CN107391583B - Method and system for converting website login log information into vectorized data - Google Patents
Method and system for converting website login log information into vectorized data Download PDFInfo
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Abstract
The invention relates to the technical field of computer data mining, in particular to a method and a system for converting log information of website login into vectorized data, which comprises the following steps: sequentially reading each field information in the website login log information, and establishing a vector of the website login log information, wherein the position of data in the vector corresponds to each field position one by one: and if the data type of the read current field information is character type data, generating a sub-vector expressing the current character type data, and storing the sub-vector to a corresponding position of the current field information in the vector. In the invention, character type data is converted into the sub-vectors and then stored into the vectors corresponding to the website login log information, so that the dimension number of the vectorized data of the website login log information can be greatly increased, and the method is beneficial to the subsequent processes of machine learning and data mining modeling. The expressive force of the established model is improved, the degree of under-fitting is reduced, and the accuracy is improved.
Description
Technical Field
The invention relates to the technical field of computer data mining, in particular to a method and a system for converting website login log information into vectorized data.
Background
Log of website login: a website for providing service for the user, and recorded detailed information of the user login operation. Each time a user initiates a login operation, a log is generated. The log of website log generally includes time, number of continuous requests, whether to log in different places, log-in interface (from different interfaces of website, all can log in with the same account password), User ip address, User port number, User name, whether this log-in is successful, error number, UA (User Agent) used by User, User equipment id, request duration and other information.
In the website management process, each login operation is recorded by a log file. Our goal is to convert such log files into computer-acceptable vectorized data before the computer can perform the subsequent data mining and modeling processes. In the prior art, only continuous data and binary data in the log can be utilized, such as information of continuous request times, success or failure and the like. For character-type data: the login interface, the user ip address, the user port number, the error number, the UA used by the user, the user equipment id, the request duration and the like can not be used. When the website login log information is converted into vectorized data, only data type data can be discarded. The dimensionality of the generated vectorized data is too small, so that the phenomena of under-fitting and poor model description can be generated in the subsequent data mining process, and the accuracy of the model is influenced.
Disclosure of Invention
The technical problem to be solved by the invention is to overcome the defects of the prior art and provide a method and a system for converting website login log information into vectorized data, which can convert character type data in website login logs into vectorized data.
In order to achieve the above technical object, in one aspect, the present invention provides a method for converting website login log information into vectorized data, including:
sequentially reading information of each field in the log information of website login;
establishing a vector of the website login log information according to each field information and the data type thereof, wherein the positions of the data in the vector correspond to the positions of each field one by one, and aiming at any field information:
and if the data type of the read current field information is character type data, generating a sub-vector expressing the current character type data, and storing the sub-vector to a corresponding position of the current field information in the vector.
On the other hand, the system for converting the log information of the website login into the vectorized data is characterized by comprising the following steps:
the reading subsystem is used for sequentially reading information of each field in the log of website login;
the system comprises an establishing subsystem, a log logging subsystem and a log logging subsystem, wherein the establishing subsystem is used for establishing a vector of a website log according to information of each field and data type of the field, and the positions of data in the vector correspond to the positions of each field one by one; the method specifically comprises the following steps:
and the character unit is used for generating a sub-vector expressing the current character type data if the data type of the read current field information is character type data, and storing the sub-vector to the corresponding position of the current field information in the vector.
In the invention, character type data is converted into sub-vectors and then stored into the vectors corresponding to the log information of website login, so that the dimension number of vectorized data of the log information of website login is greatly increased. This is of great benefit to the subsequent process of machine learning and data mining modeling. The expressive force of the established model is improved, the degree of under-fitting is reduced, and the accuracy is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a system configuration according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a setup subsystem according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a character unit according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a binary unit according to an embodiment of the present invention;
FIG. 6 is a block diagram of a sub-vector module according to an embodiment of the present invention;
FIG. 7 is a block diagram of a method according to an embodiment of the present invention.
Detailed Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 and fig. 6, the method for converting log information of website login into vectorized data according to the present invention includes:
101. sequentially reading information of each field in the log information of website login;
102. establishing a vector of the website login log information according to each field information and the data type thereof, wherein the positions of the data in the vector correspond to the positions of each field one by one, and aiming at any field information:
1021. if the data type of the read current field information is character type data, generating a sub-vector expressing the current character type data, and storing the sub-vector to a corresponding position of the current field information in the vector; the method comprises the following specific steps:
generating an auxiliary data set of the current character type data, wherein the auxiliary data set lists all data under the attribute corresponding to the current character type data according to a specified sequence;
establishing an initial sub-vector of the current character type data, wherein all data in the initial sub-vector are 0, and the positions of the data in the initial sub-vector correspond to the data listed in the auxiliary data set one by one;
generating a sub-vector of the previous character type data according to the auxiliary data set and the initial sub-vector of the current character type data, which is as follows:
sequentially scanning each data in the auxiliary data set, and comparing the data with the current character type data;
if data different from the current character type data is scanned, scanning next data in the auxiliary data set;
if the same data as the current character type data is scanned, the data in the initial sub-vector at the corresponding position of the data is set to be 1, and the scanning is stopped, wherein the initial sub-vector at the moment is the sub-vector of the current character type data.
1022. And if the data type of the current field information is read to be continuous data, storing the current continuous data to the corresponding position of the current field information in the vector in a floating point number mode.
Since the continuation data itself appears as floating point numbers, the continuation data can be directly stored to the corresponding position of the current field in the vector.
1023. And if the data type of the current field information is read to be binary data, storing the current binary data to the corresponding position of the current field information in the vector in the form of floating point number.
If the value of the read binary data is true, storing 1 to the corresponding position of the current binary data in the vector;
and if the value of the read binary data is false, storing 0 to the corresponding position of the current binary data in the vector.
As shown in fig. 2 to 5, the system for converting log information of website login into vectorized data according to the present invention includes:
the reading subsystem 1 is used for sequentially reading information of each field in a website login log;
the establishing subsystem 2 is used for establishing a vector of the website login log according to the field information and the data type of the field information, and the positions of the data in the vector correspond to the positions of the fields one by one; the establishing subsystem 2 specifically includes:
the character unit 21 is configured to generate a sub-vector expressing the current character data if the data type of the read current field information is character data, and store the sub-vector to a corresponding position of the current field information in the vector.
Preferably, the establishing subsystem 2 may further include:
a continuous unit 22, configured to store the current continuous data in the form of floating point number to a corresponding position of the current field information in the vector if the data type of the read current field information is continuous data;
the binary unit 23 is configured to, if the data type of the read current field information is binary data, store the current binary data in the form of floating point number to a corresponding position of the current field information in the vector.
In a specific implementation, one possible structure of the character unit 21 includes:
an auxiliary module 211, configured to generate an auxiliary data set of the current character-type data, where the auxiliary data set lists all data under the attribute corresponding to the current character-type data according to a specified sequence;
an initial module 212, configured to establish an initial sub-vector of the current character-type data, where all data in the initial sub-vector is 0, and the positions of the data in the initial sub-vector correspond to the data listed in the auxiliary data set one by one;
a sub-vector module 213, configured to generate a sub-vector of the previous character-type data according to the auxiliary data set and the initial sub-vector of the current character-type data.
In a specific implementation, one possible structure of the sub-vector module 213 includes:
a comparison submodule 2131, configured to scan each piece of data in the auxiliary data set in sequence, and compare the scanned piece of data with the current character type data;
a skip submodule 2132, configured to scan next data in the auxiliary data set if data different from the current character data is scanned;
the changing sub-module 2133 is configured to, when the same data as the current character-type data is scanned, set the data in the initial sub-vector at the position corresponding to the data to 1, and stop the scanning, where the initial sub-vector at this time is the sub-vector of the current character-type data.
In a specific implementation, one possible structure of the binary unit 23 includes:
a true value module 231, configured to store 1 to a corresponding position of the current binary data in the vector if the value of the read binary data is true;
a false value module 232, configured to store 0 to a corresponding position of the current binary data in the vector if the value of the read binary data is false.
For example, we take a log of a day for a certain website. Each login results in a record with the following information: time, the number of continuous requests, whether to log in from different places, a login interface (different interfaces of a website can log in by the same account password), information such as ip address of a user, port number of the user, user name, success or failure of the login, error number, UA used by the user, user equipment id, request duration and the like.
Take 3 fields in the log as an example: the number of consecutive requests (consecutive data), whether to register to another place (binary data), and whether to register to an interface (character data).
Firstly, establishing an auxiliary data set to obtain all possible values of a login interface: sms, web, h5 and mail, store these 4 data into the dataset with the login interface as the main key.
Then establishing a corresponding initial subvector B, namely {0,0,0,0 };
if the current weblog information is: 24| true | h5, the fields are converted in order:
firstly, the number of continuous requests is 24, which is continuous data, a vector A of website login log information is newly established, 24 is directly stored in a new dimension of A, and A can be represented as {24 };
the next field is: whether the data is remotely logged in is binary data, and the value is true. At this time, a new dimension is created in the vector a, and the position of the new dimension corresponds to the position of "true" in the blog information, and 1 is stored therein. At this time, vector a: {24,1 };
the next field is the login interface and is character type data, and then the information with the login interface as the main key is found in the auxiliary data set, and 4 data of sms, web, h5 and mail are returned in sequence. H5 in the original field is compared with the returned information one by one, and the data is found to be consistent with the 3 rd data; the value of the third data in the initial sub-vector B is then changed to 1, at which time the initial sub-vector B is generated as sub-vector B: {0,0,1,0}.
Sub-vector B is then stored in vector A, the stored location corresponding to the location of "h 5" in the weblog; namely vector a: {24,1,0,0,1,0}. Therefore, the network log information is successfully converted into the representation of vectorized data, and the subsequent steps of data mining and the like are possible.
When the log information is converted into the vectorization data, the character type data is utilized, so that the data quantity of the vectorization data obtained by the user is greatly improved. The method is beneficial to the subsequent process of machine learning and data mining modeling, and can improve the model expression, reduce the degree of fitting deficiency, improve the accuracy and the like.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. To those skilled in the art; various modifications to these embodiments will be readily apparent, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Those of skill in the art will further appreciate that the various illustrative logical blocks, units, and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, elements, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design requirements of the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.
The various illustrative logical blocks, or elements, described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a user terminal. In the alternative, the processor and the storage medium may reside in different components in a user terminal.
In one or more exemplary designs, the functions described in the embodiments of this invention may be implemented in hardware, software, firmware, or any combination thereof, if implemented in software, these functions may be stored on a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium including a computer storage medium and a communications medium that facilitates transfer of a computer program from one place to another.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method for converting website login log information into vectorized data, the method comprising:
sequentially reading information of each field in the log information of website login;
establishing a vector of the website login log information according to each field information and the data type thereof, wherein the positions of the data in the vector correspond to the positions of each field one by one, and aiming at any field information:
if the data type of the read current field information is character type data, generating a sub-vector expressing the current character type data, and storing the sub-vector to a corresponding position of the current field information in the vector;
if the data type of the current field information is read to be continuous data, storing the current continuous data to the corresponding position of the current field information in the vector in the form of floating point number;
and if the data type of the current field information is read to be binary data, storing the current binary data to the corresponding position of the current field information in the vector in the form of floating point number.
2. The method of claim 1, wherein the generating of the subvectors expressing the current character-type data specifically comprises:
generating an auxiliary data set of the current character type data, wherein the auxiliary data set lists all data under the attribute corresponding to the current character type data according to a specified sequence;
establishing an initial sub-vector of the current character type data, wherein all data in the initial sub-vector are 0, and the positions of the data in the initial sub-vector correspond to the data listed in the auxiliary data set one by one;
and generating a sub-vector of the previous character type data according to the auxiliary data set and the initial sub-vector of the current character type data.
3. The method according to claim 2, wherein the generating the sub-vector of the previous character-type data according to the auxiliary data set and the initial sub-vector of the current character-type data comprises:
sequentially scanning each data in the auxiliary data set, and comparing the data with the current character type data;
if data different from the current character type data is scanned, scanning next data in the auxiliary data set;
if the same data as the current character type data is scanned, the data in the initial sub-vector at the corresponding position of the data is set to be 1, and the scanning is stopped, wherein the initial sub-vector at the moment is the sub-vector of the current character type data.
4. The method according to claim 1, wherein if the data type of the read current field information is binary data, storing the current binary data in a floating point manner to a corresponding position of the current field information in a vector, specifically comprising:
if the value of the read binary data is true, storing 1 to the corresponding position of the current binary data in the vector;
and if the value of the read binary data is false, storing 0 to the corresponding position of the current binary data in the vector.
5. A system for converting website login log information into vectorized data, the system comprising:
the reading subsystem is used for sequentially reading information of each field in the log of website login;
the system comprises an establishing subsystem, a log logging subsystem and a log logging subsystem, wherein the establishing subsystem is used for establishing a vector of a website log according to information of each field and data type of the field, and the positions of data in the vector correspond to the positions of each field one by one; the establishing subsystem specifically comprises:
the character unit is used for generating a sub-vector expressing the current character type data if the data type of the read current field information is character type data, and storing the sub-vector to a position corresponding to the current field information in the vector;
the continuous unit is used for storing the current continuous data to the corresponding position of the current field information in the vector in a floating point number mode if the data type of the read current field information is the continuous data;
and the binary unit is used for storing the current binary data to the corresponding position of the current field information in the vector in a floating point number mode if the data type of the read current field information is the binary data.
6. The system for converting website login log information into vectorized data according to claim 5, wherein the character unit comprises:
the auxiliary module is used for generating an auxiliary data set of the current character type data, and the auxiliary data set lists all data under the attribute corresponding to the current character type data according to a specified sequence;
the device comprises an initial module, a data processing module and a data processing module, wherein the initial module is used for establishing an initial sub-vector of current character type data, all data in the initial sub-vector are 0, and the positions of the data in the initial sub-vector correspond to the data listed in an auxiliary data set one by one;
and the sub-vector module is used for generating a sub-vector of the previous character type data according to the auxiliary data set and the initial sub-vector of the current character type data.
7. The system for converting website login log information into vectorized data according to claim 6, wherein the sub-vector module comprises:
the comparison submodule is used for scanning each data in the auxiliary data set in sequence and comparing the data with the current character type data respectively;
the skip submodule is used for scanning the next data in the auxiliary data set if the data different from the current character type data is scanned;
and the change sub-module is used for setting the data in the initial sub-vector at the position corresponding to the data to be 1 and stopping scanning if the same data as the current character type data is scanned, wherein the initial sub-vector at the moment is the sub-vector of the current character type data.
8. The system for converting website login log information into vectorized data according to claim 7, wherein the binary unit comprises:
the truth value module is used for storing 1 to the corresponding position of the current binary data in the vector if the value of the read binary data is true;
and the false value module is used for storing 0 to the corresponding position of the current binary data in the vector if the value of the read binary data is false.
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