CN104778177A - Data processing method and device - Google Patents
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Abstract
The invention provides a data processing method and a device. The method comprises the steps of receiving a processing request transmitted by request equipment, obtaining the average quantity of information corresponding to types released by users in an objective website within a preset objective time range according to acquired historical raw data, computing a product of the average quantity of the information corresponding to each type and a preset weight number corresponding to the type for the average quantity of the information corresponding to each type, computing the sum of the products corresponding to the types, obtaining the user liveness of the users relative to the objective website within the preset objective time range, and transmitting a processing result to the request equipment, wherein the processing request comprises an identifier of the objective website; the historical raw data comprises information released by the users in websites; the sum of the weight numbers corresponding to the types is 1; and the processing result comprises the user liveness. According to the data processing method and the device, the user liveness of the users can be accurately and quickly acquired.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a data processing method and apparatus.
Background
With the rapid development of online social networks such as social networking sites, microblogs, online communities and the like, a real two-way propagation and new media era is gradually formed. The online social network allows each user to create their own content and to quickly propagate it away. According to incomplete statistics, over 3000 new data are generated in domestic large microblog websites per second on average.
On the basis of such a scale of data, how to quickly and accurately acquire the user liveness by an online social network manager becomes an urgent problem to be solved. In contrast, in the prior art, no scheme capable of accurately and quickly acquiring the activity of the user exists.
Disclosure of Invention
The invention provides a data processing method and a data processing device, which are used for solving the problem that the prior art cannot accurately and quickly acquire the activity of a user.
A first aspect of the present invention provides a data processing method, including:
receiving a processing request sent by a request device, wherein the processing request comprises an identifier of a target website;
acquiring the average quantity of information corresponding to each type issued in a target website by a user within a preset target time range according to collected historical original data, wherein the historical original data comprises the information issued in each website by each user;
calculating the product of the average number of the information corresponding to each type and a preset weight corresponding to the type, wherein the sum of the weights corresponding to the types is 1;
calculating the sum of products corresponding to each type to obtain the user activity of the user relative to the target website within the target time range;
sending a processing result to the requesting device, the processing result including the user activity.
Another aspect of the present invention provides a data processing apparatus comprising:
the receiving module is used for receiving a processing request sent by a request device, wherein the processing request comprises an identifier of a target website;
the acquisition module is used for acquiring the average quantity of information corresponding to each type and published in the target website by a user within a preset target time range according to the acquired historical original data, wherein the historical original data comprises the information published in each website by each user;
the processing module is used for calculating the product of the average number of the information corresponding to each type and a preset weight corresponding to the type, wherein the sum of the weights corresponding to each type is 1;
the processing module is further configured to calculate a sum of products corresponding to the types, and obtain a user activity of the user relative to the target website within the target time range;
a sending module, configured to send a processing result to the requesting device, where the processing result includes the user activity.
According to the data processing method and device provided by the invention, the average quantity of information corresponding to each type issued by the target user in the preset target time range and in the preset target website is obtained according to the collected historical original data, and further, the user activity of the target user relative to the target website in the target time range is obtained based on the weight corresponding to each type, so that the user activity of the user can be accurately and quickly obtained.
Drawings
Fig. 1 is a schematic flowchart of a data processing method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a data processing apparatus according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.
Fig. 1 is a schematic flow chart of a data processing method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
101. and receiving a processing request sent by a request device, wherein the processing request comprises the identification of the target website.
Specifically, the website may be a microblog website, such as a green microblog, a vacation microblog, or the like, and the identification of the website may be identified by a Service Profile Identifier (spID for short).
102. According to the collected historical original data, the average number of information corresponding to each type issued in the target website by the user in a preset target time range is obtained, and the historical original data comprises the information issued in each website by each user.
In practical application, the historical raw data can be obtained by designing a data structure and storing and compressing the data structure by using a distributed architecture on the basis of large-scale social network information. Specifically, the social network information may include a posting time, a website, a type of posted information, an identification of a user, and the like. The types may include various situations, such as originality, forwarding, commenting, sharing, and the like.
Further, in the data processing process of this embodiment, in order to be able to quickly read the historical raw data, the information in the social network information may be concatenated, and a "#" separation is used between each piece of information. Specifically, the user identifier may be a user code (usercode) identifier, the type identifier may be an assetType identifier, and the release time identifier may be a date identifier. For example, the last stored historical raw data may be in the form of: the post time # website identifies the # type identifies the # user's identification, namely date # spID # assetType # usercode. For example, if the user of user a issues information of type D at website C at time B, the historical raw data corresponding to the information may be B # C # D # a.
Further, in order to save storage space and transmission bandwidth, data obtained after splicing can be compressed before storage, and specifically, a Lempel-Ziv-oberhimer (LZO) compression algorithm with a high compression ratio and a high transmission speed can be used for compression.
Specifically, in this embodiment, the average number of information may be the number of information in a unit time length, and accordingly, 102 may include:
obtaining the quantity of information corresponding to each type issued in the target website by the user within the target time range according to the historical original data;
and dividing the number of the information corresponding to each type by the duration corresponding to the target time range to obtain the average number of the information corresponding to each type published in the target website by the user in the target time range.
And the duration corresponding to the target time range is the number of unit durations in the target time range. Specifically, the unit time length may be preset. It should be noted that, for different unit durations, the average number of calculated information may also be different, and for example, assuming that the number of certain types of information published in a website by a user in the last week is 70, if a day is taken as the unit duration, the duration corresponding to the target time range is 7/1=7, and the average number of corresponding information is 70/7= 10; if the unit duration is one week directly, the duration corresponding to the target time range is 7/7=1, and the average number of the corresponding pieces of information is 70/1= 70.
More specifically, in practical applications, the specific process of obtaining the amount of the information corresponding to each type published in the target website by the user in the target time range may include:
according to a preset target time range, obtaining information corresponding to each type issued in each website by each user in the target time range, wherein the data format is as follows: spiD # assetType # usercode;
and performing dimensionality reduction processing on the data to obtain the quantity of information corresponding to each type issued in the target website by the target user in the target time range, wherein the data format is spID # assetType # user code num.
Specifically, dimension reduction processing may be performed using a format (key, value), where the value is the number of times the key value appears. Furthermore, in order to more intuitively understand the scheme of the present embodiment, the following example is used for illustration, and it should be noted that the following example is only a specific embodiment, and does not limit other embodiments of the present embodiment.
Assuming that the number of the information of type D issued by the user a in the website C in the target time range needs to be counted currently, the information corresponding to each type issued by each user in each website in the target time range may be queried by using the C # D # a as a key value. Specifically, through the above operation, the format of the output data is C # D # a [1,1, … ], where a "1" is noted in [1,1, … ] every time the key value appears. After the query is finished, counting the number of '1', namely summing up the times of occurrence of the key value, to obtain a value, namely the number of the information of the type D issued by the user A in the website C within the target time range. Further, the output result data is C # D # a num, where num is a value.
103. And calculating the product of the average number of the information corresponding to each type and a preset weight corresponding to the type, wherein the sum of the weights corresponding to the types is 1.
Specifically, there is a difference in contribution of different types of information to the user liveness, for example, in the types of originality, forwarding and comment, the information of the original type can reflect the user liveness better. Therefore, the weights corresponding to different types can be set according to the influence of different types on the activity of the user. For example, assume that the types include an original type, a forwarding type, and a comment type, the weight corresponding to the original type is 0.7, the weight corresponding to the forwarding type is 0.2, and the weight corresponding to the comment type is 0.1. Correspondingly, assuming that the average number of the information corresponding to the original, forwarded and comment types published in the target website by the user in the target time range is 30, 20 and 10, respectively, the product corresponding to the original type is 0.7 × 30=21, the product corresponding to the forwarded type is 0.2 × 20=4, and the product corresponding to the comment type is 0.1 × 10= 1.
104. And calculating the sum of products corresponding to each type to obtain the user activity of the user relative to the target website within the target time range.
Specifically, in the above example, the user activity of the user with respect to the target website in the target time range is 21+4+1= 26.
105. Sending a processing result to the requesting device, the processing result including the user activity.
In practical application, the obtained processing result can be stored in a distributed file system, and correspondingly, the application system can read the processing result from the distributed file system to perform data analysis, foreground page display and the like.
In addition, in an actual application scenario, in order to further analyze the user activity, one-time analysis and historical data comparison can be performed on the historical data. The one-time analysis comprises user activity analysis, active user analysis of a target website and the like. The historical data comparison comprises the steps of analyzing the change rule of the user activity, and carrying out multi-dimensional comparison on the analyzed data in a period of time, for example, obtaining the change rule, the change curve and the like of the user activity of a certain user relative to the user activity of a target website in each time range, so that the change rule of the user activity can be clear at a glance. Optionally, the processing result may further include an identification of an active user of the target website within the target time range; the method may further comprise:
for each type, performing descending ranking on the user according to the quantity of information corresponding to the type published in the target website by the user in the target time range;
and determining the users n before ranking as the active users of the target website in the target time range, wherein n is a preset value.
Specifically, in practical applications, a specific process for implementing the above steps may include: according to data corresponding to the spiD # assetType # usercode num, taking the spiD # assetType # INTmax-num as a key value, and performing descending ranking on the users, wherein the data format is the spiD # assetType # INTmax-num [ usercode, usercode, … ], and [ usercode, usercode, … ] is the ranked user identification; and determining users corresponding to the first n user identifications in the user identifications of [ usercode, usercode, … ] as active users of the target website within the target time range. Where INTmax-num is used to achieve descending ordering.
By the method and the device, the active users of the target website in the target time range can be conveniently and quickly determined.
Optionally, before obtaining the user activity of the user, the user may be screened by the present embodiment, so as to save resources. Correspondingly, 104 may specifically include: and calculating the sum of products corresponding to each type to obtain the user activity of the active user relative to the target website in the target time range.
Optionally, the processing result may further include a number of users publishing information corresponding to each type in the target website; after the number of the information corresponding to each type published in the target website by the user in the target time range is obtained, the following process may be performed:
according to the data corresponding to the spiD # assetType # usercode num, the spiD # assetType is used as key, the usercode is filtered, and the output data format is as follows: spiD # assettType [ num, num, … ];
summing each num to obtain the total number of users publishing the information corresponding to the type in the target website, wherein the data format is as follows: spiD # assetType sum (num).
By the embodiment, the total number of the users publishing the information corresponding to the type in the target website can be conveniently and quickly counted.
Optionally, the processing result may further include a total number of users who publish information in the target website; after the number of the information corresponding to each type published in the target website by the user in the target time range is obtained, the following process may be performed:
according to the data corresponding to the spiD # assetType # usercode num, taking the spiD # usercode as a key value, outputting the data with the following file format: spiD # usercode [1,1, … ];
and filtering out the same value values corresponding to different types by taking the spiD as a key value and the usercode as a value, and outputting the data with the following file format: a spad usercode;
taking the spiD as a key value, recording each usercode value as '1', and outputting data with the following file format: spiD [1,1, … ];
counting the number of '1' to obtain the total number of users releasing information in the target website, wherein the data format is as follows: spiD sum (1).
By the embodiment, the total number of the users who issue the information in the target website can be counted conveniently and quickly.
According to the data processing method provided by the invention, the average quantity of the information corresponding to each type issued in the target website by the user in the preset target time range is obtained according to the collected historical original data, and further, the user activity of the user relative to the target website in the target time range is obtained based on the weight corresponding to each type, so that the user activity of the user can be accurately and quickly obtained.
Fig. 2 is a schematic structural diagram of a data processing apparatus according to a second embodiment of the present invention, as shown in fig. 2, the apparatus includes: the device comprises a receiving module 21, an obtaining module 22, a processing module 23 and a sending module 24; wherein,
a receiving module 21, configured to receive a processing request sent by a requesting device, where the processing request includes an identifier of a target website;
the acquisition module 22 is configured to acquire, according to the acquired historical raw data, an average number of pieces of information corresponding to each type and published in the target website by the user within a preset target time range, where the historical raw data includes information published in each website by each user;
a processing module 23, configured to calculate, for the average number of information corresponding to each type, a product of the average number of information corresponding to each type and a preset weight corresponding to the type, where a sum of the weights corresponding to each type is 1;
the processing module 23 is further configured to calculate a sum of products corresponding to the types, so as to obtain a user activity of the user relative to the target website within the target time range;
a sending module 24, configured to send a processing result to the requesting device, where the processing result includes the user activity.
In practical applications, the apparatus may further include: and the acquisition module is used for acquiring and storing the historical original data by utilizing a distributed architecture.
Further, in the data processing process of this embodiment, in order to be able to read the historical raw data quickly, the storage form of the historical raw data may be: the post time # website identifies the # type identifies the # user's identification, namely date # spID # assetType # usercode.
Further, in order to save storage space and transmission bandwidth, the apparatus may further include a compression module configured to compress the historical raw data before storing the historical raw data. Specifically, the compression can be performed by using a Lempel-Ziv-Oberhumer compression algorithm with a high compression ratio and a high transmission speed, which is abbreviated as LZO compression algorithm.
Specifically, the obtaining module may include: the statistical unit is used for acquiring the quantity of information corresponding to each type issued in the target website by the user within the target time range according to the historical original data; and the calculating unit is used for dividing the number of the information corresponding to each type by the duration corresponding to the target time range to obtain the average number of the information corresponding to each type published in the target website by the user in the target time range.
The time length corresponding to the target time range is the number of unit time lengths in the target time range, and the unit time length can be preset. More specifically, the statistical unit specifically includes:
the first processing subunit is configured to obtain, according to a preset target time range, information corresponding to each type issued by each user in each website within the target time range, where a data format of the information is: spiD # assetType # usercode;
and the second processing subunit is configured to perform dimension reduction processing on the data to obtain the quantity of information corresponding to each type issued in the target website by the target user in the target time range, where the data format of the information is spID # assetType # usercode num.
In practical applications, the apparatus may further include: and the storage module is used for storing the processing result into the distributed file system. Correspondingly, the application system can read the processing result from the distributed file system to perform data analysis, foreground page display and the like.
In addition, in an actual application scenario, in order to further analyze the user activity, the processing result further includes an identifier of an active user of the target website within the target time range; correspondingly, the device may further include:
the sorting module is used for carrying out descending ranking on the users according to the number of the information corresponding to the types published in the target website by the users in the target time range;
and the screening module is used for determining the users with n top ranking as the active users of the target website within the target time range, wherein n is a preset value.
In practical application, the sorting module may be specifically configured to perform descending ranking on the user according to data corresponding to the spID # assetType # usercodenum, where a data format of the sorting module is spID # assetType # INTmax-num [ usercode, usercode, … ], where [ usercode, usercode, … ] is a ranked user identifier;
the screening module may be specifically configured to determine, as active users of the target website within the target time range, users corresponding to the first n user identifiers in each user identifier of [ usercode, usercode, … ].
Where INTmax-num is used to achieve descending ordering. By the method and the device, the active users of the target website in the target time range can be conveniently and quickly determined.
Optionally, before obtaining the user activity of the user, the user may be screened by the present embodiment, so as to save resources. Correspondingly, the processing module 23 is specifically configured to calculate a sum of products corresponding to the types, and obtain the user activity of the active user relative to the target website within the target time range.
Optionally, the processing result may further include a number of users publishing information corresponding to each type in the target website; the processing module 23 may be further configured to filter out the usercode according to the data corresponding to the spID # assetType # usercode num, with the spID # assetType as the key, where the output data format is: spiD # assettType [ num, num, … ];
the processing module 23 may be further configured to sum each num to obtain a total number of users publishing the information corresponding to the type in the target website, where a data format of the total number is: spID # assettypesum (num).
By the embodiment, the total number of the users publishing the information corresponding to the type in the target website can be conveniently and quickly counted.
Optionally, the processing result may further include a total number of users who publish information in the target website; the processing module 23 may be further configured to output data in a file format as follows by using the spID # usercode as a key value according to the data corresponding to the spID # assetType # usercode num: spiD # usercode [1,1, … ];
the processing module 23 may also be configured to filter out different types of corresponding same value values by using the spID as a key value and using the usercode as a value, and output data in the following file format: a spIdusercode;
the processing module 23 may be further configured to take the spID as a key value, record each usercode value as "1", and output data in the following file format: spiD [1,1, … ];
the processing module 23 may be further configured to count the number of "1" to obtain the total number of users publishing information in the target website, where the data format is as follows: spiD sum (1).
By the embodiment, the total number of the users who issue the information in the target website can be counted conveniently and quickly. In addition, the processing module 23 may be further configured to obtain a change rule and a change curve of the user activity of the user according to the user activity of the user relative to the target website in each time range.
According to the data processing device provided by the invention, the average quantity of information corresponding to each type issued in the target website by the user in the preset target time range is obtained according to the collected historical original data, and further, the user activity of the user relative to the target website in the target time range is obtained based on the weight corresponding to each type, so that the user activity of the user can be accurately and quickly obtained.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A data processing method, comprising:
receiving a processing request sent by a request device, wherein the processing request comprises an identifier of a target website;
acquiring the average quantity of information corresponding to each type issued in a target website by a user within a preset target time range according to collected historical original data, wherein the historical original data comprises the information issued in each website by each user;
calculating the product of the average number of the information corresponding to each type and a preset weight corresponding to the type, wherein the sum of the weights corresponding to the types is 1;
calculating the sum of products corresponding to each type to obtain the user activity of the user relative to the target website within the target time range;
sending a processing result to the requesting device, the processing result including the user activity.
2. The method according to claim 1, wherein the obtaining an average number of each type of information corresponding to each type published in the target website by the user within a preset target time range according to the collected historical raw data comprises:
obtaining the quantity of information corresponding to each type issued in the target website by the user within the target time range according to the historical original data;
and dividing the number of the information corresponding to each type by the duration corresponding to the target time range to obtain the average number of the information corresponding to each type published in the target website by the user in the target time range.
3. The method of claim 2, wherein the processing results further include an identification of active users of the target website within the target time range; after the obtaining of the amount of the information corresponding to each type published in the target website by the user in the target time range, the method further includes:
for each type, performing descending ranking on the user according to the quantity of information corresponding to the type published in the target website by the user in the target time range;
and determining the users n before ranking as the active users of the target website in the target time range, wherein n is a preset value.
4. The method according to claim 3, wherein the calculating a sum of products corresponding to the types to obtain the user activity of the user with respect to the target website within the target time range specifically comprises:
and calculating the sum of products corresponding to each type to obtain the user activity of the active user relative to the target website in the target time range.
5. The method according to any one of claims 1-4, further comprising:
and storing the processing result into a distributed file system so that an application system reads the processing result from the distributed file system.
6. A data processing apparatus, comprising:
the receiving module is used for receiving a processing request sent by a request device, wherein the processing request comprises an identifier of a target website;
the acquisition module is used for acquiring the average quantity of information corresponding to each type and published in the target website by a user within a preset target time range according to the acquired historical original data, wherein the historical original data comprises the information published in each website by each user;
the processing module is used for calculating the product of the average number of the information corresponding to each type and a preset weight corresponding to the type, wherein the sum of the weights corresponding to each type is 1;
the processing module is further configured to calculate a sum of products corresponding to the types, and obtain a user activity of the user relative to the target website within the target time range;
a sending module, configured to send a processing result to the requesting device, where the processing result includes the user activity.
7. The apparatus of claim 6, wherein the obtaining module comprises:
the statistical unit is used for acquiring the quantity of information corresponding to each type issued in the target website by the user within the target time range according to the historical original data;
and the calculating unit is used for dividing the number of the information corresponding to each type by the duration corresponding to the target time range to obtain the average number of the information corresponding to each type published in the target website by the user in the target time range.
8. The apparatus of claim 7, wherein the processing result further comprises an identification of active users of the target website within the target time range; the device further comprises:
the sorting module is used for carrying out descending ranking on the users according to the number of the information corresponding to the types published in the target website by the users in the target time range;
and the screening module is used for determining the users with n top ranking as the active users of the target website within the target time range, wherein n is a preset value.
9. The apparatus of claim 8,
the processing module is specifically configured to calculate a sum of products corresponding to the types, and obtain the user activity of the active user relative to the target website within the target time range.
10. The apparatus according to any one of claims 6-9, further comprising:
and the storage module is used for storing the processing result into a distributed file system so as to enable an application system to read the processing result from the distributed file system.
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| CN106815308A (en) * | 2016-12-16 | 2017-06-09 | 上海客鹭信息技术有限公司 | Towards the onion formula data organization method and system of big data analysis |
| CN108062350A (en) * | 2017-11-08 | 2018-05-22 | 深圳市金立通信设备有限公司 | A kind of data processing method, node device and computer-readable medium |
| CN108419137A (en) * | 2018-01-15 | 2018-08-17 | 上海全土豆文化传播有限公司 | Data processing method and data processing equipment |
| CN108460611A (en) * | 2017-02-20 | 2018-08-28 | 阿里巴巴集团控股有限公司 | A kind of information processing method and its application process and relevant device |
| WO2019144551A1 (en) * | 2018-01-24 | 2019-08-01 | 平安科技（深圳）有限公司 | Multimedia file processing method, server, and storage medium |
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