.1A patent/CN110647527B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101340464A (en) * | 2008-08-22 | 2009-01-07 | 中国电信股份有限公司 | Method and system for unified network resource management |
| CN103839088A (en) * | 2012-11-22 | 2014-06-04 | 汉朗科技(北京)有限责任公司 | Electronic label system based on near-distance wireless communication technology, and electronic label updating method |
| CN104602042A (en) * | 2014-12-31 | 2015-05-06 | 合一网络技术(北京)有限公司 | User behavior based label setting method |
| CN106326291A (en) * | 2015-07-01 | 2017-01-11 | 羊剑 | Tag-based demand guidance pairing method and system |
Non-Patent Citations (2)
| Title |
|---|
| spark sql：relational data processing in spark;Michael Armbrust等;《sigmod 15 ACM New York》;20151231;1383-1394 * |
| 一种采用标签引擎技术信息发布***的设计与实现;朱明等;《计算机应用与软件》;20150215;第32卷(第2期);89-93 * |
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