A method and device for managing models in a data warehouse

Abstract

The present invention discloses a method and device for managing models in a data warehouse, and relates to the field of computer technology. A specific implementation of the method includes: determining one or more first characteristic attributes of multiple models in the data warehouse; calculating the information gain of the first characteristic attribute; using the first characteristic attribute whose information gain is greater than a threshold as the second characteristic attribute; calculating the management priority values ​​of the multiple models respectively according to the one or more second characteristic attribute values ​​of the multiple models; and determining the management strategy of the model according to the management priority value of the model. This implementation improves the accuracy of model classification in the data warehouse, thereby realizing accurate management of the model.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a management method and device for models in a data warehouse. Background Art

[0002] As data warehouses grow larger and larger, they often contain thousands of models. How to reasonably classify a large number of models to identify the importance of different models is crucial to the efficient management of models.

[0003] At present, the classification of models usually relies on the subjective judgment of decision makers on each model. Specifically, decision makers identify different levels of labels for models according to their understanding of the models, so as to use different labels to identify the importance of the models.

[0004] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art:

[0005] Existing technologies mainly rely on the subjective judgment of decision makers, but it is difficult for decision makers to conduct a completely objective evaluation of a large number of models in the data warehouse, which leads to inaccurate classification of models and makes it difficult to manage models accurately. Summary of the invention

[0006] In view of this, an embodiment of the present invention provides a method and device for managing models in a data warehouse, which can improve the accuracy of model classification in the data warehouse and thus achieve precise management of the models.

[0007] To achieve the above objective, according to one aspect of an embodiment of the present invention, a method for managing models in a data warehouse is provided.

[0008] A method for managing a model in a data warehouse according to an embodiment of the present invention includes:

[0009] Determining one or more first characteristic attributes of a plurality of models in the data warehouse;

[0010] Calculating the information gain of the first feature attribute;

[0011] Taking the first characteristic attribute whose information gain is greater than a threshold as the second characteristic attribute;

[0012] Calculating management priority values ​​of the multiple models respectively according to one or more second characteristic attribute values ​​of the multiple models;

[0013] A management strategy for the model is determined according to the management priority value of the model.

[0014] Optionally,

[0015] The first characteristic attribute includes any one or more of the following: the identification of the model, the type of the model, the CPU capacity occupied by the model, the memory capacity occupied by the model, the type or version of the system in which the model is located, the alarm parameters of the model, the latest completion time corresponding to the model, the number of recursive sub-models corresponding to the model, the number of times the model is used, and the importance identification label of the model.

[0016] Optionally,

[0017] The calculating the management priority values ​​of the multiple models respectively according to the one or more second characteristic attribute values ​​of the multiple models comprises:

[0018] Calculating the weight value of the second characteristic attribute by using an entropy method;

[0019] The management priority values ​​of the multiple models are calculated respectively according to the weight values ​​of one or more of the second characteristic attributes of the multiple models and the second characteristic attribute values.

[0020] Optionally,

[0021] The second characteristic attribute value is subjected to continuity preprocessing and normalization preprocessing, and a weight value of the second characteristic attribute corresponding to the second characteristic value is calculated according to the second characteristic attribute value after the continuity preprocessing and the normalization preprocessing.

[0022] Optionally, the method further comprises:

[0023] The multiple models are managed according to the model management strategy.

[0024] To achieve the above objective, according to another aspect of an embodiment of the present invention, a device for managing models in a data warehouse is provided.

[0025] A management device for a model in a data warehouse according to an embodiment of the present invention includes:

[0026] A first characteristic attribute determination module, a second characteristic attribute determination module, a priority value calculation module and a management module; wherein:

[0027] The first feature attribute module is used to determine one or more first feature attributes of a plurality of models in the data warehouse;

[0028] The second characteristic attribute determination module is used to calculate the information gain of the first characteristic attribute, and use the first characteristic attribute whose information gain is greater than a threshold as the second characteristic attribute;

[0029] The priority value calculation module is used to calculate the management priority values ​​of the multiple models respectively according to one or more second characteristic attribute values ​​of the multiple models;

[0030] The management module is used to determine the management strategy of the model according to the management priority value of the model.

[0031] Optionally,

[0032] The first characteristic attribute includes any one or more of the following: the identification of the model, the type of the model, the CPU capacity occupied by the model, the memory capacity occupied by the model, the type or version of the system in which the model is located, the alarm parameters of the model, the latest completion time corresponding to the model, the number of recursive sub-models corresponding to the model, the number of times the model is used, and the importance identification label of the model.

[0033] Optionally,

[0034] The priority value calculation module is used to calculate the weight value of the second characteristic attribute using the entropy method; and calculate the management priority values ​​of the multiple models respectively according to the weight value of one or more of the second characteristic attributes of the multiple models and the second characteristic attribute value.

[0035] To achieve the above objective, according to another aspect of an embodiment of the present invention, an electronic device for model management in a data warehouse is provided.

[0036] An electronic device for model management in a data warehouse according to an embodiment of the present invention comprises: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement a method for managing models in a data warehouse according to an embodiment of the present invention.

[0037] To achieve the above objective, according to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided.

[0038] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, which, when executed by a processor, implements a method for managing a model in a data warehouse according to an embodiment of the present invention.

[0039] One embodiment of the above invention has the following advantages or beneficial effects: by determining one or more first characteristic attributes of multiple models in a data warehouse and calculating the information gain of the first characteristic attributes, then selecting a characteristic attribute with an information gain greater than a threshold from the first characteristic attributes as a second characteristic attribute, thereby selecting a second characteristic attribute that is more important to the model, then calculating the management priority value of the model according to the second characteristic attribute, and determining the management strategy of the model according to the management priority value of the model. Thus, by calculating the information gain, selecting the second characteristic attribute that is more important to the model, and then determining the management strategy of the model according to the second characteristic attribute, compared with the prior art that relies on the subjective judgment of the decision maker, the accuracy of the model classification is improved, which is conducive to the accurate management of the model.

[0040] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0042] Figure 1 is a schematic diagram of main steps of a method for managing a model in a data warehouse according to an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of main steps of a method for managing a model in a data warehouse according to another embodiment of the present invention;

[0044] Figure 3 is a schematic diagram of main modules of a device for managing models in a data warehouse according to an embodiment of the present invention;

[0045] Figure 4 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0046] Figure 5 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0048] It should be pointed out that the embodiments of the present invention and the technical features therein may be combined with each other without conflict.

[0049] Figure 1 It is a schematic diagram of main steps of a method for managing models in a data warehouse according to an embodiment of the present invention.

[0050] like Figure 1 As shown, a method for managing a model in a data warehouse according to an embodiment of the present invention mainly includes the following steps:

[0051] Step S101: determining one or more first characteristic attributes of a plurality of models in the data warehouse;

[0052] Among them, the first characteristic attribute may include any one or more of the following: the identification of the model, the type of the model, the CPU capacity occupied by the model, the memory capacity occupied by the model, the type or version of the system in which the model is located, the alarm parameters of the model, the latest completion time corresponding to the model, the number of recursive sub-models corresponding to the model, the number of times the model is used, and the importance identification label of the model.

[0053] Specifically, each model in the data warehouse can correspond to a task. The model identifier can be the name of the task or the number of the task, etc. The type of the model is the task type, such as a synchronization task or a training task, etc. The CPU capacity occupied by the model is the size of the CPU limited to be used by the task corresponding to the model when executing the task. Similarly, the memory capacity occupied by the model is the size of the memory limited to be used by the task when executing the task corresponding to the model. The type or version of the system in which the model is located is the type or version of the system used to execute the task corresponding to the model. The alarm parameter of the model is generally set to 0 or 1, 0 means no alarm is needed, and 1 means an alarm is needed. The latest completion time corresponding to the model is the latest completion time required for the task. The recursive sub-model corresponding to the model is the recursive sub-task corresponding to the corresponding task. For example, the task of calculating the sales volume of goods is a recursive sub-task of the task of counting the number of orders. The importance identification label of the model is an identification label set for the model by the decision maker in advance according to the importance of the model.

[0054] In one embodiment of the present invention, the first characteristic attribute and the corresponding attribute description may be shown in the following Table 1:

[0055] Table 1

[0056]

[0057]

[0058] Step S102: Calculate the information gain of the first feature attribute.

[0059] The information gain can be determined by calculating the difference between the information entropy and the conditional entropy. Specifically, the information gain of each first feature attribute can be calculated according to the attribute value of the first feature attribute. Taking the first feature attribute cpu_limit_size in Table 1 as an example, according to the attribute values ​​of the first feature attribute cpu_limit_size of multiple models, the information gain of the first feature model cpu_limit_size can be calculated.

[0060] For example, there are 10 models in the data warehouse. The attribute values ​​of the first characteristic attribute cpu_limit_size of these 10 models are shown in Table 2 below:

[0061] Table 2

[0062]

[0063]

[0064] It can be understood that the calculation formula of information entropy is as follows (1):

[0065]

[0066] Among them, H(X) represents information entropy, p(x i ) represents the probability corresponding to the i-th result.

[0067] According to the above formula and the attribute values ​​in Table 2, the result is that the number of important ones is 5, accounting for 1 / 2. Then according to the calculation formula of information entropy, the corresponding information entropy is:

[0068]

[0069] In addition, according to Table 2, the first characteristic attribute cpu_limit_size may have four values ​​(2, 4, 6, 10), among which, for the result of 2, 1 is important and 2 are unimportant; for the result of 4, 1 is important and 1 is unimportant; for the result of 6, 1 is important and 1 is unimportant; for the result of 10, 2 are important and 1 is unimportant.

[0070] The calculation formula of conditional entropy is shown in the following formula (2):

[0071] H(Y|X)=∑ x∈X p(x)H(Y|X=x)............(2)

[0072] Thus, the conditional entropy corresponding to each of the above attribute values ​​can be calculated. Specifically,

[0073]

[0074]

[0075]

[0076]

[0077] The resulting conditional entropy is

[0078]

[0079] Furthermore, according to the information entropy and the conditional entropy, it can be concluded that the information gain of the first characteristic attribute cpu_limit_size is 0.301-0.286=0.015.

[0080] It is understandable that in actual applications, the number of models in the data warehouse is large, and the range of the attribute values ​​of each first characteristic attribute is also large. Table 2 above is only a feasible implementation method listed to illustrate the specific calculation process of information gain, and does not represent the actual situation in the data warehouse. Similarly, the above calculation method can be used to calculate the information gain of other first characteristic attributes. For example, the calculated information gain values ​​of each first characteristic attribute can be shown in Table 3 below:

[0081] Table 3

[0082] Property name Information Gain task_type 0.08911038143742278 cpu_limit_size 0.015 mem_limit_size 0.2323467381656763 version 0.046546468162639965 is_alarm 0.06295350556158263 sla_time 1.9777831405604434 recur_children_num 2.004385207243784 Business used num 1.9666493581871802 core_tag 1.9542904519220188

[0083] Step S103: taking the first characteristic attribute whose information gain is greater than a threshold as the second characteristic attribute.

[0084] Feature selection based on information gain is a widely used feature selection method. By comparing the size of information gain, the appropriate second feature attribute can be selected to improve the accuracy of model management. According to the information gain in Table 3, the four first feature attributes with the largest new gain can be selected as the second feature attributes for calculating the model management priority value, that is, sla_time, recur_children_num, Business used num and core_tag as the second feature attributes.

[0085] Step S104: Calculate management priority values ​​of the multiple models respectively according to one or more second characteristic attribute values ​​of the multiple models.

[0086] When calculating the management priority value, the entropy method can be used to calculate the weight value of the second characteristic attribute; the management priority values ​​of the multiple models are calculated respectively according to the weight value of one or more of the second characteristic attributes of the multiple models and the second characteristic attribute value.

[0087] Since the entropy method is suitable for continuous numerical calculations, and the attribute values ​​of the two second characteristic attributes, sla_time and core_tag, are discrete numerical values, in order to facilitate the calculation of the weight value of the second characteristic attribute using the entropy method, the second characteristic attribute value can be preprocessed in advance for continuity and normalization. According to the second characteristic attribute value after continuity preprocessing and normalization preprocessing, the weight value of the second characteristic attribute corresponding to the second characteristic value is calculated.

[0088] For example, before the continuous preprocessing, the second characteristic attribute values ​​corresponding to the 10 models are shown in Table 4 below:

[0089] Table 4

[0090]

[0091]

[0092] When performing continuity preprocessing, the scoring criteria for sla_time (as shown in Table 5) and core_tag (as shown in Table 6) can be determined based on empirical values:

[0093] Table 5

[0094] Time Range Fraction 0:00-2:00 50 2:00-4:00 40 4:00-6:00 30 6:00-8:00 20 8:00-23:59 10

[0095] Table 6

[0096] Associated core tag level Fraction L1 100 L2 80 L3 50 L4 30 other 10

[0097] After continuous preprocessing of the attribute values ​​of sla_time and core_tag according to the scoring criteria shown in Table 5 and Table 6, the second feature attribute values ​​are shown in Table 7 below:

[0098] Table 7

[0099]

[0100]

[0101] Furthermore, since the measurement units of the second characteristic attributes are usually not uniform, they can be standardized before calculating the weight values, that is, the absolute values ​​of the second characteristic attribute values ​​are converted into relative values, thereby solving the homogeneity problem of different second characteristic attribute values.

[0102] Then, when calculating the management priority value, the information entropy value and the information utility value can be calculated using the entropy method according to the second characteristic attribute value after continuity preprocessing and normalization preprocessing, and then the weight value of the second characteristic attribute is calculated according to the information entropy value and the information utility value. For example, the weight value of the second characteristic attribute of a certain model is calculated as shown in Table 8. Finally, the management priority values ​​of multiple models are calculated separately by weighted summation.

[0103] Table 8

[0104] The second characteristic attribute Weight value sla_time 0.19806947 recur_children_num 0.32498544 Business used num 0.21461184 core_tag 0.26233325

[0105] It is worth mentioning that after calculating the weight value of the second characteristic attribute, if the decision maker has some experience in subjectively estimating the weight value in advance, the weight value calculated by the entropy method can be further corrected with the help of the experience value.

[0106] Step S105: Determine the management strategy of the model according to the management priority value of the model.

[0107] After calculating the management priority values ​​of multiple models respectively by weighted summation, the ranking of the multiple models can be determined according to the management priority values. The higher the management priority value of the model, the higher its ranking, the higher the corresponding importance, and the higher the priority of its management strategy.

[0108] After the management strategies of each model are determined, the multiple models can be managed according to the model management strategy. For example, the model with the highest priority in the model management strategy is monitored in real time. Once a problem occurs during the execution of the task corresponding to the model, the relevant business personnel are notified immediately by email or phone. For the model with the lowest priority in the model management strategy, intermittent monitoring is implemented for a longer period of time, such as checking every hour whether there is a problem with the task corresponding to the model. If there is a problem, the relevant business personnel are notified by email.

[0109] In summary, the management method of the model in the data warehouse provided by the embodiment of the present invention may include the following steps: Figure 2 Steps shown:

[0110] Step S201: Determine a plurality of first characteristic attributes of a plurality of models in the data warehouse.

[0111] Step S202: Calculate the information gain of the first feature attribute.

[0112] Step S203: taking the first characteristic attribute whose information gain is greater than a threshold as the second characteristic attribute.

[0113] Step S204: performing continuity preprocessing and normalization preprocessing on the second characteristic attribute value of the second characteristic attribute, and calculating the weight value of the second characteristic attribute corresponding to the second characteristic value by using an entropy method according to the second characteristic attribute value after the continuity preprocessing and the normalization preprocessing.

[0114] Step S205: Calculate management priority values ​​of the multiple models respectively according to the weight values ​​of the multiple second characteristic attributes of the multiple models and the second characteristic attribute values.

[0115] Step S206: Determine the management strategy of the model according to the management priority value of the model.

[0116] Step S207: managing the multiple models according to the model management strategy.

[0117] It can be seen from the management method of models in a data warehouse according to an embodiment of the present invention that by determining one or more first characteristic attributes of multiple models in the data warehouse and calculating the information gain of the first characteristic attributes, a second characteristic attribute whose information gain is greater than a threshold is selected from the first characteristic attributes, thereby selecting a second characteristic attribute that is more important to the model, and then calculating the management priority value of the model according to the second characteristic attribute, and determining the management strategy of the model according to the management priority value of the model. Thus, by calculating the information gain to select the second characteristic attribute that is more important to the model, and then determining the management strategy of the model according to the second characteristic attribute, compared with the prior art that relies on the subjective judgment of the decision maker, the accuracy of the model classification is improved, which is conducive to the accurate management of the model.

[0118] Figure 3 4 is a schematic diagram of main modules of a device for managing models in a data warehouse according to an embodiment of the present invention.

[0119] like Figure 3 As shown, the management device 300 of the model in the data warehouse of the embodiment of the present invention includes: a first characteristic attribute determination module 301, a second characteristic attribute determination module 302, a priority value calculation module 303 and a management module 304; wherein,

[0120] The first characteristic attribute module 301 is used to determine one or more first characteristic attributes of multiple models in the data warehouse;

[0121] The second characteristic attribute determination module 302 is used to calculate the information gain of the first characteristic attribute, and take the first characteristic attribute whose information gain is greater than a threshold as the second characteristic attribute;

[0122] The priority value calculation module 303 is used to calculate the management priority values ​​of the multiple models respectively according to one or more second characteristic attribute values ​​of the multiple models;

[0123] The management module 304 is used to determine the management strategy of the model according to the management priority value of the model.

[0124] In one embodiment of the present invention, the first characteristic attribute includes any one or more of the following: the identification of the model, the type of the model, the CPU capacity occupied by the model, the memory capacity occupied by the model, the type or version of the system in which the model is located, the alarm parameters of the model, the latest completion time corresponding to the model, the number of recursive sub-models corresponding to the model, the number of times the model is used, and the importance identification label of the model.

[0125] In one embodiment of the present invention, the priority value calculation module 303 is used to calculate the weight value of the second characteristic attribute using the entropy method; and calculate the management priority values ​​of the multiple models respectively according to the weight values ​​of one or more of the second characteristic attributes of the multiple models and the second characteristic attribute values.

[0126] In one embodiment of the present invention, the priority value calculation module 303 is used to calculate the weight value of the second characteristic attribute using the entropy method; and calculate the management priority values ​​of the multiple models respectively according to the weight values ​​of one or more of the second characteristic attributes of the multiple models and the second characteristic attribute values.

[0127] In one embodiment of the present invention, the priority value calculation module 303 is used to perform continuity preprocessing and normalization preprocessing on the second characteristic attribute value, and calculate the weight value of the second characteristic attribute corresponding to the second characteristic value based on the second characteristic attribute value after continuity preprocessing and normalization preprocessing.

[0128] In one embodiment of the present invention, the management module 304 is further used to manage the multiple models according to the model management strategy.

[0129] It can be seen from the management device of the model in the data warehouse according to the embodiment of the present invention that by determining one or more first characteristic attributes of multiple models in the data warehouse and calculating the information gain of the first characteristic attribute, the characteristic attribute whose information gain is greater than the threshold is selected from the first characteristic attribute as the second characteristic attribute, thereby selecting the second characteristic attribute that is more important to the model, and then calculating the management priority value of the model according to the second characteristic attribute, and determining the management strategy of the model according to the management priority value of the model. Therefore, by calculating the information gain, the second characteristic attribute that is more important to the model is selected, and then the management strategy of the model is determined according to the second characteristic attribute. Compared with the prior art that relies on the subjective judgment of the decision maker, the accuracy of the model classification is improved, which is conducive to the accurate management of the model.

[0130] An electronic device for model management in a data warehouse according to an embodiment of the present invention comprises: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement a method for managing models in a data warehouse according to an embodiment of the present invention.

[0131] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, which, when executed by a processor, implements a method for managing a model in a data warehouse according to an embodiment of the present invention.

[0132] Figure 4 An exemplary system architecture 400 is shown to which the method for managing models in a data warehouse or the apparatus for managing models in a data warehouse according to an embodiment of the present invention can be applied.

[0133] like Figure 4 As shown, system architecture 400 may include terminal devices 401, 402, 403, network 404 and server 405. Network 404 is used to provide a medium for communication links between terminal devices 401, 402, 403 and server 405. Network 404 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0134] Users can use terminal devices 401, 402, 403 to interact with server 405 through network 404 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 401, 402, 403, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0135] The terminal devices 401 , 402 , and 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0136] Server 405 may be a server that provides various services, such as a backend management server that provides support for shopping websites browsed by users using terminal devices 401, 402, and 403. The backend management server may analyze and process received data such as product information query requests, and feed back processing results (such as target push information and product information) to the terminal device.

[0137] It should be noted that the method for managing models in a data warehouse provided in the embodiment of the present invention is generally executed by the server 405 , and accordingly, the device for managing models in a data warehouse is generally disposed in the server 405 .

[0138] It should be understood that Figure 4 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0139] Reference below Figure 5 , which shows a schematic diagram of the structure of a computer system 500 of a terminal device suitable for implementing an embodiment of the present invention. Figure 5 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0140] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0141] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0142] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present invention are executed.

[0143] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0144] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0145] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor includes a first feature attribute determination module, a second feature attribute determination module, a priority value calculation module and a management module. The names of these modules do not constitute a limitation on the modules themselves in some cases. For example, the first feature attribute determination module may also be described as "a module for determining one or more first feature attributes of multiple models in the data warehouse".

[0146] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: determining one or more first characteristic attributes of multiple models in the data warehouse; calculating the information gain of the first characteristic attribute; selecting a second characteristic attribute whose information gain is greater than a threshold from the first characteristic attribute; calculating the management priority values ​​of the multiple models respectively according to the one or more second characteristic attribute values ​​of the multiple models; and determining the management strategy of the model according to the management priority value of the model.

[0147] According to the technical solution of the embodiment of the present invention, by determining one or more first characteristic attributes of multiple models in the data warehouse and calculating the information gain of the first characteristic attributes, a second characteristic attribute whose information gain is greater than a threshold is selected from the first characteristic attributes, thereby selecting a second characteristic attribute that is more important to the model, and then calculating the management priority value of the model according to the second characteristic attribute, and determining the management strategy of the model according to the management priority value of the model. Thus, by calculating the information gain, a second characteristic attribute that is more important to the model is selected, and then the management strategy of the model is determined according to the second characteristic attribute. Compared with the prior art that relies on the subjective judgment of the decision maker, the accuracy of the model classification is improved, which is conducive to the accurate management of the model.

[0148] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for managing models in a data warehouse, characterized in that: include: Determining one or more first characteristic attributes of a plurality of models in the data warehouse; The first characteristic attribute includes any one of the following: the identifier of the model, the type of the model, the CPU capacity occupied by the model, the memory capacity occupied by the model, the type or version of the system where the model is located, the alarm parameters of the model, the latest completion time corresponding to the model, the number of recursive sub-models corresponding to the model, the number of times the model is used, and the importance identification label of the model; Calculating the information gain of the first feature attribute; Taking the first characteristic attribute whose information gain is greater than a threshold as the second characteristic attribute; Calculating management priority values ​​of the multiple models respectively according to one or more second characteristic attribute values ​​of the multiple models; specifically comprising: calculating a weight value of the second characteristic attribute by using an entropy method; calculating management priority values ​​of the multiple models respectively according to one or more weight values ​​of the second characteristic attribute of the multiple models and the second characteristic attribute value; Determining a management strategy for the model according to the management priority value of the model; Each model in the data warehouse corresponds to a task, the identifier of the model is the name of the task or the number of the task, and the type of the model is the task type.

2. The method according to claim 1, characterized in that: The second characteristic attribute value is subjected to continuity preprocessing and normalization preprocessing, and a weight value of the second characteristic attribute corresponding to the second characteristic attribute value is calculated according to the second characteristic attribute value after the continuity preprocessing and the normalization preprocessing.

3. The method according to claim 1, characterized in that Also includes: The multiple models are managed according to the model management strategy.

4. A management device for a model in a data warehouse, characterized in that: include: A first characteristic attribute determination module, a second characteristic attribute determination module, a priority value calculation module and a management module; wherein: The first characteristic attribute module is used to determine one or more first characteristic attributes of multiple models in the data warehouse; the first characteristic attribute includes any one of the following: the identification of the model, the type of the model, the CPU capacity occupied by the model, the memory capacity occupied by the model, the type or version of the system where the model is located, the alarm parameters of the model, the latest completion time corresponding to the model, the number of recursive sub-models corresponding to the model, the number of times the model is used, and the importance identification label of the model; The second characteristic attribute determination module is used to calculate the information gain of the first characteristic attribute, and use the first characteristic attribute whose information gain is greater than a threshold as the second characteristic attribute; The priority value calculation module is used to calculate the management priority values ​​of the multiple models according to one or more second characteristic attribute values ​​of the multiple models; specifically used to: calculate the weight value of the second characteristic attribute by using the entropy method; calculate the management priority values ​​of the multiple models according to the weight value of one or more second characteristic attributes of the multiple models and the second characteristic attribute value; The management module is used to determine the management strategy of the model according to the management priority value of the model; Each model in the data warehouse corresponds to a task, the identifier of the model is the name of the task or the number of the task, and the type of the model is the task type.

5. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 3.

6. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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