CN115422240A - Multi-dimensional data set data processing method and device - Google Patents
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Abstract
The embodiment of the application provides a multi-dimensional data set data processing method and device, which can be used in the field of finance, and the method comprises the following steps: carrying out data set dimension summarizing calculation on the multi-dimensional data set, and determining a summarizing value data set; determining corresponding data sensitivity and data set contribution degree according to the summarized value data set; determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule; the method and the device can accurately and conveniently determine the difference data in the multi-dimensional data set.
Description
Technical Field
The application relates to the field of data processing, can also be used in the field of finance, and particularly relates to a multi-dimensional data set data processing method and device.
Background
In the financial market risk standard method measurement, under different precondition conditions (different market data or different risk situation settings), a data set of the tree-shaped risk capital value including multiple dimensions (investment portfolio tree ID, investment portfolio node ID, risk portfolio, risk factor, product type, product number) generated by the evaluation of the financial instruments and the sensitivity method risk measurement needs to be rapidly located, wherein the dimension causing the most major risk capital change (such as risk portfolio = USDCNY or investment portfolio node ID = 2023).
In the prior art, for two multi-dimensional data sets, a summary value of a target dimension is calculated according to a single dimension each time, and the single dimension with the largest difference is found out; however, the combination of the dimensions with the largest difference of the single dimensions is not necessarily the dimension combination with the largest difference after the multi-dimension combination, and it is desirable to find the dimension combination which is the smallest set of dimensions that shows the largest difference at the earliest, for example, the most ideal dimension combination is product type = foreward, risk combination = USD, but the dimension combination found by this scheme necessarily includes all the dimensions, which may be product type = foreward, risk combination = USD, risk factor = IRATE _ USDCNY _1M, node = FX-SPT-AABB, and thus there is a problem of dimension being too fine, that is, excessive search, which often fails to meet the real needs of the user.
In the prior art, the possibility of finding out the combination of all dimensions for two multi-dimensional data sets exists, all the dimension combinations are traversed, the summary value of the target dimension is calculated, and the dimension combination with the largest difference is found out; but the possibility of traversing all the dimension combinations causes the computation amount to show non-linear exponential increase with the increase of the dimension number of the data set, and the performance is sharply reduced.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a data processing method and device for a multi-dimensional data set, which can accurately and conveniently determine difference data in the multi-dimensional data set.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the present application provides a method for processing data of a multi-dimensional data set, including:
carrying out data set dimension summarizing calculation on the multi-dimensional data set, and determining a summarizing value data set;
determining corresponding data sensitivity and data set contribution degree according to the summarized value data set;
and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule.
Further, before performing a dataset dimension summary calculation on the multi-dimensional dataset and determining a summary value dataset, the method includes:
sorting the multi-dimensional data sets according to the data discrimination to obtain respective dimension sorting;
and carrying out data set dimension summarizing calculation on the multi-dimensional data set according to the dimension sequence, and determining a summarized value data set.
Further, the determining a corresponding data set contribution from the aggregated value data set comprises:
performing external association processing on the plurality of summarized value data sets;
and determining the contribution degree of the data set according to the single dimension value and the dimension total value of the data set after the external association processing.
Further, the determining a target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule includes:
determining dimension searching data of which the data sensitivity and the data set contribution degree in the summarized value data set exceed threshold values;
and determining a target dimension path according to the dimension search data.
In a second aspect, the present application provides a multidimensional data set data processing apparatus comprising:
the summarizing calculation module is used for carrying out data set dimension summarizing calculation on the multi-dimensional data set and determining a summarizing value data set;
the sensitivity contribution degree calculating module is used for determining corresponding data sensitivity and data set contribution degree according to the summarized value data set;
and the target path determining module is used for determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution degree and a preset screening rule.
Further, the summary calculation module includes:
the dimensionality sorting unit is used for sorting the multi-dimensional data sets according to the data discrimination to obtain respective dimensionality sorting;
and the data set summarizing unit is used for carrying out data set dimension summarizing calculation on the multi-dimensional data set according to the dimension sequence and determining a summarized value data set.
Further, the sensitivity contribution calculation module includes:
the external association processing unit is used for performing external association processing on the plurality of summarized value data sets;
and the contribution degree calculating unit is used for determining the contribution degree of the data set according to the single dimension value and the dimension total value of the data set after the external association processing.
Further, the target path determination module includes:
the dimension search data determining unit is used for determining dimension search data of which the data sensitivity and the data set contribution degree in the summarized data set exceed threshold values;
and the target path calculation unit is used for determining a target dimension path according to the dimension search data.
In a third aspect, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the multidimensional dataset data processing method when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for multidimensional dataset data processing.
In a fifth aspect, the present application provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method for multidimensional dataset data processing.
According to the technical scheme, the data processing method and the data processing device for the multi-dimensional data set are characterized in that the data set dimension summarizing calculation is carried out on the multi-dimensional data set to determine a summarizing data set; determining corresponding data sensitivity and data set contribution degree according to the summarized value data set; and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule, so that difference data in the multi-dimensional data set can be accurately and conveniently determined.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram of a multidimensional dataset data processing method in an embodiment of the present application;
FIG. 2 is a second flowchart illustrating a multidimensional data set processing method according to an embodiment of the present application;
FIG. 3 is a third flowchart illustrating a data processing method for a multidimensional data set according to an embodiment of the present application;
FIG. 4 is a fourth flowchart illustrating a multidimensional dataset data processing method according to an embodiment of the present application;
FIG. 5 is a block diagram of one embodiment of a multidimensional data set data processing apparatus;
FIG. 6 is a second block diagram of a multidimensional data set processing apparatus according to the second embodiment of the present application;
FIG. 7 is a third block diagram of a multidimensional data set data processing apparatus in an embodiment of the present application;
FIG. 8 is a fourth block diagram of a multidimensional data set data processing apparatus in an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws and regulations.
In view of the problems in the prior art, the application provides a method and a device for processing data of a multi-dimensional data set, wherein a summarized data set is determined by performing data set dimension summarizing calculation on the multi-dimensional data set; determining corresponding data sensitivity and data set contribution degree according to the summarized value data set; and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule, so that difference data in the multi-dimensional data set can be accurately and conveniently determined.
In order to accurately and conveniently determine difference data in a multidimensional dataset, the present application provides an embodiment of a multidimensional dataset data processing method, and referring to fig. 1, the multidimensional dataset data processing method specifically includes the following contents:
step S101: and carrying out data set dimension summarizing calculation on the multi-dimensional data set, and determining a summarized value data set.
Optionally, the multi-dimensional data set may take two multi-dimensional data sets DSA and DSB as an example, which both have n +1 dimensions d1... Dn, x, and the target dimension is x.
For example, the application may preset a confidence _ distance of the sensitivity and a confidence _ confidence of the contribution, such as 0.01. The currently searched dimension path routeofflatute, such as dsorted1 (product type) = foreign exchange forward- > dsorted2 (risk combination) = USD.
Optionally, a preset group of prioritized selected rule sets rulesetforchaose, for example, the contribution degree is greater than 50%, the sensitivity of variation is greater than 1, and the like; a preset set of rule sets RuleSetForFinish that can end the lookup, such as contribution greater than 50% and sensitivity of variation greater than 1.
Optionally, one chained storage dataset _ chain of the data set according to the dimension path, DSA and DSB are root nodes, and each time the data passes through a node on the dimension path, the data is screened by one layer according to the dimension condition to obtain new data sets DSA _ route1 and DSB _ route1, and then the data continues to obtain DSA _ route1_ route2 and DSB _ route1_ route2, and so on.
Thus, for two multidimensional data sets DSA and DSB, except for the target dimension x, the data resolution is sorted from low to high (the order of the dimension may also be artificially specified according to a specific scene), and the order of the dimensions is obtained: dsorted1.. Dsorted n, which is calculated according to the sequence, taking dsorted1 as an example, DSA calculates the dimension x of a data set according to dimension dsorted1 in a gathering way, the obtained gathered value data set DSA _ sum _ dsorted1, DSB calculates the dimension x of the data set according to dimension dsorted1 in a gathering way, the obtained gathered value data set DSB _ sum _ dsorted1, and the two data sets are related according to dimension dsorted1 to obtain a data set DSAB _ sum _ dsorted1, and the value caused by external connection is the empty dimension x which is directly assigned to be 0.
Step S102: and determining the corresponding data sensitivity and the corresponding data set contribution degree according to the summarized value data set.
Alternatively, the present application may calculate the varying sensitivity distance according to the formula abs ((a-b)/min (a, b)) (note: the boundary condition min (a, b) is 0, if one of a, b is not 0, the denominator of the above formula is the number which is not 0, and if both are 0, the above formula directly yields the result 0); meanwhile, a contribution ratio is calculated for each row of data, and is the sum of the value of the x dimension of the row of data divided by the value of the x dimension of the data set, so that the contribution ratio of the row of data to the whole data set is indicated and represents the influence degree on the whole.
Step S103: and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule.
Optionally, DSAB _ sum _ dsorted1 filters out the parts of distance > confidence _ distance and constraint _ ratio > confidence _ constraint, if the number of records is equal to 0, it indicates that there is no difference in the dimension below the set confidence, and the dimension is directly discarded; if the record number is greater than 0, a row of data with larger sensitivity and contribution degree is selected according to a preset rule set rulesetForChoose, the value of a dsorted1 column of the row of data is cached _ value _ dsorted1, and if any one of the rule set ruleSetForfinish is met, the searching process is ended.
It can be understood that whether the dimension is added to the currently searched dimension path is selected according to the above judgment, if the dimension path is added, dsorted1= cached _ value _ dsorted1 is added to the dimension path routeofflatute, and the current data set DSA and DSB are filtered according to the currently selected dimension condition dsorted1= cached _ value _ dsorted1, so as to obtain a new data set and store the new data set into the dataset _ chain; if the condition is met, ending the searching process, otherwise, continuing to calculate the next dimension, wherein the input for continuing the calculation of the next dimension is a new data set in the screened dataset _ chain; after all dimensions are calculated or the exit is carried out in advance according with the end condition, the obtained currently searched dimension path routeofflatute is the finally found dimension combination with the largest difference.
As can be seen from the above description, the multidimensional data set data processing method provided in the embodiment of the present application can determine a summarized value data set by performing data set dimension summarized calculation on a multidimensional data set; determining corresponding data sensitivity and data set contribution degree according to the summarized value data set; and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule, so that difference data in the multi-dimensional data set can be accurately and conveniently determined.
In an embodiment of the multidimensional data set data processing method of the present application, referring to fig. 2, the following may be further specifically included:
step S201: and sequencing the multi-dimensional data sets according to the data discrimination to obtain respective dimension sequencing.
Step S202: and carrying out data set dimension summarizing calculation on the multi-dimensional data set according to the dimension sequence, and determining a summarized value data set.
Optionally, for two multidimensional data sets DSA and DSB, except for the target dimension x, the data sets are sorted from low to high according to the data discrimination (the order of the dimension may also be artificially specified according to a specific scene), and the order of the dimension is obtained: dsortedn.
In an embodiment of the multidimensional data set data processing method of the present application, referring to fig. 3, the following may be further specifically included:
step S301: and performing external association processing on the plurality of summarized value data sets.
Step S302: and determining the contribution degree of the data set according to the single dimension value and the dimension total value of the data set after the external association processing.
Alternatively, the present application may calculate the varying sensitivity distance according to the formula abs ((a-b)/min (a, b)) (note that the boundary condition min (a, b) is 0, if one of a, b is not 0, the denominator of the above formula is the number which is not 0, and if both are 0, the above formula directly obtains the result 0); meanwhile, a contribution ratio is calculated for each row of data, and is the sum of the value of the x dimension of the row of data divided by the value of the x dimension of the data set, so that the contribution ratio of the row of data to the whole data set is indicated and represents the influence degree on the whole.
In an embodiment of the multidimensional data set data processing method of the present application, referring to fig. 4, the following may be further specifically included:
step S401: and determining dimension searching data of which the data sensitivity and the data set contribution degree exceed threshold values in the summarized value data set.
Step S402: and determining a target dimension path according to the dimension search data.
Optionally, DSAB _ sum _ dsorted1 filters out the parts of distance > confidence _ distance and constraint _ ratio > confidence _ constraint, if the number of records is equal to 0, it indicates that there is no difference in the dimension below the set confidence, and the dimension is directly discarded; if the record number is greater than 0, a row of data with larger sensitivity and contribution degree is selected according to a preset rule set rulesetForChoose, the value of a dsorted1 column of the row of data is cached _ value _ dsorted1, and if any one of the rule set ruleSetForfinish is met, the searching process is ended.
It can be understood that, according to the above judgment, whether to add this dimension to the currently searched dimension path is selected, if so, the dimension path routeofflatute is added with dsorted1= cached _ value _ dsorted1, and the current data set DSA and DSB are filtered according to the currently selected dimension condition dsorted1= cached _ value _ dsorted1, so as to obtain a new data set and store the new data set into dataset _ chain; if the condition is met, ending the searching process, otherwise, continuing to calculate the next dimension, wherein the input for continuing the calculation of the next dimension is a new data set in the screened dataset _ chain; after all dimensions are calculated or the exit is carried out in advance according with the end condition, the obtained currently searched dimension path routeofflattide is the finally found dimension combination with the largest difference.
In order to accurately and conveniently determine difference data in a multidimensional dataset, the present application provides an embodiment of a multidimensional dataset data processing apparatus for implementing all or part of contents of the multidimensional dataset data processing method, and referring to fig. 5, the multidimensional dataset data processing apparatus specifically includes the following contents:
and the summarizing calculation module 10 is used for carrying out data set dimension summarizing calculation on the multi-dimensional data set and determining a summarizing value data set.
And a sensitivity contribution degree calculation module 20, configured to determine, according to the summarized value data set, a corresponding data sensitivity and a corresponding data set contribution degree.
And a target path determining module 30, configured to determine a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule.
As can be seen from the above description, the multidimensional data set data processing apparatus provided in the embodiment of the present application can determine a summarized value data set by performing data set dimension summarizing calculation on a multidimensional data set; determining corresponding data sensitivity and data set contribution degree according to the summarized value data set; and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule, so that difference data in the multi-dimensional data set can be accurately and conveniently determined.
In an embodiment of the multidimensional data set data processing apparatus of the present application, referring to fig. 6, the summary calculation module 10 includes:
and the dimension sorting unit 11 is configured to sort the multi-dimensional data sets according to the data discrimination to obtain respective dimension sorting.
And the data set summarizing unit 12 is used for performing data set dimension summarizing calculation on the multi-dimensional data set according to the dimension sequence and determining a summarized value data set.
In an embodiment of the multidimensional dataset data processing apparatus of the present application, referring to fig. 7, the sensitivity contribution calculating module 20 includes:
an external association processing unit 21 is configured to perform external association processing on the plurality of summarized value data sets.
And the contribution degree calculating unit 22 is configured to determine the data set contribution degree according to the single dimension value and the data set dimension total value after the outer association processing.
In an embodiment of the multidimensional dataset data processing apparatus of the present application, referring to fig. 8, the target path determining module 30 comprises:
a dimension search data determining unit 31, configured to determine dimension search data in which the data sensitivity and the data set contribution degree in the summarized value data set exceed threshold values.
And the target path calculation unit 32 is configured to determine a target dimension path according to the dimension search data.
In order to accurately and conveniently determine difference data in a multidimensional dataset in a hardware level, the present application provides an embodiment of an electronic device for implementing all or part of contents in the multidimensional dataset data processing method, where the electronic device specifically includes the following contents:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the multi-dimensional data set data processing device and relevant equipment such as a core service system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiment of the multidimensional data set data processing method and the embodiment of the multidimensional data set data processing apparatus in the embodiment, and the contents thereof are incorporated herein, and repeated descriptions are omitted.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), an in-vehicle device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical applications, part of the multidimensional dataset data processing method may be performed on the electronic device side as described above, or all operations may be performed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.
The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.
Fig. 9 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 9, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is noted that this fig. 9 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the functionality of the multidimensional data set data processing method may be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:
step S101: and carrying out data set dimension summarizing calculation on the multi-dimensional data set, and determining a summarized value data set.
Step S102: and determining the corresponding data sensitivity and the corresponding data set contribution degree according to the summarized value data set.
Step S103: and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule.
As can be seen from the above description, the electronic device provided in the embodiment of the present application determines a summarized value data set by performing data set dimension summarized calculation on a multi-dimensional data set; determining corresponding data sensitivity and data set contribution degree according to the summarized value data set; and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule, so that difference data in the multi-dimensional data set can be accurately and conveniently determined.
In another embodiment, the multidimensional data set processing apparatus may be configured separately from the central processor 9100, for example, the multidimensional data set processing apparatus may be configured as a chip connected to the central processor 9100, and the multidimensional data set processing method function is realized by the control of the central processor.
As shown in fig. 9, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 9; in addition, the electronic device 9600 may further include components not shown in fig. 9, which may be referred to in the prior art.
As shown in fig. 9, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., read Only Memory (ROM), random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the multidimensional dataset data processing method with a server or a client as an execution subject in the foregoing embodiments, where the computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the computer program implements all the steps in the multidimensional dataset data processing method with a server or a client as an execution subject in the foregoing embodiments, for example, when the processor executes the computer program, the processor implements the following steps:
step S101: and carrying out data set dimension summarizing calculation on the multi-dimensional data set, and determining a summarized value data set.
Step S102: and determining the corresponding data sensitivity and the corresponding data set contribution degree according to the summarized value data set.
Step S103: and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule.
As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application determines a summarized value dataset by performing dataset dimension summarized calculation on a multi-dimensional dataset; determining corresponding data sensitivity and data set contribution degree according to the summarized value data set; and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule, so that difference data in the multi-dimensional data set can be accurately and conveniently determined.
Embodiments of the present application further provide a computer program product capable of implementing all steps in the multidimensional dataset data processing method with the execution subject being a server or a client in the foregoing embodiments, and when being executed by a processor, the computer program/instruction implements the steps of the multidimensional dataset data processing method, for example, the computer program/instruction implements the following steps:
step S101: and carrying out data set dimension summarizing calculation on the multi-dimensional data set, and determining a summarized value data set.
Step S102: and determining the corresponding data sensitivity and the corresponding data set contribution degree according to the summarized value data set.
Step S103: and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule.
As can be seen from the above description, the computer program product provided in the embodiment of the present application determines a summarized value dataset by performing dataset dimension summarized calculation on a multi-dimensional dataset; determining corresponding data sensitivity and data set contribution degree according to the summarized value data set; and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule, so that difference data in the multi-dimensional data set can be accurately and conveniently determined.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (11)
1. A method of data processing for a multi-dimensional dataset, the method comprising:
carrying out data set dimension summarizing calculation on the multi-dimensional data set, and determining a summarizing value data set;
determining corresponding data sensitivity and data set contribution degree according to the summarized value data set;
and determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution and a preset screening rule.
2. The method for processing multidimensional dataset data according to claim 1, before performing dataset dimension summary calculation on the multidimensional dataset and determining a summary value dataset, comprising:
sorting the multi-dimensional data sets according to the data discrimination to obtain respective dimension sorting;
and carrying out data set dimension summarizing calculation on the multi-dimensional data set according to the dimension sequence, and determining a summarized value data set.
3. The method of multi-dimensional dataset data processing according to claim 1, wherein said determining a corresponding dataset contribution from the summary value dataset comprises:
performing external association processing on the plurality of summarized value data sets;
and determining the contribution degree of the data set according to the single dimension value and the dimension total value of the data set after the external association processing.
4. The method as claimed in claim 1, wherein said determining a target dimension path according to the dataset sensitivities, the dataset contributions, and a preset filtering rule comprises:
determining dimension searching data of which the data sensitivity and the data set contribution degree in the summarized value data set exceed threshold values;
and determining a target dimension path according to the dimension search data.
5. A multidimensional dataset data processing apparatus, comprising:
the summarizing calculation module is used for carrying out data set dimension summarizing calculation on the multi-dimensional data set and determining a summarizing value data set;
the sensitivity contribution degree calculating module is used for determining corresponding data sensitivity and data set contribution degree according to the summarized value data set;
and the target path determining module is used for determining a target dimension path and a maximum difference dimension combination corresponding to the target dimension path according to the data set sensitivity, the data set contribution degree and a preset screening rule.
6. The multidimensional dataset data processing apparatus of claim 5, wherein the summary computation module comprises:
the dimensionality sorting unit is used for sorting the multi-dimensional data sets according to the data discrimination to obtain respective dimensionality sorting;
and the data set summarizing unit is used for carrying out data set dimension summarizing calculation on the multi-dimensional data set according to the dimension sequence and determining a summarized value data set.
7. The multidimensional dataset data processing apparatus of claim 5, wherein the sensitivity contribution calculation module comprises:
the external association processing unit is used for performing external association processing on the plurality of summarized value data sets;
and the contribution degree calculating unit is used for determining the contribution degree of the data set according to the single dimension value and the dimension total value of the data set after the external association processing.
8. The multidimensional dataset data processing apparatus of claim 5, wherein the target path determination module comprises:
the dimension search data determining unit is used for determining dimension search data of which the data sensitivity and the data set contribution degree in the summarized data set exceed threshold values;
and the target path calculation unit is used for determining a target dimension path according to the dimension search data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of processing multidimensional dataset data according to any of the claims 1 to 4 are implemented when the program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for processing multidimensional dataset data according to any of the claims 1 to 4.
11. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the steps of the multidimensional dataset data processing method of any of claims 1 to 4.
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