CN116662348A - Financial database index construction method, device, equipment and storage medium - Google Patents
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Abstract
The application relates to a financial database index construction method which can be applied to the fields of financial science and technology and related fields. The method comprises the following steps: acquiring the data quantity of target data in the searching range of the data searching tool in the financial database and acquiring data attributes contained in the target data under the condition that the data searching tool aiming at the financial database is identified as the target data searching tool, and acquiring the quantity of each attribute value in each data attribute; obtaining information gain corresponding to each data attribute based on the data quantity and the data quantity, and screening out target data attributes from the data attributes based on the information gain; obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute; and searching the database index of the financial database by using the initial index data attribute as a data searching tool. The method can be used for efficiently constructing the financial database index.
Description
Technical Field
The present application relates to the technical field of financial databases, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for constructing a financial database index.
Background
With the development of the technical field of financial databases, a financial database data searching technology is developed, and the technology searches target data from a financial database based on a database index constructed in advance by staff through a data searching tool corresponding to the financial database.
In the above technical solution, as the financial service changes, the manner of increasing the service data volume changes, which may cause the problem of low access efficiency because the original index is no longer applicable, and at this time, a new index needs to be reconstructed, and the construction efficiency of the database index is low because a new database index is constructed manually.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a financial database index construction method, apparatus, computer device, computer-readable storage medium, and computer program product that are capable of efficiently constructing a financial database index.
In a first aspect, the present application provides a method for constructing a financial database index. The method comprises the following steps:
Under the condition that a data searching tool aiming at a financial database is identified as a target data searching tool, acquiring the data quantity of target data in the searching range of the data searching tool in the financial database, acquiring data attributes contained in the target data, and acquiring the quantity of each attribute value in each data attribute; the target data searching tool is a data searching tool with a lower efficiency value than a preset efficiency value;
obtaining information gain corresponding to each data attribute based on the data quantity and the quantity, and screening target data attributes from the data attributes based on the information gain;
obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute;
and searching the database index of the financial database by using the initial index data attribute as the data searching tool.
In one embodiment, after searching the database index of the financial database as the data searching tool, the initial index data attribute further includes: acquiring a target index data attribute from the current data attribute corresponding to the data attribute under the condition that the data searching tool is identified as a target data searching tool again; the current data attribute is other data attributes except the initial index data attribute in the data attributes; and taking the initial index data attribute and the target index data attribute as new initial index data attributes, and returning to execute the step of searching the database index of the financial database by taking the initial index data attribute as the data searching tool until the data searching tool is not identified as the target data searching tool.
In one embodiment, the obtaining the target index data attribute from the current data attribute corresponding to the data attribute includes: acquiring the data quantity of current data corresponding to the current data attribute in the target data, and acquiring the quantity of each attribute value in the current data attribute; obtaining information gain corresponding to each current data attribute based on the data quantity of the current data and the quantity of each attribute value in the current data attribute; and acquiring a target index data attribute from the current data attribute based on the information gain corresponding to the current data attribute.
In one embodiment, the obtaining, based on the data amount and the number, an information gain corresponding to each data attribute includes: based on the data quantity and the quantity, obtaining information entropy and weight corresponding to each attribute value in the data attribute; and obtaining the information gain corresponding to the data attribute according to the information entropy and the weight corresponding to each attribute value in the data attribute.
In one embodiment, the screening the target data attribute from the data attributes based on the information gain includes: acquiring an average value of the information gain; and screening the data attribute with the information gain larger than the average value from the data attributes as the target data attribute.
In one embodiment, the obtaining the information gain ratio of each target data attribute according to the information gain of each target data attribute includes: acquiring an inherent value of the target data attribute; the inherent value is used for representing the variety number of the attribute value in the target data attribute; and obtaining the information gain rate of the target data attribute based on the ratio of the information gain to the inherent value.
In one embodiment, after the constructing the database index corresponding to the financial database, the method further includes: transmitting the database index to the data lookup tool in response to a data lookup command of the data lookup tool; and according to the obtained searching result of the data searching tool based on the database index, sending the data corresponding to the searching result to a data searching terminal corresponding to the data searching tool.
In a second aspect, the application further provides a financial database index construction device. The device comprises:
the data volume acquisition module is used for acquiring the data volume of target data in the searching range of the data searching tool in the financial database and acquiring data attributes contained in the target data and the number of each attribute value in the data attributes under the condition that the data searching tool aiming at the financial database is identified as the target data searching tool; the target data searching tool is a data searching tool with a lower efficiency value than a preset efficiency value;
The target data attribute acquisition module is used for acquiring information gain corresponding to each data attribute based on the data quantity and the quantity, and screening target data attributes from the data attributes based on the information gain;
the index data attribute module is used for obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute;
and the database index construction module is used for searching the database index of the financial database by taking the initial index data attribute as the data searching tool.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
under the condition that a data searching tool aiming at a financial database is identified as a target data searching tool, acquiring the data quantity of target data in the searching range of the data searching tool in the financial database, acquiring data attributes contained in the target data, and acquiring the quantity of each attribute value in each data attribute; the target data searching tool is a data searching tool with a lower efficiency value than a preset efficiency value;
Obtaining information gain corresponding to each data attribute based on the data quantity and the quantity, and screening target data attributes from the data attributes based on the information gain;
obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute;
and searching the database index of the financial database by using the initial index data attribute as the data searching tool.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
under the condition that a data searching tool aiming at a financial database is identified as a target data searching tool, acquiring the data quantity of target data in the searching range of the data searching tool in the financial database, acquiring data attributes contained in the target data, and acquiring the quantity of each attribute value in each data attribute; the target data searching tool is a data searching tool with a lower efficiency value than a preset efficiency value;
Obtaining information gain corresponding to each data attribute based on the data quantity and the quantity, and screening target data attributes from the data attributes based on the information gain;
obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute;
and searching the database index of the financial database by using the initial index data attribute as the data searching tool.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
under the condition that a data searching tool aiming at a financial database is identified as a target data searching tool, acquiring the data quantity of target data in the searching range of the data searching tool in the financial database, acquiring data attributes contained in the target data, and acquiring the quantity of each attribute value in each data attribute; the target data searching tool is a data searching tool with a lower efficiency value than a preset efficiency value;
Obtaining information gain corresponding to each data attribute based on the data quantity and the quantity, and screening target data attributes from the data attributes based on the information gain;
obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute;
and searching the database index of the financial database by using the initial index data attribute as the data searching tool.
The method, the device, the computer equipment, the storage medium and the computer program product for constructing the financial database index acquire the data quantity of target data in the searching range of the data searching tool in the financial database and acquire the data attribute contained in the target data and acquire the quantity of each attribute value in each data attribute under the condition that the data searching tool aiming at the financial database is identified as the target data searching tool; obtaining information gain corresponding to each data attribute based on the data quantity and the data quantity, and screening out target data attributes from the data attributes based on the information gain; obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute; and searching the database index of the financial database by using the initial index data attribute as a data searching tool. According to the application, the data quantity of the target data corresponding to the data searching tool in the financial database is obtained, the number of each attribute value in the data attribute of the target data is obtained, then the information gain corresponding to each data attribute is obtained based on the data quantity of the target data and the number of each attribute value, then the target data attribute is screened out from the data attribute according to the information gain, the information gain rate of the target data attribute is obtained, and finally the financial database index can be constructed efficiently according to the information gain rate.
Drawings
FIG. 1 is a flow chart of a method for constructing a financial database index in one embodiment;
FIG. 2 is a flow diagram of further building a financial database index in one embodiment;
FIG. 3 is a flow diagram of obtaining a target index data attribute in one embodiment;
FIG. 4 is a flow chart of acquiring information gain corresponding to a data attribute in an embodiment;
FIG. 5 is a block diagram of a financial database index building apparatus in one embodiment;
fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that, the term "first\second" related to the embodiment of the present application is merely to distinguish similar objects, and does not represent a specific order for the objects, it is to be understood that "first\second" may interchange a specific order or precedence where allowed. It is to be understood that the "first\second" distinguishing aspects may be interchanged where appropriate to enable embodiments of the application described herein to be implemented in sequences other than those illustrated or described.
In one embodiment, as shown in fig. 1, a method for constructing a financial database index is provided, and this embodiment is illustrated by applying the method to a server, it is understood that the method may also be applied to a terminal, and may also be applied to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
step S101, under the condition that the data searching tool aiming at the financial database is identified as a target data searching tool, acquiring the data quantity of target data in the searching range of the data searching tool in the financial database, acquiring data attributes contained in the target data, and acquiring the quantity of each attribute value in each data attribute; the target data lookup tool is a data lookup tool below a preset efficiency value.
The financial database is a database server storing financial business data, the data searching tool is a database data searching tool based on SQL sentences corresponding to the financial database, and the target data searching tool is the data searching tool with the data searching efficiency value not meeting a preset value, wherein the data searching efficiency value is obtained by calculating a searching record in the process of searching the financial database based on the data searching tool, for example, the searching record can be time consuming per thousand lines of records, average execution time and the like. Then, the target data is the data in the searching range of the data searching tool in the financial database, wherein the searching range is the range of each data searching tool capable of searching the data in the financial database, and the data quantity is the quantity of the target data. Finally, the data attribute is various attributes of the target data, for example, in the financial database of the application, the attribute of the target data can be a partition, a region number, a business date, an amount, a merchant type, a time stamp and the like, and the attribute value is a value of the data attribute, wherein each data attribute has one or more values from the dimension of the data attribute, for example, the data attribute of "business date" can be a plurality of different dates, but from the dimension of the target data, for example, the attribute value of the data attribute of a certain target data only has one value, for example, the data attribute of "business date" of a certain target data only has one value.
Specifically, acquiring a searching record in the process of searching the financial database by the data searching tool, acquiring a searching efficiency value of the data searching tool based on the searching record, judging that the data searching tool is a target data searching tool if the efficiency value does not meet a preset value, finally acquiring the data quantity of target data in the searching range of the data searching tool in the financial database, acquiring data attributes contained in the target data, and acquiring the quantity of each attribute value in each data attribute. For example, as shown in table 1, the number of the target data D is 100, the data attributes are respectively a-partition, b-area code, c-date, D-currency, e-amount, f-merchant type and g-timestamp, the attribute a (V) =3 represents that the data attribute "partition" has 3 types of attribute values which are respectively a1, a2 and a3, wherein a1=10 represents that the target data of the attribute value a1 has 10, and the number of the a1, a2 and a3 is just 100.
| Target data d=100 | a-partition | b-area number | c-date | d-currency of | e-amount of money | f-merchant type | g-time stamp |
| Number of attributes=7 | al＝10 | b1＝10 | c1＝5 | d1＝15 | e1＝8 | f1＝10 | g1＝1 |
| Attribute a (V) =3 | a2＝30 | b2＝30 | c2＝5 | d2＝15 | e2＝10 | f2＝10 | g2＝1 |
| Attribute b (V) =3 | a3＝60 | b3＝60 | …… | d3＝20 | e3＝2 | f3＝8 | …… |
| Attribute c (V) =20 | c19＝5 | d4＝25 | e4＝4 | f4＝8 | g99＝1 | ||
| Attribute d (V) =5 | c20＝5 | d5＝25 | e5＝1 | …… | g100＝1 | ||
| Attribute e (V) =80 | e6＝1 | f11＝8 | |||||
| Attribute f (V) =12 | …… | f12＝8 | |||||
| Attribute g (V) =100 | e79＝1 | ||||||
| e80＝1 |
TABLE 1 target data attribute value statistics
Step S102, based on the data quantity and the data quantity, obtaining the information gain corresponding to each data attribute, and based on the information gain, screening out the target data attribute from the data attributes.
The information gain is asymmetric in probability theory and information theory, and is used for measuring the difference of two probability distributions P and Q, the information gain describes the difference of the codes by using P when the codes are coded by using Q, in the application, the information gain is specifically used for representing the reasonable degree of index classification of the target data by a certain data attribute, and the target data attribute is the data attribute meeting the preset condition based on the information gain.
Specifically, through the data volume of the target data and the number of the attribute values, the information gain corresponding to each data attribute can be obtained through an information gain calculation formula, and the target data attribute meeting the preset condition is screened from the data attributes based on the information gain.
For example, suppose that data attribute a has V possible values { a } 1 ,a 2 ,...,a v If a is used to divide the target data D, V branch nodes are generated, wherein the V branch nodes include all the data attributes a in D having a value of a v Is denoted as D v . First calculate D v In (2) and giving weight |D to the branch nodes in consideration of the difference of the number of samples contained in different branch nodes v The influence of the branch point of the larger number of samples is larger, |/|d|, and thus the information gain obtained by dividing the target data D by the data attribute a can be calculated as followsThe following is shown:
from the above, the information gain of each data attribute can be calculated as: gain (D, a) =0.55, gain (D, b) =0.55, gain (D, c) =0.78, gain (D, D) =0.53, gain (D, e) =0.88 Gain (D, f) =0.70, gain (D, g) =0.93, three data attributes c, e, g satisfying a preset condition are selected as target data attributes.
Step S103, according to the information gain of each target data attribute, obtaining the information gain rate of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute.
The information gain rate is a ratio of the information gain to an eigenvalue of the data attribute, wherein the eigenvalue is positively correlated with the number of attribute values of the data attribute, and the information gain rate is also used for representing a reasonable degree of index classification of the target data by a certain data attribute, but the data attribute with a large number of information gain preference attribute values can cause inaccurate reasonable degree of index classification of the target data by the certain data attribute, and the information gain rate removes the characteristic of the data attribute with a large number of information gain preference attribute values, so that the information gain rate of each target data attribute needs to be obtained according to the information gain of each target data attribute, and the target data attribute with the largest information gain rate is used as the initial index data attribute.
Specifically, according to the information gain of each target data attribute, the data amount of the target data and the number of the attribute values, the information gain rate of each target data attribute is obtained, and the target data attribute with the maximum information gain rate is used as the initial index data attribute.
For example, since the information gain has a preference for a larger number of data attributes, the gain ratio in the C4.5 decision tree algorithm is further introduced here, and is defined as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,referred to as the intrinsic value of data attribute a. The greater the number of possible values of data attribute a (i.e., the greater V), the greater the value of IV (a) will generally be. It should be noted that the gain criterion has a preference for the data attribute with a smaller number of available values, so that the target data attribute with higher information gain than the average level is found from the data attributes, and then the data attribute with the highest gain is selected as the initial index data attribute.
By the above equation, the information gain ratio of each target data attribute can be calculated as: gain ratio (D, c) =0.18, gain ratio (D, e) =0.15, gain ratio (D, g) =0.14, and the maximum target data attribute c is the initial index data attribute.
Step S104, the initial index data attribute is used as a data searching tool to search the database index of the financial database.
The database index is an index of the financial database, and the database index is used for quickly searching data required in the financial database.
Specifically, the initial index data attribute is a database index for searching the financial database for the data searching tool.
In the above method for constructing the financial database index, under the condition that the data searching tool aiming at the financial database is identified as the target data searching tool, acquiring the data quantity of target data in the searching range of the data searching tool in the financial database, acquiring the data attribute contained in the target data, and acquiring the quantity of each attribute value in each data attribute; obtaining information gain corresponding to each data attribute based on the data quantity and the data quantity, and screening out target data attributes from the data attributes based on the information gain; obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute; and searching the database index of the financial database by using the initial index data attribute as a data searching tool. According to the application, the data quantity of the target data corresponding to the data searching tool in the financial database is obtained, the number of each attribute value in the data attribute of the target data is obtained, then the information gain corresponding to each data attribute is obtained based on the data quantity of the target data and the number of each attribute value, then the target data attribute is screened out from the data attribute according to the information gain, the information gain rate of the target data attribute is obtained, and finally the financial database index can be constructed efficiently according to the information gain rate.
In one embodiment, as shown in fig. 2, after searching the database index of the financial database using the initial index data attribute as a data searching tool, the method further comprises the following steps:
step S201, in the case that the data searching tool is identified as the target data searching tool again, acquiring a target index data attribute from the current data attribute corresponding to the data attribute; the current data attribute is other data attributes than the initial index data attribute among the data attributes.
The current data attribute is a data attribute except the initial index data attribute in the data attributes of the target data, and the target index data attribute is used for constructing the data attribute of the database index.
Specifically, in the case where the data search tool is identified again as the target data search tool based on the above database index, the target index data attribute is acquired from the current data attributes other than the initial index data attribute. For example, as shown in Table 1, the number of the target data D is 100, the data attributes are a-partition, b-area code, c-date, D-currency, e-amount, f-merchant type, and g-timestamp, respectively, wherein the data attribute c-date is the initial index data attribute, and the data attribute c-date is a-partition, b-area code, D-currency, e-amount, f-merchant type, and g-timestamp.
Step S202, the initial index data attribute and the target index data attribute are used as new initial index data attributes, and the step of searching the database index of the financial database by using the initial index data attributes as the data searching tool is performed in a return mode until the data searching tool is not identified as the target data searching tool.
Specifically, adding the target index data attribute into the database index to form a new database index, and checking whether the data searching tool is the target data searching tool again on the basis of the new database index until the data searching tool is not identified as the target data searching tool, and stopping forming the new database index.
In this embodiment, by checking whether the data searching tool is the target data searching tool based on the new database index until the data searching tool is not identified as the target data searching tool, the formation of the new database index is stopped, so that the finally obtained data searching tool is not the target data searching tool.
In one embodiment, as shown in fig. 3, the method for obtaining the target index data attribute from the current data attribute corresponding to the data attribute includes the following steps:
Step S301, acquiring the data amount of the current data corresponding to the current data attribute in the target data, and acquiring the number of each attribute value in the current data attribute.
The current data is the data of the target data except the data corresponding to the initial index data attribute, that is, the current data is the data of the target data which does not contain the initial index data attribute.
Specifically, the current data of the target data which does not contain the initial index data attribute is obtained, the data quantity of the current data is obtained, and the number of each attribute value in the current data attribute is obtained.
Step S302, based on the data amount of the current data and the number of each attribute value in the current data attribute, obtaining the information gain corresponding to each current data attribute.
Specifically, based on the data amount of the current data and the number of each attribute value in the current data attribute, the information entropy corresponding to the attribute value of each current data attribute is obtained, and based on the information entropy, the information gain corresponding to each current data attribute is obtained.
Step S303, acquiring the target index data attribute from the current data attribute based on the information gain corresponding to the current data attribute.
Specifically, an average value of information gains corresponding to the current data attribute is obtained, a data attribute larger than the average value in the current data attribute is obtained, an information gain rate of the data attribute larger than the average value is obtained, and the data attribute with the largest information gain rate is used as a target index data attribute.
In this embodiment, the target index data attribute can be more reasonably obtained from the current data attribute by calculating the data amount of the current data and the number of each attribute value in the current data attribute based on the principle of information gain.
In one embodiment, as shown in fig. 4, based on the data amount and the data number, the information gain corresponding to each data attribute is obtained, which includes the following steps:
step S401, based on the data quantity and the data quantity, obtaining the information entropy and the weight corresponding to each attribute value in the data attribute.
The information entropy corresponding to each attribute value is uncertainty of each attribute value as a unique attribute value of the data attribute, and the weight refers to a weight coefficient of each attribute value in a certain data attribute.
Specifically, the proportion of the attribute of the kth class data in the current target data D is assumed to be P k (k=1, 2, |y|), the information entropy of D is defined as follows:
And obtaining the information entropy corresponding to each attribute value in the data attribute based on the data quantity and the quantity by the calculation formula of the information entropy, and obtaining the weight corresponding to each attribute value in the data attribute based on the data quantity, the quantity and the information entropy.
Based on the data in table 1, for example, for data attribute a, |y|=1, according to->The entropy of the attribute values a1, a2, a3 can be calculated as:
step S402, according to the information entropy and the weight corresponding to each attribute value in the data attribute, obtaining the information gain corresponding to the data attribute.
Specifically, assume that data attribute a has V possible attribute values { a } 1 ,a 2 ,...,a v If a is used to divide the target data D, V branch nodes are generated, wherein the V branch nodes include all the data attributes a in D having a value of a v Is denoted as D v . First calculate D v In (2) and giving weight |D to the branch nodes in consideration of the difference of the number of samples contained in different branch nodes v The influence of the branch point of the larger number of samples is larger, |/|d|, and thus the information gain obtained by dividing the target data D with the data attribute a can be calculated:and according to the information entropy and the weight corresponding to each attribute value in the data attribute, obtaining the information gain corresponding to the data attribute.
In this embodiment, the information entropy and the weight corresponding to each attribute value in the data attribute are calculated through the data quantity and the data quantity, and then the information gain corresponding to the data attribute is further calculated, so that the information gain corresponding to the data attribute can be accurately calculated.
In one embodiment, the screening of the target data attributes from the data attributes based on the information gain includes the steps of:
acquiring an average value of the information gain; and screening the data attributes with information gain larger than the average value from the data attributes, and taking the data attributes as target data attributes.
Wherein the average value is an average information gain value of the individual data attribute information gains.
Specifically, an average information gain value of the information gain of each data attribute is calculated, and a data attribute having an information gain greater than the average value among the data attributes is taken as a target data attribute.
In this embodiment, by calculating the average information gain value of the information gain of each data attribute, and using the data attribute with the information gain greater than the average value as the target data attribute, a batch of target data attributes which can be more reasonably used as the database index in the data attributes can be screened out.
In one embodiment, the information gain ratio of each target data attribute is obtained according to the information gain of each target data attribute, and the method comprises the following steps:
Acquiring an inherent value of a target data attribute; the intrinsic value is used for representing the variety number of the attribute values in the target data attribute; and obtaining the information gain rate of the target data attribute based on the ratio of the information gain to the eigenvalue.
Wherein the intrinsic value is used for representing the category number of the attribute value in the target data attribute.
Specifically, an intrinsic value of the target data attribute is acquired, and a ratio of the information gain to the intrinsic value is taken as an information gain rate of the target data attribute.
In this embodiment, by acquiring the eigenvalue of the target data attribute and taking the ratio of the information gain to the eigenvalue as the information gain rate of the target data attribute, the information gain rate of the target data attribute can be accurately obtained.
In one embodiment, after constructing the database index corresponding to the financial database, the method further comprises the following steps:
transmitting the database index to the data lookup tool in response to a data lookup command of the data lookup tool; and sending the data corresponding to the search result to a data search terminal corresponding to the data search tool according to the search result obtained by the data search tool based on the database index.
The data searching command is a target data searching command carried by the data searching tool, the data searching command is generated by a data searching terminal and is written into the data searching tool, the data searching terminal is a data demand terminal aiming at a financial database, and the searching result refers to target data searched by the data searching tool based on a database index.
Specifically, the data searching terminal generates a data searching tool carrying a data searching command and sends the data searching tool to the financial database server, then the financial database server responds to the data searching command of the data searching tool and sends a database index to the data searching tool, the data searching tool sends a searching result to the financial database server based on the obtained searching result of the database index, and the financial database server sends data corresponding to the searching result to the data searching terminal corresponding to the data searching tool according to the searching result.
In this embodiment, the data searching tool sends the data corresponding to the searching result to the data searching terminal corresponding to the data searching tool based on the searching result obtained by the database index, so that the data searching terminal can accurately obtain the required target data from the financial database.
In an application embodiment, a method for constructing a financial database index is provided, which specifically includes the following steps:
1. sample data acquisition. In the case where it is determined that the data search tool for the financial database is an inefficient data search tool, the target data and the data attributes of the target data involved in the financial database by the data search tool are acquired, for example: the number of table records, field names, data attribute values, the number of records corresponding to the attribute values, primary keys, indexes, and the like. In particular, for a data lookup tool without hit index, full-scale data is acquired as target data (full-scale data is particularly large, and then partial samples are randomly extracted at a certain ratio). For a data lookup tool that hits a partial index, the largest subset of hit indexes is obtained as target data.
2. And introducing a C4.5 decision tree algorithm to realize self-adaptive learning generation of a database index.
Firstly, the gain ratio of the data attribute of the target data is calculated, and in the process of dividing the data attribute by using the decision tree, samples contained in the branch nodes are expected to belong to the same category as much as possible, namely, the purity of the nodes is higher and higher. Information entropy, which is one of the most commonly used indicators for measuring the purity of target data, is introduced here as a representative. Assuming that the proportion of the attribute of the kth class data in the current target data D is P k (k=1, 2, |y|), the information entropy of D is defined as follows, and the smaller the value of Ent (D), the higher the purity of D.
Suppose that there are V possible values { a } for data attribute a 1 ,a 2 ,...,a v If a is used to divide the target data D, V branch nodes are generated, wherein the V branch nodes include all the data attributes a in D having a value of a v Is denoted as D v . First calculate D v In (2) and giving weight |D to the branch nodes in consideration of the difference of the number of samples contained in different branch nodes v I/i D i, i.e., the greater the impact of branching nodes with a greater number of samplesThe information gain obtained by dividing the target data D with the data attribute a can then be calculated as follows:
In general, the larger the information gain, the greater the purity improvement obtained by dividing using the data attribute a. Since the information gain has a preference for the data attribute with a larger number of available values, the gain ratio in the C4.5 decision tree algorithm is further introduced here, and is defined as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,referred to as the intrinsic value of data attribute a. The greater the number of possible values of data attribute a (i.e., the greater V), the greater the value of IV (a) will generally be. It should be noted that the gain criterion has a preference for the data attribute with a smaller number of available values, so that the target data attribute with higher information gain than the average level is found from the data attributes, and then the data attribute with the highest gain is selected as the database index.
3. After each round of self-adaptive learning is completed, selecting an index data attribute which can be used as a database index, then using the index data attribute as a new index to access a financial database, calculating related index characteristic item information, judging whether a data searching tool is low-efficiency or not on the basis of the new index, if so, continuing the steps until the data searching tool is not low-efficiency, and stopping the next round of division.
In this embodiment, the data amount of the target data corresponding to the data searching tool in the financial database is obtained, the number of each attribute value in the data attribute of the target data is obtained, then the information gain corresponding to each data attribute is obtained based on the data amount of the target data and the number of each attribute value, then the target data attribute is selected from the data attributes according to the information gain, the information gain rate of the target data attribute is obtained, and finally the financial database index can be efficiently constructed according to the information gain rate.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a financial database index construction device for realizing the above related financial database index construction method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the apparatus for constructing a financial database index provided below may be referred to the limitation of the method for constructing a financial database index hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 5, there is provided a financial database index construction apparatus, comprising: a data volume acquisition module 501, a target data attribute acquisition module 502, an index data attribute module 503, and a database index construction module 504, wherein:
the data amount obtaining module 501 is configured to obtain, when it is identified that the data searching tool for the financial database is a target data searching tool, a data amount of target data within a searching range of the data searching tool in the financial database, obtain data attributes contained in the target data, and obtain a number of each attribute value in each data attribute; the target data searching tool is a data searching tool with a lower efficiency value than a preset efficiency value;
The target data attribute obtaining module 502 is configured to obtain information gain corresponding to each data attribute based on the data amount and the data amount, and screen out a target data attribute from the data attributes based on the information gain;
an index data attribute module 503, configured to obtain an information gain rate of each target data attribute according to the information gain of each target data attribute, and use the target data attribute with the maximum information gain rate as an initial index data attribute;
the database index construction module 504 is configured to search the database index of the financial database using the initial index data attribute as a data search tool.
In one embodiment, the database index building module 504 is further configured to obtain the target index data attribute from the current data attribute corresponding to the data attribute if the data lookup tool is identified as the target data lookup tool again; the current data attribute is other data attributes except the initial index data attribute; and taking the initial index data attribute and the target index data attribute as new initial index data attributes, and returning to execute the step of searching the database index of the financial database by taking the initial index data attributes as a data searching tool until the data searching tool is not identified as the target data searching tool.
In one embodiment, the database index building module 504 is further configured to obtain a data amount of the current data corresponding to the current data attribute in the target data, and obtain a number of each attribute value in the current data attribute; obtaining information gain corresponding to each current data attribute based on the data quantity of the current data and the quantity of each attribute value in the current data attribute; and acquiring the target index data attribute from the current data attribute based on the information gain corresponding to the current data attribute.
In one embodiment, the target data attribute obtaining module 502 is further configured to obtain, based on the data amount and the data amount, an information entropy and a weight corresponding to each attribute value in the data attribute; and obtaining the information gain corresponding to the data attribute according to the information entropy and the weight corresponding to each attribute value in the data attribute.
In one embodiment, the target data attribute obtaining module 502 is further configured to obtain an average value of the information gain; and screening the data attributes with information gain larger than the average value from the data attributes, and taking the data attributes as target data attributes.
In one embodiment, the index data attribute module 503 is further configured to obtain an intrinsic value of the target data attribute; the intrinsic value is used for representing the variety number of the attribute values in the target data attribute; and obtaining the information gain rate of the target data attribute based on the ratio of the information gain to the eigenvalue.
In one embodiment, the financial database index building apparatus further includes a data lookup module, further configured to send the database index to the data lookup tool in response to a data lookup command of the data lookup tool; and sending the data corresponding to the search result to a data search terminal corresponding to the data search tool according to the search result obtained by the data search tool based on the database index.
The above-described respective modules in the financial database index construction apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing financial database index build data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a method of constructing a financial database index.
It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
under the condition that the data searching tool aiming at the financial database is identified as a target data searching tool, acquiring the data quantity of target data in the searching range of the data searching tool in the financial database, acquiring data attributes contained in the target data, and acquiring the quantity of each attribute value in each data attribute; the target data searching tool is a data searching tool with a lower efficiency value than a preset efficiency value;
obtaining information gain corresponding to each data attribute based on the data quantity and the data quantity, and screening out target data attributes from the data attributes based on the information gain;
Obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute;
and searching the database index of the financial database by using the initial index data attribute as a data searching tool.
In one embodiment, the processor when executing the computer program further performs the steps of: under the condition that the data searching tool is identified as the target data searching tool again, acquiring a target index data attribute from the current data attribute corresponding to the data attribute; the current data attribute is other data attributes except the initial index data attribute in the data attributes; and taking the initial index data attribute and the target index data attribute as new initial index data attributes, and returning to execute the step of searching the database index of the financial database by taking the initial index data attributes as a data searching tool until the data searching tool is not identified as the target data searching tool.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring the data quantity of current data corresponding to the current data attribute in the target data, and acquiring the quantity of each attribute value in the current data attribute; obtaining information gain corresponding to each current data attribute based on the data quantity of the current data and the quantity of each attribute value in the current data attribute; and acquiring the target index data attribute from the current data attribute based on the information gain corresponding to the current data attribute.
In one embodiment, the processor when executing the computer program further performs the steps of: based on the data quantity and the data quantity, obtaining information entropy and weight corresponding to each attribute value in the data attribute; and obtaining the information gain corresponding to the data attribute according to the information entropy and the weight corresponding to each attribute value in the data attribute.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring an average value of the information gain; and screening the data attributes with information gain larger than the average value from the data attributes, and taking the data attributes as target data attributes.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring an inherent value of a target data attribute; the intrinsic value is used for representing the variety number of the attribute values in the target data attribute; and obtaining the information gain rate of the target data attribute based on the ratio of the information gain to the eigenvalue.
In one embodiment, the processor when executing the computer program further performs the steps of: transmitting the database index to the data lookup tool in response to a data lookup command of the data lookup tool; and sending the data corresponding to the search result to a data search terminal corresponding to the data search tool according to the search result obtained by the data search tool based on the database index.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (11)
1. A method for constructing a financial database index, the method comprising:
under the condition that a data searching tool aiming at a financial database is identified as a target data searching tool, acquiring the data quantity of target data in the searching range of the data searching tool in the financial database, acquiring data attributes contained in the target data, and acquiring the quantity of each attribute value in each data attribute; the target data searching tool is a data searching tool with a lower efficiency value than a preset efficiency value;
Obtaining information gain corresponding to each data attribute based on the data quantity and the quantity, and screening target data attributes from the data attributes based on the information gain;
obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute;
and searching the database index of the financial database by using the initial index data attribute as the data searching tool.
2. The method of claim 1, wherein said searching the database index of the financial database as the data search tool with the initial index data attribute further comprises:
acquiring a target index data attribute from the current data attribute corresponding to the data attribute under the condition that the data searching tool is identified as a target data searching tool again; the current data attribute is other data attributes except the initial index data attribute in the data attributes;
and taking the initial index data attribute and the target index data attribute as new initial index data attributes, and returning to execute the step of searching the database index of the financial database by taking the initial index data attribute as the data searching tool until the data searching tool is not identified as the target data searching tool.
3. The method according to claim 2, wherein the obtaining the target index data attribute from the current data attribute corresponding to the data attribute includes:
acquiring the data quantity of current data corresponding to the current data attribute in the target data, and acquiring the quantity of each attribute value in the current data attribute;
obtaining information gain corresponding to each current data attribute based on the data quantity of the current data and the quantity of each attribute value in the current data attribute;
and acquiring a target index data attribute from the current data attribute based on the information gain corresponding to the current data attribute.
4. The method of claim 1, wherein the obtaining, based on the data amount and the number, an information gain corresponding to each of the data attributes comprises:
based on the data quantity and the quantity, obtaining information entropy and weight corresponding to each attribute value in the data attribute;
and obtaining the information gain corresponding to the data attribute according to the information entropy and the weight corresponding to each attribute value in the data attribute.
5. The method of claim 1, wherein the screening the data attributes for the target data attribute based on the information gain comprises:
Acquiring an average value of the information gain;
and screening the data attribute with the information gain larger than the average value from the data attributes as the target data attribute.
6. The method of claim 1, wherein the obtaining the information gain ratio of each of the target data attributes according to the information gain of each of the target data attributes comprises:
acquiring an inherent value of the target data attribute; the inherent value is used for representing the variety number of the attribute value in the target data attribute;
and obtaining the information gain rate of the target data attribute based on the ratio of the information gain to the inherent value.
7. The method of claim 1, further comprising, after said constructing the database index corresponding to the financial database:
transmitting the database index to the data lookup tool in response to a data lookup command of the data lookup tool;
and according to the obtained searching result of the data searching tool based on the database index, sending the data corresponding to the searching result to a data searching terminal corresponding to the data searching tool.
8. A financial database index building apparatus, the apparatus comprising:
The data volume acquisition module is used for acquiring the data volume of target data in the searching range of the data searching tool in the financial database and acquiring data attributes contained in the target data and the number of each attribute value in the data attributes under the condition that the data searching tool aiming at the financial database is identified as the target data searching tool; the target data searching tool is a data searching tool with a lower efficiency value than a preset efficiency value;
the target data attribute acquisition module is used for acquiring information gain corresponding to each data attribute based on the data quantity and the quantity, and screening target data attributes from the data attributes based on the information gain;
the index data attribute module is used for obtaining the information gain rate of each target data attribute according to the information gain of each target data attribute, and taking the target data attribute with the maximum information gain rate as the initial index data attribute;
and the database index construction module is used for searching the database index of the financial database by taking the initial index data attribute as the data searching tool.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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