CN115757302A - Data analysis method, device, equipment and storage medium - Google Patents
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Abstract
The present specification discloses a method, an apparatus, a device, and a storage medium for data analysis, which can determine log data of a program for executing a call operation on data to be analyzed from a large amount of log data by determining the program for executing the call operation on the data to be analyzed, so as to reduce the number of the log data that needs to be screened, and further can screen target log data from each candidate log data, and analyze the data to be analyzed according to the target log data.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for data analysis.
Background
With the development of internet technology, each internet service provider pays more and more attention to user privacy data related to application services, and in order to protect the privacy data, each internet service provider needs to know which business operations in the application services call the user privacy data.
The common method is to screen out the log data of the related personal privacy data from the log data recorded by the application service, and further know which business operations in the application service call the privacy data of the user according to the screened log, so that the privacy data of the user can be protected.
However, a large amount of log data is usually recorded in the log of the application service, which makes the process of screening each log data of the user privacy data involved in the application service extremely difficult.
Disclosure of Invention
The present specification provides a method, an apparatus, a device and a storage medium for data analysis, which partially solve the problems of the prior art.
The technical scheme adopted by the specification is as follows:
the present specification provides a method of data analysis comprising:
acquiring data to be analyzed;
determining a program for executing calling operation on the data to be analyzed;
determining log data generated by executing the program from the log data as candidate log data;
and screening candidate log data generated by calling the data to be analyzed by the program from the candidate log data to serve as target log data, and performing data analysis on the data to be analyzed according to the target log data.
Optionally, determining a tag of a program that performs a call operation on the data to be analyzed specifically includes:
and inputting the data to be analyzed into a pre-trained analysis model so as to determine a program for executing calling operation on the data to be analyzed through the analysis model.
Optionally, training the analysis model specifically includes:
constructing each sample data;
inputting the sample data into the analysis model aiming at each sample data, and determining a program for executing calling operation on the sample data through the analysis model as a program corresponding to the sample data;
and training the analysis model by taking the minimized deviation between the program corresponding to the sample data and the program for actually executing the calling operation on the sample data as an optimization target.
Optionally, constructing each sample datum specifically includes:
aiming at any two original sample data, judging whether any two original sample data are matched according to any two original sample data and programs corresponding to any two original sample data;
if so, carrying out normalization processing on the program information of the programs corresponding to the any two original sample data to obtain normalized program information;
and taking the program corresponding to the normalization program information as the program corresponding to any two original sample data to obtain each sample data.
Optionally, constructing each sample datum specifically includes:
determining a program corresponding to each original sample data as each target program;
judging whether the quantity of each original sample data corresponding to each target program exceeds a preset threshold value or not for each target program;
if so, splitting the target program into subprograms, and determining each original sample data corresponding to each subprogram;
and constructing each sample data according to the subprogram corresponding to each original sample data.
Optionally, constructing each sample datum specifically includes:
acquiring each original log data;
normalizing the data format of the data contained in each original log data to obtain each processed log data;
and constructing each sample data according to the processed log data.
Optionally, screening candidate log data generated by the program calling the data to be analyzed from the candidate log data, as target log data, specifically including:
extracting the characteristic representation of the data to be analyzed and the characteristic representation of each candidate log data through the analysis model;
and screening candidate log data generated by calling the data to be analyzed by the program from the candidate log data according to the similarity between the characteristic representation of the data to be analyzed and the characteristic representation of each candidate log data, and taking the candidate log data as target log data.
The present specification provides an apparatus for data analysis, comprising:
the acquisition module is used for acquiring data to be analyzed;
the determining module is used for determining a program for executing calling operation on the data to be analyzed;
the matching module is used for determining log data generated by executing the program from the log data to serve as candidate log data;
and the execution module is used for screening out candidate log data generated by calling the data to be analyzed by the program from the candidate log data to serve as target log data, and performing data analysis on the data to be analyzed according to the target log data.
The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described method of data analysis.
The present specification provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above-mentioned data analysis method when executing the program.
The technical scheme adopted by the specification can achieve the following beneficial effects:
the data analysis method provided in this specification includes first obtaining data to be analyzed, determining a program for executing a call operation on the data to be analyzed, determining log data generated by executing the program for executing the call operation on the data to be analyzed from the log data, using the log data as candidate log data, screening candidate log data generated by calling the data to be analyzed by the program for executing the call operation on the data to be analyzed from the candidate log data, using the candidate log data as target log data, and performing data analysis on the data to be analyzed according to the target log data.
The method can be seen in that the program for executing the calling operation on the data to be analyzed can be determined, so that the log data of the program for executing the calling operation on the data to be analyzed can be determined from a large amount of log data, the number of the log data needing to be screened can be reduced, the target log data can be screened from the candidate log data, and the data to be analyzed can be analyzed according to the target log data.
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The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. On the attachment
In the figure:
FIG. 1 is a schematic flow diagram of a method of data analysis provided herein;
FIG. 2 is a schematic diagram of a method of determining target log data provided herein;
FIG. 3 is a schematic diagram of an apparatus for data analysis provided herein;
fig. 4 is a schematic diagram of an electronic device corresponding to fig. 1 provided in the present specification.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without making any creative effort belong to the protection scope of the present specification.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a data analysis method provided in the present specification, including the following steps:
s100: and acquiring data to be analyzed.
In this specification, the service platform may obtain data to be analyzed, further may screen log data related to the data to be analyzed from the log data of the service platform, and use the log data as target log data, and analyze the data to be analyzed according to the target log data, where the log data may be, for example: log data corresponding to operations such as data call, data processing and the like recorded by programs such as a system, an application, an interface and the like.
The data to be analyzed may be data that needs to be analyzed and is determined according to the service requirement, for example: personal privacy data of the user, etc.
In addition, the data to be analyzed may have a disordered data format and a disordered code in the data content, so that the service platform may further clean the data after acquiring the data to be analyzed to obtain the cleaned data to be analyzed, and further may analyze the cleaned data to be analyzed. The data cleansing method here may be such as: removing messy codes in the data to be analyzed, adjusting data with inconsistent formats such as time, date, numerical value, full-half-angle symbol and the like in the data to be analyzed, filling missing values in the data to be analyzed and the like.
In the present specification, the execution subject of the method for implementing data analysis may refer to a designated device such as a server installed on a service platform, or may refer to a terminal device such as a desktop computer or a notebook computer.
S102: and determining a program for executing calling operation on the data to be analyzed.
After the server obtains the data to be analyzed, a program for executing a calling operation on the data to be analyzed can be determined according to a preset business rule, wherein the business rule can be a corresponding relation between each piece of data to be analyzed and each program determined according to each program corresponding to each piece of historical data to be analyzed.
In addition, after the server acquires the data to be analyzed, the data to be analyzed may be input into a pre-trained analysis model, so as to extract a feature representation corresponding to the data to be analyzed through the analysis model, and determine a program for performing a call operation on the data to be analyzed according to the extracted feature representation, and then determine the program for performing the call operation on the data to be analyzed according to the determined program.
In the above, the method for training the analysis model may be that each sample data is constructed, the sample data is input into the analysis model for each sample data, a program for performing a call operation on the sample data is determined by the analysis model, and the analysis model is trained as a program corresponding to the sample data with a goal of minimizing a deviation between the program corresponding to the sample data and the program for actually performing the call operation on the sample data as an optimization goal, where the sample data refers to at least a part of data in each log data acquired by the server and program information of the program for performing the call operation on the part of data.
The method for constructing each sample data may be that at least part of data in each log data of the history record is acquired as original sample data, a program for executing a calling operation on each original sample data is determined as a program corresponding to each original sample data, for any two original sample data, whether any two original sample data are matched is judged according to any two original sample data and program information of programs corresponding to any two original sample data, if yes, normalization processing is performed on the program information of the programs corresponding to the two original sample data, normalized program information is obtained, and the program corresponding to the normalized program information is used as the program corresponding to any two original sample data, so as to obtain each sample data.
It should be noted that, since the program information may refer to program names of programs, different program developers may name the same program with different program names, for example: the names of the methods for obtaining the user numbers can be multiple naming modes such as getUser _ id, getUser _ no, getuiserid and the like, and the naming modes are programs of the methods for obtaining the user codes substantially only because different developers have different naming habits.
Therefore, when it is determined that the difference between the two pieces of program information is small, and when it is determined that the similarity between the original sample data corresponding to the two pieces of program information is large, the server may perform normalization processing on the two pieces of program information to obtain normalized program information, and may further use both the two pieces of program corresponding to the normalized program information as the programs corresponding to the two pieces of original sample data.
In the above description, normalizing the program information is not really modifying the program information such as the program name of the program, but extracting the program information, normalizing the program information, and using the normalized program information and the original sample data as sample data.
Further, in the above content, the method for the server to determine whether any two original sample data are matched according to any two original sample data and the program information of the program corresponding to any two original sample data may be that an editing distance between the program information of the program corresponding to any two original sample data is determined by a preset editing distance algorithm, and if the determined editing distance between the two program information is smaller than a preset first threshold and the editing distance between the two original sample data is smaller than a preset second threshold, the two original sample data are considered to be matched.
In the above description, the edit distance is the minimum number of steps of single character editing (e.g., insertion, deletion, and replacement) required to convert one character string into another character string for any two character strings.
In addition, because the program corresponding to the determined original sample data can be used for training a preset analysis model, the number of the original sample data corresponding to each program should be balanced as much as possible, so that the effect of training the model to be analyzed is better, and therefore, the server can divide the program originally corresponding to a plurality of original sample data into a plurality of sub-programs, so that the difference between the number of the original sample data corresponding to each program is smaller.
Specifically, the server may further determine a program corresponding to each original sample data as each target program, where the program corresponding to the original sample data is a program for executing a call operation on the original sample data, and determine, for each target program, whether the number of each original sample data corresponding to the target program exceeds a preset third threshold, if so, split the target program into each subprogram, determine each original sample data corresponding to each subprogram, and construct each sample data according to the subprogram corresponding to each original sample data.
Further, the server may further determine, for each target program, whether the number of each original sample data corresponding to the target program is lower than a preset fourth threshold, and if so, fuse each original sample data corresponding to the target program and original sample data corresponding to other target programs whose number of original sample data is lower than the preset fourth threshold, and determine a program corresponding to each original sample data corresponding to the two target programs.
For example: assuming that the program getUseName corresponds to three original sample data with numbers 1 to 3 and the program sum corresponds to three original sample data with numbers 4 to 6, the three original sample data corresponding to the program getUserName and the three original sample data corresponding to the program sum may be regarded as one same set of original sample data, and getUserName and sum may be regarded as programs corresponding to the six original sample data.
In addition, because the original log data acquired by the server may be log data of different hardware devices and different software, the acquired log data formats are not uniform, and therefore, the server may perform normalization processing on the data formats of the data included in the original log data to obtain processed log data, and construct each sample data according to at least part of the data in the processed log data.
S104: from the respective log data, log data generated by executing the program is determined as respective candidate log data.
In this specification, the server may determine, from the log data, log data generated by the program that performs the call operation on the data to be analyzed as each candidate log data according to whether the program that determines the call operation on the data to be analyzed is consistent with the program corresponding to each log data.
It should be noted that the formats of the log data obtained by the server may not be uniform, and the obtained log data may include more interference data, for example: the storage location information of the log data, the storage time information of the log data, and the like, and these interference data may not have any effect when the server determines that the log data generated by a program for calling the data to be analyzed to perform the calling operation exists, but there is some interference, so that part of the log data may be extracted, and the information of the program calling data corresponding to the log data recorded in the log data is extracted in the form of key value pairs, for example: assuming that there is a log data that calls account password data of a user from a user table of data for an application, the log data can be extracted as the application that calls the data, and the account password data.
S106: and screening candidate log data generated by calling the data to be analyzed by the program from the candidate log data to serve as target log data, and performing data analysis on the data to be analyzed according to the target log data.
The server can extract the feature representation of the data to be analyzed and the feature representation of each candidate log data through the analysis model, screen out the candidate log data generated by calling the data to be analyzed by the program from each candidate log data according to the similarity between the feature representation of the data to be analyzed and the feature representation of each candidate log data to serve as target log data, and perform data analysis on the data to be analyzed according to the screened target log data.
It should be noted that the target log data screened by the server is the log data corresponding to the call operation for calling the data to be analyzed, and further, the detailed information of the call operation for calling the data to be analyzed may be determined according to the target log data, for example: the time of the call operation, the call method of the call operation, the information such as the data to be analyzed from which the call operation is called, and the like, and execute corresponding tasks according to the determined detailed information, for example: and analyzing whether the data to be analyzed has the possibility of data leakage in the calling process according to the determined detailed information, so that the possible places with the data leakage can be repaired, and the like.
In the above description, there may be many methods for the server to screen out the target log data from the candidate log data, for example: neighbor vector matching Algorithm (ANN), tree search algorithm, etc.
To further explain the above details, the present specification also provides a schematic diagram of a method for determining target log data, as shown in fig. 2.
Fig. 2 is a schematic diagram of a method for determining target log data provided in this specification.
As can be seen from fig. 2, after the server acquires the data to be analyzed, the server may perform data cleaning on the data to be analyzed, remove data such as messy codes in the data to be analyzed, and further input the cleaned data to be analyzed into a preset analysis model.
The method comprises the steps of analyzing a program for executing calling operation on data to be analyzed through a preset analysis model, extracting feature representations corresponding to the data to be analyzed, determining candidate logs from a large amount of log data according to determination, and determining target log data according to similarity between the extracted feature representations corresponding to the data to be analyzed and feature representations corresponding to the candidate log data extracted through the analysis model in advance.
As can be seen from the above, a program for executing a call operation on data to be analyzed can be determined by a preset analysis model, so that each candidate log data related to the analyzed program can be selected from a large amount of log data according to the analyzed program, the number of the log data to be screened can be reduced, target log data can be screened from each candidate log data, and the data to be analyzed can be analyzed according to the target log data.
The above method for data analysis provided for one or more embodiments of the present specification also provides a corresponding device for data analysis, as shown in fig. 3, based on the same idea.
Fig. 3 is a schematic diagram of a data analysis apparatus provided in the present specification, including:
an obtaining module 301, configured to obtain data to be analyzed;
a determining module 302, configured to determine a program for executing a call operation on the data to be analyzed;
a matching module 303, configured to determine, from the log data, log data generated by executing the program as candidate log data;
and the execution module 304 is configured to screen candidate log data generated by invoking the data to be analyzed by the program from the candidate log data, and perform data analysis on the data to be analyzed according to the target log data.
Optionally, the determining module 302 is specifically configured to input the data to be analyzed into a pre-trained analysis model, so as to determine, through the analysis model, a program for executing a call operation on the data to be analyzed.
Optionally, the apparatus further comprises: a training module 305;
the training module 305 is specifically configured to construct each sample data; inputting the sample data into the analysis model aiming at each sample data, and determining a program for executing calling operation on the sample data through the analysis model as a program corresponding to the sample data; and training the analysis model by taking the minimized deviation between the program corresponding to the sample data and the program for actually executing the calling operation on the sample data as an optimization target.
Optionally, the apparatus further comprises: a building module 306;
the constructing module 306 is specifically configured to, for any two original sample data, determine whether the any two original sample data are matched according to the any two original sample data and a program corresponding to the any two original sample data; if so, performing normalization processing on the program information of the programs corresponding to the any two original sample data to obtain normalized program information; and taking the program corresponding to the normalization program information as the program corresponding to any two original sample data to obtain each sample data.
Optionally, the constructing module 306 is specifically configured to determine a program corresponding to each original sample data as each target program; judging whether the quantity of each original sample data corresponding to each target program exceeds a preset threshold value or not for each target program; if so, splitting the target program into subprograms, and determining each original sample data corresponding to each subprogram; and constructing each sample data according to the subprogram corresponding to each original sample data.
Optionally, the building module 306 is specifically configured to obtain each original log data; normalizing the data format of the data contained in each original log data to obtain each processed log data; and constructing each sample data according to the processed log data.
Optionally, the executing module 304 is specifically configured to extract, through the analysis model, a feature representation of the data to be analyzed and feature representations of the candidate log data; and screening candidate log data generated by calling the data to be analyzed by the program from the candidate log data according to the similarity between the characteristic representation of the data to be analyzed and the characteristic representation of each candidate log data, and taking the candidate log data as target log data.
The present specification also provides a computer readable storage medium having stored thereon a computer program operable to perform a method of data analysis as provided above with respect to fig. 1.
This specification also provides a schematic block diagram of an electronic device corresponding to that of figure 1, shown in figure 4. As shown in fig. 4, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, but may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs the computer program to implement the method of data analysis of fig. 1. Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as ABEL (Advanced Boolean Expression Language), AHDL (alternate Hardware Description Language), traffic, CUPL (core universal Programming Language), HDCal, jhddl (Java Hardware Description Language), lava, lola, HDL, PALASM, rhyd (Hardware Description Language), and vhigh-Language (Hardware Description Language), which is currently used in most popular applications. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (10)
1. A method of data analysis, the method comprising:
acquiring data to be analyzed;
determining a program for executing calling operation on the data to be analyzed;
determining log data generated by executing the program from the log data as candidate log data;
and screening candidate log data generated by calling the data to be analyzed by the program from the candidate log data to serve as target log data, and performing data analysis on the data to be analyzed according to the target log data.
2. The method according to claim 1, wherein the step of determining the tag of the program for executing the calling operation on the data to be analyzed specifically comprises the steps of:
and inputting the data to be analyzed into a pre-trained analysis model so as to determine a program for executing calling operation on the data to be analyzed through the analysis model.
3. The method of claim 2, wherein training the analytical model specifically comprises:
constructing each sample data;
inputting the sample data into the analysis model aiming at each sample data, and determining a program for executing calling operation on the sample data through the analysis model as a program corresponding to the sample data;
and training the analysis model by taking the minimized deviation between the program corresponding to the sample data and the program for actually executing the calling operation on the sample data as an optimization target.
4. The method of claim 3, wherein constructing each sample datum specifically comprises:
aiming at any two original sample data, judging whether any two original sample data are matched according to any two original sample data and programs corresponding to any two original sample data;
if so, carrying out normalization processing on the program information of the programs corresponding to the any two original sample data to obtain normalized program information;
and taking the program corresponding to the normalization program information as the program corresponding to the any two original sample data to obtain each sample data.
5. The method of claim 3, wherein constructing each sample datum specifically comprises:
determining a program corresponding to each original sample data as each target program;
judging whether the quantity of each original sample data corresponding to each target program exceeds a preset threshold value or not for each target program;
if so, splitting the target program into subprograms, and determining each original sample data corresponding to each subprogram;
and constructing each sample data according to the subprogram corresponding to each original sample data.
6. The method of claim 3, wherein constructing each sample datum specifically comprises:
acquiring each original log data;
normalizing the data format of the data contained in each original log data to obtain each processed log data;
and constructing each sample data according to the processed log data.
7. The method according to claim 1, wherein the step of screening out candidate log data generated by the program calling the data to be analyzed from the candidate log data as target log data specifically comprises:
extracting the characteristic representation of the data to be analyzed and the characteristic representation of each candidate log data through the analysis model;
and screening candidate log data generated by calling the data to be analyzed by the program from the candidate log data according to the similarity between the characteristic representation of the data to be analyzed and the characteristic representation of each candidate log data, and taking the candidate log data as target log data.
8. An apparatus for data analysis, comprising:
the acquisition module is used for acquiring data to be analyzed;
the determining module is used for determining a program for executing calling operation on the data to be analyzed;
the matching module is used for determining log data generated by executing the program from the log data to serve as candidate log data;
and the execution module is used for screening out candidate log data generated by calling the data to be analyzed by the program from the candidate log data to serve as target log data, and performing data analysis on the data to be analyzed according to the target log data.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of any one of the preceding claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any of the preceding claims 1 to 7 when executing the program.
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