CN106874293B - Data processing method and device - Google Patents
Data processing method and device Download PDFInfo
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- CN106874293B CN106874293B CN201510921249.0A CN201510921249A CN106874293B CN 106874293 B CN106874293 B CN 106874293B CN 201510921249 A CN201510921249 A CN 201510921249A CN 106874293 B CN106874293 B CN 106874293B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
Abstract
The invention discloses a data processing method and a data processing device, relates to the technical field of telecommunication, and aims to solve the problem of low accuracy of evaluation results of webpage putting effects. The method of the invention comprises the following steps: acquiring user behavior data; inquiring the generation time of a third-party cookie for recording the user behavior data; calculating a time interval between a generation time of the user behavior data and a generation time of the cookie; if the time interval is longer than the preset time length, the third-party cookie recording the user behavior data is a stable cookie; and determining the user behavior data as valid data according to the stable cookie. The method is mainly applied to the process of acquiring data.
Description
Technical Field
The present invention relates to the field of telecommunication technologies, and in particular, to a data processing method and apparatus.
Background
A cookie is a file that stores some of the state, actions, and settings of a user when accessing a website. When a third-party internet monitoring company carries out webpage launching effect evaluation, a section of code is implanted into a webpage, netizen online browsing behavior data recorded in a local cookie is sent to a server of the third-party internet monitoring company through the section of code, and behavior data recorded by the cookie is used as a main analysis resource to evaluate the webpage launching effect.
And the third-party internet monitoring company directly analyzes the data and evaluates the webpage putting effect when using the cookie data. All behavior data for the occurrence of browsing behavior or clicking behavior is contained in the initial cookie data set. The cookie data generation includes netizen's browsing behavior for surfing the internet in a stable network environment, and netizen's browsing behavior for surfing the internet in an unstable network environment and netizen's browsing behavior resulting from cheating. Unstable internet environments, such as internet cafes, school rooms, etc., can regularly clean computer cookies, and when the same web page is browsed, new cookies can be regenerated, and cookie replacement can be frequent. And the internet citizen browsing behavior caused by cheating can also change cookies frequently, and illegally increase exposure, flow and the like in a more concealed mode. The internet citizen browsing behavior data with frequent cookie replacement is unstable and has low value in webpage putting effect evaluation. And the browsing behavior of net citizens surfing the Internet in the network environment is stabilized, and the value in the evaluation of the webpage putting effect is higher.
In the prior art, when evaluating the webpage launching effect, all initial data in the third-party cookie are acquired, wherein the initial data comprise stable netizen browsing behavior data and unstable netizen browsing behavior data, the stability of the data is not distinguished, and the data is directly processed. The value in the webpage effect evaluation is low due to unstable netizen browsing behavior data, and the data processing is the same as that of the stable netizen browsing behavior data in the data processing process, so that the accuracy of the webpage putting effect evaluation result is reduced.
Disclosure of Invention
The invention provides a data processing method and device, which can solve the problem of low accuracy of a webpage putting effect evaluation result.
In order to solve the above technical problem, in one aspect, the present invention provides a data processing method, including:
acquiring user behavior data;
inquiring the generation time of a third-party cookie for recording the user behavior data;
calculating a time interval between a generation time of the user behavior data and a generation time of the cookie;
if the time interval is longer than the preset time length, the third-party cookie recording the user behavior data is a stable cookie;
and determining the user behavior data as valid data according to the stable cookie.
In another aspect, the present invention further provides a data processing apparatus, including:
the acquiring unit is used for acquiring user behavior data;
the query unit is used for querying and recording the generation time of the third-party cookie of the user behavior data acquired by the acquisition unit;
a calculating unit, configured to calculate a time interval between a generation time of the user behavior data and a generation time of the cookie queried by the querying unit;
the recording unit is used for recording the third-party cookie of the user behavior data as a stable cookie if the time interval calculated by the calculating unit is longer than the preset time length;
and the determining unit is used for determining the user behavior data as effective data according to the stable cookie recorded by the recording unit.
The data processing method and the data processing device provided by the invention can acquire the user behavior data, inquire the generation time of the third-party cookie for recording the user behavior data, calculate the time interval between the generation time of the user behavior data and the generation time of the third-party cookie, and if the time interval is longer than the preset time length, the third-party cookie for recording the user behavior data is the stable cookie, and the user behavior data recorded in the stable cookie is the effective data. Compared with the prior art, the method and the device can eliminate unstable invalid data from a large amount of behavior data recorded by the cookie, obtain stable and effective behavior data, analyze the launching effect according to the high-value effective behavior data, and improve the accuracy of evaluating the webpage launching effect.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a data processing method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another data processing method provided by the embodiment of the invention;
FIG. 3 is a block diagram illustrating a data processing apparatus according to an embodiment of the present invention;
fig. 4 is a block diagram illustrating another data processing apparatus according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
An embodiment of the present invention provides a data processing method, as shown in fig. 1, the method includes:
101. and acquiring user behavior data.
The user behavior data may comprise at least one of: a triggering action for a network event, such as a click on an advertisement; the browsing time of the network display content, such as the exposure statistics of the advertisement, can be determined according to the browsing time of the advertisement by the user; the time to access the website; and waiting for the three user behavior data.
Specifically, the user behavior data may be generated by a user operating a target page, and the target page may be a game page, an advertisement page, a shopping page, or the like.
User behavior data is information that results from a user accessing a website and other operations on the website. This information may be stored in the user's local cookie in the form of a log. A cookie is a file that stores part of the state, actions and settings of a user when accessing a network, and typically includes the domain name of the accessed website, the access start time, the IP address of the visitor, and some settings of the visitor about the website.
The acquired user behavior data is used for analyzing the operation effect of one or more target pages, and is not used for all network users. Therefore, a code is implanted in the target page, the code can allocate a cookie number to a user clicking the target page, record other behavior data generated by the user on the target page in a local cookie, and transmit the behavior data recorded in the local cookie to a third-party internet monitoring company through the code. Therefore, the cookie of the third-party internet monitoring party only comprises behavior data of clicking a target page by the user, but not all local cookies of the user, so that the data volume of subsequent data processing is reduced, and the data processing efficiency is improved.
When a user clicks the target page for the first time, a cookie is generated, when the user clicks the target page again, user behavior data is recorded in the cookie generated for the first time, and a new cookie number is not distributed when the behavior data is recorded. This allows different users to be distinguished by cookie numbering.
102. And inquiring the generation time of the third-party cookie for recording the user behavior data.
The third party cookie means that a link address embedded with another domain name through a tag is arranged on a page browsed by a user currently, the cookie set by the link is called the third party cookie, and the cookie set by the current page is the first party cookie.
The generation time of the third-party cookie is generated when the user clicks the target page for the first time. And inquiring the cookie generation time of the recorded behavior data, namely the time when the inquiry user firstly clicks the target page.
103. And calculating the time interval between the generation time of the user behavior data and the generation time of the cookie.
The generation time of the user behavior data is obtained from the user behavior data acquired in step 101, the cookie generation time is obtained in step 102, and the time interval between the two is calculated.
Typical records of time include year, month, day, hour, minute, second. When the time interval is calculated, item-by-item comparison can be carried out according to the minimum time unit and the unified time unit, and the time difference is calculated; the manner in which the time interval is calculated is not limited in this regard.
Illustratively, the generation time of the behavior data is 23 minutes and 10 seconds at 8 days of 11 months and 2 months in 2015, the generation time of the cookie is 10 minutes and 5 seconds at 9 days of 11 months and 1 days in 2015, and the two are subtracted, and similarly to decimal subtraction, the time interval between the two is calculated to be 13 minutes and 5 seconds at 23 hours and 13 minutes from the minimum time unit second.
104. And if the time interval is longer than the preset time length, the third-party cookie recording the user behavior data is a stable cookie.
The preset duration may be a comparison value used to determine whether the behavior data is valid behavior data. The preset duration is obtained according to the behavior habit or experience of the user for accessing the webpage, and can also be adaptively adjusted according to the accuracy of the judgment result of the effective behavior data. In this embodiment, the duration of the preset duration is not limited.
And if the time interval calculated according to the step 103 is longer than the preset time, recording the third-party cookie of the user behavior data as the stable cookie. The stable cookie is a way of determining whether a user is a stable netizen, that is, when a time interval from when the cookie is born to when user behavior data reappears in a project is longer than a preset time, the user is considered as a stable netizen and has more value in analysis than a user with an unstable cookie.
105. And determining the user behavior data as valid data according to the stable cookie.
The user behavior data recorded in the stable cookie of step 104 is determined to be valid data. The effective behavior data is data with higher value in the cookies.
Illustratively, in cookies of the third party, the time information of the click target page recorded in the data information corresponding to the cookie number c1, c 1: 10 months, 1 day, 8:00 in 2015; 11/2015, 1/8: 00. The preset time period is 12 hours.
Behavior data with the click target page time of 2015, 11, 1, 8:00 is extracted. Recording the cookie generation time of the behavior data, namely, the time for a user to click a target page for the first time is 2015, 10, 1, 8:00, calculating the time interval between the generation time of the behavior data and the generation time of the cookie, wherein the time interval is 31 days and is greater than the preset time length by 12 hours, so that the cookie is determined to be a stable cookie, and the user behavior data in the cookie is valid data.
The data processing method provided by the embodiment of the invention can acquire the user behavior data, inquire the generation time of the third-party cookie for recording the user behavior data, calculate the time interval between the generation time of the user behavior data and the generation time of the third-party cookie, and if the time interval is longer than the preset time, the third-party cookie for recording the user behavior data is a stable cookie, and the user behavior data recorded in the stable cookie is effective data. Compared with the prior art, the embodiment of the invention can eliminate unstable invalid data from a large amount of behavior data recorded by the cookie, obtain stable and effective behavior data, analyze the launching effect according to the high-value effective behavior data, and improve the accuracy of evaluating the webpage launching effect.
Further, as a refinement and an extension of the method illustrated in fig. 1, another data processing method is further provided in an embodiment of the present invention, as illustrated in fig. 2, where the method includes:
201. and acquiring user behavior data.
The user behavior data is generated by the operation of the user on the webpage. The user behavior data includes at least one of: a triggering action for a network event; displaying content browsing time on a network; the time of access to the web site.
The method comprises the steps that user behavior data are obtained from cookies of a third-party internet monitoring company, and when the user behavior data are obtained, a target page can be updated at any time when the behavior data are operated or can be not updated when the behavior data are stopped. In this embodiment, the running state of the target page is not limited. Generally, in the process of running the target page, the running effect of the target page is analyzed according to the behavior data, and the content of the target page is adjusted to achieve a better effect.
For the target page which stops running, all behavior data are generated, and the behavior data are not updated along with time, so that the time problem is not involved when the behavior data are extracted, and the relation between the extraction time and the behavior data generation time is not considered.
For the target page which is still running, the time problem needs to be considered when extracting the behavior data. If the running condition of the target page in a period of time is analyzed, behavior data are generated in the period of time; and if the target page is monitored at any time, extracting behavior data generated in the corresponding monitoring period according to the monitoring period.
202. And inquiring the generation time of the third-party cookie for recording the user behavior data.
203. And calculating the time interval between the generation time of the user behavior data and the generation time of the cookie.
And the generation time of the user behavior data is the time generated by the user operating the webpage. The method for calculating the time interval comprises the following steps: calculating the time difference between the generation time of the current user behavior data and the generation time of the third-party cookie; determining a time error rate according to the time difference; a time interval between the generation time of the user behavior data and the generation time of the cookie is calculated using the time error rate.
204. And if the time interval is longer than the preset time length, the third-party cookie recording the user behavior data is a stable cookie.
205. And determining the user behavior data as valid data according to the stable cookie.
When the time interval is longer than the preset time length, detecting whether other user behavior data related to the user behavior data exist or not; when other user behavior data related to the user behavior data exist, acquiring the other user behavior data; and determining the user behavior data and other user behavior data related to the user behavior data as valid data.
Illustratively, in cookies of the third party, the time information of the click target page recorded in the data information corresponding to the cookie number c2, c 2: 11 month, 2015, 1 day, 8: 00; 11/2015, 1/9: 00; 11/2015, 2/8: 00. The preset time period is 12 hours.
Behavior data with the click target page time of 2015, 11, 2, 8:00 is extracted. Recording the cookie generation time of the behavior data, namely, the time for a user to click a target page for the first time is 8:00 in 11/1/2015, calculating the time interval between the generation time of the behavior data and the generation time of the cookie, wherein the time interval is 24 hours and is greater than the preset time length by 12 hours, so that the behavior data is determined to be valid behavior data, and the behavior data is obtained. Clicking the behavior data with the target page time of 2015, 11, 1, 9:00, generating time between the cookie generation time and the valid behavior data, and determining the generation time as the valid behavior data.
206. And counting the valid data.
The statistics of the valid data include: counting the stable netizen exposure of the effective data; and counting the stable netizen independent exposure of the effective data. The counted exposure of the stable netizen and the independent exposure of the stable netizen are user behavior data generated by the stable cookie, and the netizen generating the stable cookie is the stable netizen.
The exposure of the stable netizen is the sum of the times of browsing behaviors of the stable netizen on the webpage. The stable net citizen independent exposure is the number of users of the stable net citizen who browse the webpage. Corresponding to the situation of stable netizens, the exposure of the whole netizens and the independent exposure of the whole netizens can be counted for the user behavior data recorded in all cookies. The stable netizen exposure is the sum of times of browsing behaviors of the user on the webpage. The stable net citizen independent exposure is the number of users who browse the advertisement page.
And taking the exposure of the stable netizen and the independent exposure of the stable netizen as data indexes in the evaluation of the advertisement putting effect. The more exposure of one advertisement page indicates that the user is more easily attracted by the advertisement, and the more independent exposure indicates that the advertisement is spread more widely.
For the embodiment of the present invention, specific application scenarios may be as follows, but are not limited to the following scenarios, including: there are three netizens, C1, C2 and C3, the preset time interval is 12 hours, and cookie records are generated for the same web page, as shown in table 1.
TABLE 1
According to the description of the steps shown in fig. 2, each netizen is judged:
netizen C1: the preset time interval is 12 hours compared to the time interval of the first exposure in the item and the cookie generation time. [ (8: 00 on month 1, 11/2015) - (8: 00 on month 1, 10/2015) >12 h ]. Therefore, the third party cookie recording the C1 user behavior data is a stable cookie, C1 is a stable netizen, and the first exposure data of C1 is a stable netizen exposure.
Netizen C2: the preset time interval is 12 hours compared to the time interval of the first exposure in the item and the cookie generation time. [ (11/1/2015 9: 00) - (2015 11/1/8: 00) ═ 1 hr <12 hr ], so this exposure is not an unstable exposure. However, since C2 belongs to a stable netizen exposure at the second exposure, [ (2015 11/2/8: 00) - (2015 11/1/8: 00) ═ 24 hours >12 hours ], the third party cookie recording the C2 user behavior data is a stable cookie, and C2 is a stable netizen. The system will correct the first exposure to also be a stable netizen exposure.
Netizen C3: the time of the first and second appearance in the item is less than 12 hours from the birth of the cookie, so the two exposures are both unstable exposures.
The exposure conditions of three netizens, C1, C2 and C3, were counted and shown in Table 2.
TABLE 2
Further, as an implementation of the method shown in fig. 1 and fig. 2, another embodiment of the present invention further provides a data processing apparatus. The embodiment of the apparatus corresponds to the embodiment of the method, and for convenience of reading, details of the embodiment of the apparatus are not repeated one by one, but it should be clear that the embodiment of the apparatus can correspondingly implement all the contents of the embodiment of the method. As shown in fig. 3, the apparatus includes: an acquisition unit 31, a query unit 32, a calculation unit 33, a recording unit 34, and a determination unit 35, wherein,
an acquisition unit 31 for acquiring user behavior data;
a query unit 32, configured to query and record the generation time of the third-party cookie of the user behavior data acquired by the acquisition unit 31;
a calculating unit 33, configured to calculate a time interval between a generation time of the user behavior data and a generation time of the cookie queried by the querying unit 32;
a recording unit 34, configured to determine that the third-party cookie recording the user behavior data is a stable cookie if the time interval calculated by the calculating unit 33 is greater than a preset time duration;
the determining unit 35 is configured to determine, according to the stable cookie recorded by the recording unit 34, that the user behavior data is valid data.
Further, the user behavior data acquired by the acquiring unit 31 at least includes one of the following:
a triggering action for a network event;
displaying content browsing time on a network;
the time of access to the web site.
Further, as shown in fig. 4, the calculating unit 33 includes:
a calculating module 331, configured to calculate a time difference between a current generation time of the user behavior data and a generation time of the third-party cookie;
a determining module 332, configured to determine a time error rate according to the time difference calculated by the calculating module 331;
the calculating module 331 is further configured to calculate a time interval between the generation time of the user behavior data and the generation time of the cookie by using the time error rate determined by the determining module.
Further, as shown in fig. 4, the determining unit 35 includes:
the detecting module 351 is configured to detect whether there is any other user behavior data related to the user behavior data when the time interval is longer than a preset time;
an obtaining module 352, configured to obtain other user behavior data when there is other user behavior data related to the user behavior data detected by the detecting module 351;
the determining module 353 is configured to determine the user behavior data and other user behavior data related to the user behavior data acquired by the acquiring module 352 as valid data.
Further, as shown in fig. 4, the apparatus further includes:
a statistic unit 36 for counting the stable netizen exposure of the effective data;
and the statistical unit 36 is also used for counting the stable netizen independent exposure of the effective data. The data processing device provided by the embodiment of the invention can acquire the user behavior data, inquire the generation time of the third-party cookie for recording the user behavior data, calculate the time interval between the generation time of the user behavior data and the generation time of the third-party cookie, and if the time interval is longer than the preset time, the third-party cookie for recording the user behavior data is a stable cookie, and the user behavior data recorded in the stable cookie is effective data. Compared with the prior art, the embodiment of the invention can eliminate unstable invalid data from a large amount of behavior data recorded by the cookie, obtain stable and effective behavior data, analyze the launching effect according to the high-value effective behavior data, and improve the accuracy of evaluating the webpage launching effect.
The data processing device comprises a processor and a memory, wherein the acquiring unit 31, the querying unit 32, the calculating unit 33, the recording unit 34 and the determining unit 35 are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the problem of low accuracy of the evaluation result of the webpage launching effect is solved by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The present application further provides a computer program product adapted to perform program code for initializing the following method steps when executed on a data processing device: acquiring user behavior data; inquiring the generation time of a third-party cookie for recording the user behavior data; calculating a time interval between a generation time of the user behavior data and a generation time of the cookie; if the time interval is longer than the preset time length, the third-party cookie recording the user behavior data is a stable cookie; and determining the user behavior data as valid data according to the stable cookie.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method of data processing, the method comprising:
acquiring user behavior data, wherein the user behavior data are information generated by a user accessing a target page and the user operating the target page;
inquiring and recording the generation time of a third-party cookie of the user behavior data, wherein the generation time of the third-party cookie is generated when the user clicks the target page for the first time;
calculating a time interval between a generation time of the user behavior data and a generation time of the cookie;
if the time interval is longer than the preset time length, the third-party cookie recording the user behavior data is a stable cookie;
determining the user behavior data as valid data according to the stable cookie;
the determining, according to the stable cookie, that the user behavior data is valid data includes:
when the time interval is longer than a preset time length, detecting whether other user behavior data related to the user behavior data exist or not;
when other user behavior data related to the user behavior data exist, acquiring the other user behavior data;
and determining the user behavior data and other user behavior data related to the user behavior data as valid data.
2. The method of claim 1, wherein the user behavior data comprises at least one of:
a triggering action for a network event;
displaying content browsing time on a network;
the time of access to the web site.
3. The method of claim 2, wherein calculating the time interval between the generation time of the user behavior data and the generation time of the cookie comprises:
calculating a time difference between the current generation time of the user behavior data and the generation time of the third-party cookie;
determining a time error rate based on the time difference;
and calculating the time interval between the generation time of the user behavior data and the generation time of the cookie by using the time error rate.
4. The method of claim 1, further comprising:
counting the stable netizen exposure of the effective data;
and counting the stable netizen independent exposure of the effective data.
5. A data processing apparatus, characterized in that the apparatus comprises:
the device comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring user behavior data, and the user behavior data are information generated by a user accessing a target page and the user operating the target page;
the query unit is used for querying and recording the generation time of the third-party cookie of the user behavior data acquired by the acquisition unit, wherein the generation time of the third-party cookie is generated when the user clicks the target page for the first time;
a calculating unit, configured to calculate a time interval between a generation time of the user behavior data and a generation time of the cookie queried by the querying unit;
the recording unit is used for recording the third-party cookie of the user behavior data as a stable cookie if the time interval calculated by the calculating unit is longer than the preset time length;
the determining unit is used for determining the user behavior data as effective data according to the stable cookie recorded by the recording unit;
the determination unit includes:
the detection module is used for detecting whether other user behavior data related to the user behavior data exist or not when the time interval is longer than a preset time length;
the acquisition module is used for acquiring other user behavior data related to the user behavior data detected by the detection module when the other user behavior data exist;
and the determining module is used for determining the user behavior data and other user behavior data related to the user behavior data acquired by the acquiring module as effective data.
6. The apparatus of claim 5, wherein the user behavior data obtained by the obtaining unit comprises at least one of:
a triggering action for a network event;
displaying content browsing time on a network;
the time of access to the web site.
7. The apparatus of claim 6, wherein the computing unit comprises:
the calculating module is used for calculating the time difference between the current generation time of the user behavior data and the generation time of the third-party cookie;
the determining module is used for determining a time error rate according to the time difference calculated by the calculating module;
the calculating module is further used for calculating the time interval between the generation time of the user behavior data and the generation time of the cookie by using the time error rate determined by the determining module.
8. The apparatus of claim 5, further comprising:
the statistical unit is used for counting the stable netizen exposure of the effective data;
the statistic unit is also used for counting the stable netizen independent exposure of the effective data.
9. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, a device in which the storage medium is located is controlled to execute the data processing method of any one of claims 1 to 4.
10. A processor for running a program, wherein the program is to execute the data processing method of any one of claims 1 to 4 when the program is run.
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| CN111127050A (en) * | 2018-10-29 | 2020-05-08 | 北京奇虎科技有限公司 | Content channel evaluation method and device, electronic equipment and storage medium |
| CN109522495B (en) * | 2018-11-21 | 2021-04-06 | 世纪龙信息网络有限责任公司 | Data analysis method and device, computer equipment and storage medium |
| CN109947633B (en) * | 2018-12-29 | 2023-04-07 | 北京明略昭辉科技有限公司 | Data processing method and device, storage medium and processor |
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