CN103942341A - Data processing method and device used for multi-dimensional data set - Google Patents
Data processing method and device used for multi-dimensional data set Download PDFInfo
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- CN103942341A CN103942341A CN201410196206.6A CN201410196206A CN103942341A CN 103942341 A CN103942341 A CN 103942341A CN 201410196206 A CN201410196206 A CN 201410196206A CN 103942341 A CN103942341 A CN 103942341A
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Abstract
The invention discloses a data processing method and device used for a multi-dimensional data set. The data processing method used for the multi-dimensional data set includes the steps of inquiring about parameters to be inquired about in the multi-dimensional data set in batches, wherein the parameters to be inquired about are parameters of target objects; generating derivation values of the parameters to be inquired about in batches, wherein the derivation values are used for representing whether the parameters to be inquired about meet preset conditions or not; counting the target objects corresponding to the parameters which meet the preset conditions and are about to be inquired about in batches. By means of the data processing method and device, the problem that in the related technology, a large amount of time is consumed when inquiry is conducted on the multi-dimensional data set is solved.
Description
Technical field
The present invention relates to computer realm, in particular to a kind of data processing method for cube and device.
Background technology
At present, in correlation technique, when inquiry cube, often need first cube to be filtered one by one according to filtercondition, by filtering one by one the result obtaining, gather again, such as, the concentrated data of (filter) multidimensional data first filtered one by one, obtain filter result, and then in bulk statistics (count) filter result, meet the data of filtercondition.Because this inquiry mode is the query pattern based on cell coupling (cell-by-cell), therefore need to inquire about one by one plurality of cells lattice, whole query script is consuming time greatly, performance is low.
Such as, for the product that produces order, if want to inquire about the species number that quantity on order is wherein greater than 5 product, adopt following query statement inquire about cube: Count (Filter (and [Product] .[Product] .[Product], [Measures] .[Internet Order Quantity] >5)).Due to count (filter ...) be the computation schema of a kind of cell-by-cell, therefore consuming time large, performance is low.
For inquiring about cube large problem consuming time in correlation technique, effective solution is not yet proposed at present.
Summary of the invention
Fundamental purpose of the present invention is to provide a kind of data processing method for cube and device, to solve, inquires about cube large problem consuming time in correlation technique.
To achieve these goals, according to an aspect of the present invention, provide a kind of data processing method for cube.The method comprises: the parameter to be checked that batch query multidimensional data is concentrated, wherein, the parameter that parameter to be checked is destination object; The derived value of Mass production parameter to be checked, wherein, it is pre-conditioned that derived value is used for representing whether parameter to be checked meets; And meet pre-conditioned destination object corresponding to parameter to be checked according to derived value bulk statistics.
Further, before the derived value of Mass production parameter to be checked, data processing method also comprises: judge whether parameter to be checked meets in batches pre-conditioned, wherein, if judging parameter to be checked in batches meets pre-conditioned, the derived value of Mass production parameter to be checked comprises: the first derived value of Mass production parameter to be checked, meets pre-conditioned destination object corresponding to parameter to be checked according to derived value bulk statistics and comprise: according to the first derived value bulk statistics, meet pre-conditioned destination object corresponding to parameter to be checked.
Further, judge in batches parameter to be checked whether meet pre-conditioned after, if judging parameter to be checked in batches does not meet pre-conditioned, the derived value of Mass production parameter to be checked comprises: the second derived value of Mass production parameter to be checked, meets pre-conditioned destination object corresponding to parameter to be checked according to derived value bulk statistics and comprise: according to the first derived value and the second derived value bulk statistics, meet pre-conditioned destination object corresponding to parameter to be checked.
Further, before the first derived value of Mass production parameter to be checked, data processing method also comprises: Mass production parameter to be checked for representing that parameter to be checked meets pre-conditioned the first sign, wherein, Mass production parameter to be checked for after representing that parameter to be checked meets pre-conditioned the first sign, the first derived value of Mass production parameter to be checked comprises: in batches the first sign is converted into the first derived value.
Further, in batches the first sign being converted into the first derived value comprises: in batches the first sign is converted into 1, according to the first derived value bulk statistics, meeting pre-conditioned parameter to be checked comprises: add up 1, be met the quantity of destination object corresponding to pre-conditioned parameter to be checked.
To achieve these goals, according to a further aspect in the invention, provide a kind of data processing equipment for cube.This device comprises: query unit, and the parameter to be checked of concentrating for batch query multidimensional data, wherein, the parameter that parameter to be checked is destination object; The first generation unit, for the derived value of Mass production parameter to be checked, wherein, it is pre-conditioned that derived value is used for representing whether parameter to be checked meets; And statistic unit, for meet pre-conditioned destination object corresponding to parameter to be checked according to derived value bulk statistics.
Further, this data processing equipment also comprises: judging unit, for before the derived value of Mass production parameter to be checked, judge whether parameter to be checked meets in batches pre-conditioned, wherein: if the first generation unit also meets pre-conditioned for judging parameter to be checked in batches, the first derived value of Mass production parameter to be checked, statistic unit is also for meeting pre-conditioned destination object corresponding to parameter to be checked according to the first derived value bulk statistics.
Further, the first generation unit also for judge in batches parameter to be checked whether meet pre-conditioned after, if judge in batches parameter to be checked do not meet pre-conditioned, the second derived value of Mass production parameter to be checked; And statistic unit is also for meeting pre-conditioned destination object corresponding to parameter to be checked according to the first derived value and the second derived value bulk statistics.
Further, this data processing equipment also comprises: the second generation unit, for before the first derived value of Mass production parameter to be checked, Mass production parameter to be checked for representing that parameter to be checked meets pre-conditioned the first sign, wherein, the first generation unit also for Mass production parameter to be checked for after representing that parameter to be checked meets pre-conditioned the first sign, in batches the first sign is converted into the first derived value.
Further, the first generation unit is also for being converted into 1 by the first sign in batches; And statistic unit is also for adding up 1, is met the quantity of destination object corresponding to pre-conditioned parameter to be checked.
By the present invention, the parameter to be checked that adopts batch query multidimensional data to concentrate, wherein, the parameter that parameter to be checked is destination object; The derived value of Mass production parameter to be checked, wherein, it is pre-conditioned that derived value is used for representing whether parameter to be checked meets; And meet pre-conditioned destination object corresponding to parameter to be checked according to derived value bulk statistics.Due to can the concentrated cell of batch processing multidimensional data, have therefore solved and in correlation technique, inquired about cube large problem consuming time, and then reached the effect of fast query inquiry cube.
Accompanying drawing explanation
The accompanying drawing that forms the application's a part is used to provide a further understanding of the present invention, and schematic description and description of the present invention is used for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is according to the schematic diagram of the data processing equipment for cube of the embodiment of the present invention;
Fig. 2 is the schematic diagram of the data processing equipment for cube of optional embodiment according to the present invention;
Fig. 3 is according to the process flow diagram of the data processing method for cube of the embodiment of the present invention; And
Fig. 4 is the process flow diagram of the data processing method for cube of optional embodiment according to the present invention.
Embodiment
It should be noted that, in the situation that not conflicting, embodiment and the feature in embodiment in the application can combine mutually.Describe below with reference to the accompanying drawings and in conjunction with the embodiments the present invention in detail.
In order to make those skilled in the art better understand the present invention program, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the embodiment of a part of the present invention, rather than whole embodiment.Embodiment based in the present invention, does not make the every other embodiment obtaining under creative work prerequisite those of ordinary skills, all should belong to protection scope of the present invention.
It should be noted that, the term " first " in instructions of the present invention and claims and above-mentioned accompanying drawing, " second " etc. are for distinguishing similar object, and needn't be for describing specific order or precedence.The data that should be appreciated that such use suitably can exchanged in situation, so as embodiments of the invention described herein can with except diagram here or describe those order enforcement.In addition, term " comprises " and " having " and their any distortion, is intended to be to cover not exclusive comprising.
According to embodiments of the invention, a kind of data processing equipment for cube is provided, should be used for for the data processing equipment of cube the concentrated cell data of batch processing multidimensional data to improve the speed of user's data query.
Fig. 1 is according to the schematic diagram of the data processing equipment for cube of the embodiment of the present invention.
As shown in Figure 1, this device comprises: query unit 10, the first generation unit 20 and statistic unit 30.
The parameter to be checked that query unit 10 is concentrated for batch query multidimensional data.Wherein, the parameter that parameter to be checked is destination object.
Parameter to be checked can refer to the data that customer analysis destination object relies on, and parameter to be checked can refer to the parameter that customer analysis destination object relies on, and it can comprise the order numbers of product.
For example, at a multidimensional data, concentrate, record all product types of Liao Mou enterprise and the order numbers of every kind of product type generation etc., if it is total several over the product of predetermined value that client wants to analyze order numbers, product just can be used as destination object so, and the order numbers of product just can be used as parameter to be checked.By inquiring about parameter to be checked, obtain Query Result, attribute information that can evaluating objects object according to this Query Result.Particularly, for example, multidimensional data is concentrated and has been recorded the first product, the second product, three products and four-product, the order numbers that corresponding the first product, the second product, three products and four-product produce is respectively 30,26,15 and 13, if being order numbers, querying condition is greater than 20, the Query Result obtaining is the first product and the second product, amounts to 2 kinds of products.
The parameter to be checked that batch query multidimensional data is concentrated can be to inquire about at one time a plurality of parameters to be checked, like this, can overcome and inquire about one by one the shortcoming that parameter to be checked need to consume a large amount of query times, reaches the effect of fast query.Based on above-mentioned example, the parameter to be checked that batch query multidimensional data is concentrated can be to inquire about the first product, the second product, three products and 4 kinds of order numbers corresponding to products difference of four-product simultaneously, rather than inquires about one by one order numbers corresponding to above-mentioned four kinds of products difference.
It should be noted that, except the order numbers of product, parameter to be checked can also comprise other parameters that customer analysis destination object relies on, for example, and the weight of product, volume, the even production time, shelf-life etc. of product.
The first generation unit 20 is for the derived value of Mass production parameter to be checked.
Wherein, to be used for representing whether parameter to be checked meets pre-conditioned for derived value.This is pre-conditioned can be preset value.
It should be noted that, by inquiring about parameter to be checked, filter out which parameter to be checked and meet pre-conditionedly, which parameter to be checked does not meet pre-conditioned, to meeting pre-conditioned and not meeting pre-conditioned parameter to be checked and distinguish by the derived value generating.Still take that at multidimensional data, to concentrate the order numbers of inquiry product whether to be greater than pre-conditioned be example, treating query argument meets pre-conditioned, can generate a kind of unified derived value, treating query argument does not meet pre-conditioned, can generate another kind of unified derived value, two kinds of derived values can be different derived values.After generating derived value, can be stored in and be derived from row.
In embodiments of the present invention, the mode of the derived value of Mass production parameter to be checked can comprise:
Mode one, to meeting pre-conditioned parameter all to be checked, generate a kind of identical derived value, and generate another kind of identical derived value to not meeting pre-conditioned parameter all to be checked, , to meeting and not meeting pre-conditioned parameter all to be checked, all generate derived value, but the derived value that meets pre-conditioned parameter generation to be checked is different from the derived value that does not meet pre-conditioned parameter generation to be checked, and all to meet the derived value that pre-conditioned parameter to be checked generates identical, and all not meet the derived value that pre-conditioned parameter to be checked generates also identical.
Mode two, only generates identical derived value to meeting pre-conditioned parameter all to be checked, and does not generate derived value to not meeting pre-conditioned parameter all to be checked.
Mode three, contrary with mode two, only to not meeting pre-conditioned parameter all to be checked, generate identical derived value, and do not generate derived value to meeting pre-conditioned parameter all to be checked.
By generating derived value, owing to having removed each inquiry from, all need to inquire about parameter to be checked and whether meet the pre-conditioned shared time, therefore can save query time for subsequent query, improve inquiry velocity.
Statistic unit 30 is for meeting pre-conditioned destination object corresponding to parameter to be checked according to derived value bulk statistics.
According to derived value bulk statistics, meet destination object corresponding to pre-conditioned parameter to be checked and can comprise that all or part of derived value statistics of basis meets pre-conditioned destination object corresponding to parameter to be checked.
Particularly, can add up according to the mode of derived value and the derived value of generation itself of above-mentioned Mass production parameter to be checked:
For mode one, can be simultaneously by meeting pre-conditioned and not meeting pre-conditioned derived value bulk statistics and meet pre-conditioned destination object corresponding to parameter to be checked, or can only by meeting pre-conditioned derived value bulk statistics, meet pre-conditioned destination object corresponding to parameter to be checked, or can first by adding up sum and the statistics of destination object, not meet the number that pre-conditioned derived value bulk statistics meets destination object corresponding to pre-conditioned parameter to be checked, then the sum of destination object is poor with the number that meets destination object corresponding to pre-conditioned parameter to be checked, obtain the difference of above-mentioned two numbers, wherein, this difference is the number that meets destination object corresponding to pre-conditioned parameter to be checked.
For mode two, can meet pre-conditioned destination object corresponding to parameter to be checked by meeting the direct bulk statistics of pre-conditioned derived value.
For mode three, can first by not meeting the direct bulk statistics of pre-conditioned derived value, not meet the number of destination object corresponding to pre-conditioned parameter to be checked, add up again the sum of destination object to be analyzed, then the number of the sum of the destination object destination object corresponding with not meeting pre-conditioned parameter to be checked is poor, the difference obtaining is the number that meets destination object corresponding to pre-conditioned parameter to be checked.
In embodiments of the present invention, the mode that can express by multidimensional (Multi-Dimensional Expressions is called for short MDX) is carried out the batch query (Bulk) that multidimensional data is concentrated cell.Particularly, can realize by the mode of iif and sum combination the query pattern of Bulk.For example, the product that is greater than k to generating quantity on order generates derived value m, and the product that is not more than k to generating quantity on order generates derived value n, can use following query statement to inquire about: Sum ([product] .[product] .[product], Iif ([Measures] .[Internet Order Quantity] >k, m, n)).
Pass through the embodiment of the present invention, adopt batch query parameter to be checked and generate the mode of the derived value of parameter to be checked, the shortcoming that can avoid inquiring about one by one parameter to be checked and Query Result be carried out to real-time counting, has reached the effect of saving query time, improving inquiry velocity.
Fig. 2 is the schematic diagram of the data processing equipment for cube of optional embodiment according to the present invention.
As shown in Figure 2, this embodiment can be used as preferred implementation embodiment illustrated in fig. 1, the data processing equipment for cube of this embodiment, except comprising query unit 10, the first generation unit 20 and the statistic unit 30 of the first embodiment, also comprises judging unit 40.
Identical with the first embodiment of the effect of query unit 10, does not repeat them here.
Judging unit 40 is pre-conditioned for judging whether parameter to be checked meets in batches.
Particularly, can be in batches by parameter to be checked and pre-conditioned to compare to judge whether parameter to be checked meets pre-conditioned.Wherein, if judge in batches parameter to be checked meet pre-conditioned, the derived value of Mass production parameter to be checked.If judge in batches parameter to be checked, do not meet pre-conditionedly, do not generate the derived value of parameter to be checked.After generating derived value, can be stored in and be derived from row.
Like this, only in parameter to be checked, meet when pre-conditioned and generate derived value, can save for storing the storage space of derived value.
The first generation unit 20 is also for the first derived value of Mass production parameter to be checked.
If judge in batches parameter to be checked meet pre-conditioned, the first derived value of Mass production parameter to be checked.If judge in batches parameter to be checked, do not meet pre-conditionedly, do not do any processing.
Statistic unit 30 is also for meeting pre-conditioned destination object corresponding to parameter to be checked according to the first derived value bulk statistics.
The first derived value can be shaping data, and because meeting pre-conditioned parameter to be checked, each there is first derived value, like this, by a plurality of the first derived values itself or its distortion are carried out to accumulation calculating, can count the quantity that meets destination object corresponding to pre-conditioned parameter to be checked.
Pass through the embodiment of the present invention, employing amount judges whether parameter to be checked meets pre-conditioned mode, make only to meeting pre-conditioned parameter to be checked, to carry out the function that generates derived value, simplified the process of the generation of derived value, and reached the effect of saving storage space.
Alternatively, in embodiments of the present invention, judge in batches parameter to be checked whether meet pre-conditioned after, if meet pre-conditioned except judging parameter to be checked in batches, outside the first derived value of Mass production parameter to be checked, can also comprise that the first generation unit 20 is also for carrying out the function of the second derived value of Mass production parameter to be checked if judging in batches parameter to be checked does not meet pre-conditionedly.Wherein, the first derived value and the second derived value are different derived values.Like this, statistic unit 30 is also for meeting pre-conditioned destination object corresponding to parameter to be checked according to the first derived value and the second derived value bulk statistics.The first derived value and the second derived value can be all shaping data, and because meeting pre-conditioned parameter to be checked, each there is first derived value, each does not meet pre-conditioned parameter to be checked and has second derived value, like this, by a plurality of the first derived values itself or its distortion and a plurality of the first derived value itself or its distortion are carried out to accumulation calculating, can count the quantity that meets destination object corresponding to pre-conditioned parameter to be checked.
Alternatively, in embodiments of the present invention, this data processing equipment can also comprise: the second generation unit.The second generation unit is for before the first derived value of Mass production parameter to be checked, Mass production parameter to be checked for representing that parameter to be checked meets pre-conditioned the first sign, wherein, the first sign can be one section of key word, and this key word can be character string.The first generation unit 20 can also for Mass production parameter to be checked for after representing that parameter to be checked meets pre-conditioned the first sign, in batches the first sign is converted into the first derived value.
Further alternatively, the first generation unit 20 can also, in batches the first sign being converted into 1, being about to each first sign and being converted into 1.Like this, statistic unit 30 can also, for all the first derived values 1 are added up, be met the quantity of destination object corresponding to pre-conditioned parameter to be checked.
In like manner, before the second derived value of Mass production parameter to be checked, this data processing equipment can also comprise: the 3rd generation unit.The 3rd generation unit for Mass production parameter to be checked for representing that parameter to be checked meets pre-conditioned the second sign, wherein, the second sign can be one section of key word, this key word can be character string.It should be noted that, the first sign is different from the second sign.The first generation unit 20 can also for Mass production parameter to be checked for after representing that parameter to be checked meets pre-conditioned the second sign, in batches the second sign is converted into the second derived value.Further, the first generation unit 20 can also, in batches the second sign being converted into 0, being about to each second sign and being converted into 0.Like this, statistic unit 30 also, for all the first derived values 1 and the second all derived values 0 are added up, is met the quantity of destination object corresponding to pre-conditioned parameter to be checked.For example, the product that is greater than k to generating quantity on order generates derived value 1, and the product that is not more than k to generating quantity on order generates derived value 0, can use following query statement to inquire about: Sum ([product] .[product] .[product], Iif ([Measures] .[Internet Order Quantity] >k, 1,0)).Like this, can not need each derived value to be out of shape, and directly cumulative existing derived value can count the quantity that meets pre-conditioned destination object, thereby simplify statistic processes.
According to embodiments of the invention, a kind of data processing method for cube is provided, should be used for for the data processing method of cube the concentrated cell data of batch processing multidimensional data to improve the speed of user's data query.Should may operate in computer-processing equipment for the data processing method of cube.It should be noted that, the data processing method for cube that the embodiment of the present invention provides can be carried out by the data processing equipment for cube of the embodiment of the present invention, and the data processing equipment for cube of the embodiment of the present invention also can be for carrying out the data processing method for cube of the embodiment of the present invention.
Fig. 3 is according to the process flow diagram of the data processing method for cube of the embodiment of the present invention.
As shown in Figure 3, the method comprises that following step S302 is to step S306:
Step S302, the parameter to be checked that batch query multidimensional data is concentrated.
Parameter to be checked can refer to the data that customer analysis destination object relies on, and parameter to be checked can refer to the parameter that customer analysis destination object relies on, and it can comprise the order numbers of product.
For example, at a multidimensional data, concentrate, record all product types of Liao Mou enterprise and the order numbers of every kind of product type generation etc., if it is total several over the product of predetermined value that client wants to analyze order numbers, product just can be used as destination object so, and the order numbers of product just can be used as parameter to be checked.By inquiring about parameter to be checked, obtain Query Result, attribute information that can evaluating objects object according to this Query Result.Particularly, for example, multidimensional data is concentrated and has been recorded the first product, the second product, three products and four-product, the order numbers that corresponding the first product, the second product, three products and four-product produce is respectively 30,26,15 and 13, if being order numbers, querying condition is greater than 20, the Query Result obtaining is the first product and the second product, amounts to 2 kinds of products.
The parameter to be checked that batch query multidimensional data is concentrated can be to inquire about at one time a plurality of parameters to be checked, like this, can overcome and inquire about one by one the shortcoming that parameter to be checked need to consume a large amount of query times, reaches the effect of fast query.Based on above-mentioned example, the parameter to be checked that batch query multidimensional data is concentrated can be to inquire about the first product, the second product, three products and 4 kinds of order numbers corresponding to products difference of four-product simultaneously, rather than inquires about one by one order numbers corresponding to above-mentioned four kinds of products difference.
It should be noted that, except the order numbers of product, parameter to be checked can also comprise other parameters that customer analysis destination object relies on, for example, and the weight of product, volume, the even production time, shelf-life etc. of product.
Step S304, the derived value of Mass production parameter to be checked.
Wherein, to be used for representing whether parameter to be checked meets pre-conditioned for derived value.This is pre-conditioned can be preset value.
It should be noted that, by inquiring about parameter to be checked, filter out which parameter to be checked and meet pre-conditionedly, which parameter to be checked does not meet pre-conditioned, to meeting pre-conditioned and not meeting pre-conditioned parameter to be checked and distinguish by the derived value generating.Still take that at multidimensional data, to concentrate the order numbers of inquiry product whether to be greater than pre-conditioned be example, treating query argument meets pre-conditioned, can generate a kind of unified derived value, treating query argument does not meet pre-conditioned, can generate another kind of unified derived value, two kinds of derived values can be different derived values.After generating derived value, can be stored in and be derived from row.
In embodiments of the present invention, the mode of the derived value of Mass production parameter to be checked can comprise:
Mode one, to meeting pre-conditioned parameter all to be checked, generate a kind of identical derived value, and generate another kind of identical derived value to not meeting pre-conditioned parameter all to be checked, , to meeting and not meeting pre-conditioned parameter all to be checked, all generate derived value, but the derived value that meets pre-conditioned parameter generation to be checked is different from the derived value that does not meet pre-conditioned parameter generation to be checked, and all to meet the derived value that pre-conditioned parameter to be checked generates identical, and all not meet the derived value that pre-conditioned parameter to be checked generates also identical.
Mode two, only generates identical derived value to meeting pre-conditioned parameter all to be checked, and does not generate derived value to not meeting pre-conditioned parameter all to be checked.
Mode three, contrary with mode two, only to not meeting pre-conditioned parameter all to be checked, generate identical derived value, and do not generate derived value to meeting pre-conditioned parameter all to be checked.
By generating derived value, owing to having removed each inquiry from, all need to inquire about parameter to be checked and whether meet the pre-conditioned shared time, therefore can save query time for subsequent query, improve inquiry velocity.
Step S306, meets pre-conditioned destination object corresponding to parameter to be checked according to derived value bulk statistics.
According to derived value bulk statistics, meet destination object corresponding to pre-conditioned parameter to be checked and can comprise that all or part of derived value statistics of basis meets pre-conditioned destination object corresponding to parameter to be checked.
Particularly, can add up according to the mode of the derived value of Mass production parameter to be checked in above-mentioned steps and the derived value of generation itself:
For mode one, can be simultaneously by meeting pre-conditioned and not meeting pre-conditioned derived value bulk statistics and meet pre-conditioned destination object corresponding to parameter to be checked, or can only by meeting pre-conditioned derived value bulk statistics, meet pre-conditioned destination object corresponding to parameter to be checked, or can first by adding up sum and the statistics of destination object, not meet the number that pre-conditioned derived value bulk statistics meets destination object corresponding to pre-conditioned parameter to be checked, then the sum of destination object is poor with the number that meets destination object corresponding to pre-conditioned parameter to be checked, obtain the difference of above-mentioned two numbers, wherein, this difference is the number that meets destination object corresponding to pre-conditioned parameter to be checked.
For mode two, can meet pre-conditioned destination object corresponding to parameter to be checked by meeting the direct bulk statistics of pre-conditioned derived value.
For mode three, can first by not meeting the direct bulk statistics of pre-conditioned derived value, not meet the number of destination object corresponding to pre-conditioned parameter to be checked, add up again the sum of destination object to be analyzed, then the number of the sum of the destination object destination object corresponding with not meeting pre-conditioned parameter to be checked is poor, the difference obtaining is the number that meets destination object corresponding to pre-conditioned parameter to be checked.
In embodiments of the present invention, the mode that can express by multidimensional (Multi-Dimensional Expressions is called for short MDX) is carried out the batch query (Bulk) that multidimensional data is concentrated cell.Particularly, can realize by the mode of iif and sum combination the query pattern of Bulk.For example, the product that is greater than k to generating quantity on order generates derived value m, and the product that is not more than k to generating quantity on order generates derived value n, can use following query statement to inquire about: Sum ([product] .[product] .[product], Iif ([Measures] .[Internet Order Quantity] >k, m, n)).
Pass through the embodiment of the present invention, adopt batch query parameter to be checked and generate the mode of the derived value of parameter to be checked, the shortcoming that can avoid inquiring about one by one parameter to be checked and Query Result be carried out to real-time counting, has reached the effect of saving query time, improving inquiry velocity.
Fig. 4 is the process flow diagram of the data processing method for cube of optional embodiment according to the present invention.
As shown in Figure 4, should comprise that following step S402 was to step S408 for the data processing method of cube, this embodiment can be used as preferred implementation embodiment illustrated in fig. 3.
Step S402, the step S302 with embodiment illustrated in fig. 3, does not repeat them here.
Step S404, judges whether parameter to be checked meets pre-conditioned in batches.
Wherein, the parameter that parameter to be checked is destination object.Particularly, can be in batches by parameter to be checked and pre-conditioned to compare to judge whether parameter to be checked meets pre-conditioned.Wherein, if judge in batches parameter to be checked meet pre-conditioned, the derived value of Mass production parameter to be checked.If judge in batches parameter to be checked, do not meet pre-conditionedly, do not generate the derived value of parameter to be checked.After generating derived value, can be stored in and be derived from row.
Like this, only in parameter to be checked, meet when pre-conditioned and generate derived value, can save for storing the storage space of derived value.
Step S406, if judge in batches parameter to be checked meet pre-conditioned, the first derived value of Mass production parameter to be checked.
If judge in batches parameter to be checked meet pre-conditioned, the first derived value of Mass production parameter to be checked.If judge in batches parameter to be checked, do not meet pre-conditionedly, do not do any processing.
Step S408, meets pre-conditioned destination object corresponding to parameter to be checked according to the first derived value bulk statistics.
The first derived value can be shaping data, and because meeting pre-conditioned parameter to be checked, each there is first derived value, like this, by a plurality of the first derived values itself or its distortion are carried out to accumulation calculating, can count the quantity that meets destination object corresponding to pre-conditioned parameter to be checked.
Pass through the embodiment of the present invention, employing amount judges whether parameter to be checked meets pre-conditioned mode, make only to meeting pre-conditioned parameter to be checked, to carry out the function that generates derived value, simplified the process of the generation of derived value, and reached the effect of saving storage space.
Alternatively, in embodiments of the present invention, judge in batches parameter to be checked whether meet pre-conditioned after, if meet pre-conditioned except judging parameter to be checked in batches, outside the first derived value of Mass production parameter to be checked, can also comprise if judge in batches parameter to be checked do not meet pre-conditioned, the step of the second derived value of Mass production parameter to be checked.Wherein, the first derived value and the second derived value are different derived values.Like this, according to derived value bulk statistics, meeting destination object corresponding to pre-conditioned parameter to be checked can comprise: according to the first derived value and the second derived value bulk statistics, meet pre-conditioned destination object corresponding to parameter to be checked.The first derived value and the second derived value can be all shaping data, and because meeting pre-conditioned parameter to be checked, each there is first derived value, each does not meet pre-conditioned parameter to be checked and has second derived value, like this, by a plurality of the first derived values itself or its distortion and a plurality of the first derived value itself or its distortion are carried out to accumulation calculating, can count the quantity that meets destination object corresponding to pre-conditioned parameter to be checked.
Alternatively, in embodiments of the present invention, before the first derived value of Mass production parameter to be checked, this data processing method can also comprise: Mass production parameter to be checked for representing that parameter to be checked meets pre-conditioned the first sign, wherein, the first sign can be one section of key word, and this key word can be character string.Mass production parameter to be checked for after representing that parameter to be checked meets pre-conditioned the first sign, the first derived value of Mass production parameter to be checked can comprise: in batches the first sign is converted into the first derived value.
Further alternatively, in batches the first sign being converted into the first derived value can comprise: in batches the first sign is converted into 1, is about to each first sign and is converted into 1.Like this, according to the first derived value bulk statistics, meeting pre-conditioned parameter to be checked can comprise: all the first derived values 1 are added up, be met the quantity of destination object corresponding to pre-conditioned parameter to be checked.
In like manner, before the second derived value of Mass production parameter to be checked, also data processing method can also comprise: Mass production parameter to be checked for representing that parameter to be checked meets pre-conditioned the second sign, wherein, the second sign can be one section of key word, and this key word can be character string.It should be noted that, the first sign is different from the second sign.Mass production parameter to be checked for after representing that parameter to be checked meets pre-conditioned the second sign, the second derived value of Mass production parameter to be checked can comprise: in batches the second sign is converted into the second derived value.Further, in batches the second sign being converted into the second derived value can comprise: in batches the second sign is converted into 0, is about to each second sign and is converted into 0.Like this, according to the first derived value and the second derived value bulk statistics, meeting pre-conditioned parameter to be checked can comprise: all the first derived values 1 and the second all derived values 0 are added up, be met the quantity of destination object corresponding to pre-conditioned parameter to be checked.For example, the product that is greater than k to generating quantity on order generates derived value 1, and the product that is not more than k to generating quantity on order generates derived value 0, can use following query statement to inquire about: Sum ([product] .[product] .[product], Iif ([Measures] .[Internet Order Quantity] >k, 1,0)).Like this, can not need each derived value to be out of shape, and directly cumulative existing derived value can count the quantity that meets pre-conditioned destination object, thereby simplify statistic processes.
From above description, can find out, the present invention can carry out mass simultaneous inquiry to the concentrated cell of multidimensional data, like this, because time of consuming of inquiry equals the query time of the longest cell consuming time, rather than time consuming summation during the inquiry of all cells, therefore, save query time, improved search efficiency.
It should be noted that, in the step shown in the process flow diagram of accompanying drawing, can in the computer system such as one group of computer executable instructions, carry out, and, although there is shown logical order in flow process, but in some cases, can carry out shown or described step with the order being different from herein.
Obviously, those skilled in the art should be understood that, above-mentioned each module of the present invention or each step can realize with general calculation element, they can concentrate on single calculation element, or be distributed on the network that a plurality of calculation elements form, alternatively, they can be realized with the executable program code of calculation element, thereby, they can be stored in memory storage and be carried out by calculation element, or they are made into respectively to each integrated circuit modules, or a plurality of modules in them or step are made into single integrated circuit module to be realized.Like this, the present invention is not restricted to any specific hardware and software combination.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for a person skilled in the art, the present invention can have various modifications and variations.Within the spirit and principles in the present invention all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (10)
1. for a data processing method for cube, it is characterized in that, comprising:
The parameter to be checked that batch query multidimensional data is concentrated, wherein, the parameter that described parameter to be checked is destination object;
The derived value of parameter to be checked described in Mass production, wherein, described derived value is used for representing whether described parameter to be checked meets pre-conditioned; And
According to described derived value bulk statistics, meet described destination object corresponding to described pre-conditioned parameter described to be checked.
2. data processing method according to claim 1, is characterized in that,
Before the derived value of parameter to be checked described in Mass production, described data processing method also comprises: judge whether described parameter to be checked meets in batches described pre-conditioned,
Wherein, if judge in batches described parameter to be checked, meet described pre-conditionedly, described in Mass production, the derived value of parameter to be checked comprises: the first derived value of parameter to be checked described in Mass production,
According to described derived value bulk statistics, meeting described pre-conditioned destination object corresponding to parameter described to be checked comprises: according to described the first derived value bulk statistics, meet described destination object corresponding to described pre-conditioned parameter described to be checked.
3. data processing method according to claim 2, is characterized in that,
Judge in batches described parameter to be checked whether meet described pre-conditioned after, if judging described parameter to be checked in batches does not meet described pre-conditioned, described in Mass production, the derived value of parameter to be checked comprises: the second derived value of parameter to be checked described in Mass production
According to described derived value bulk statistics, meeting described pre-conditioned destination object corresponding to parameter described to be checked comprises: according to described the first derived value and described the second derived value bulk statistics, meet described destination object corresponding to described pre-conditioned parameter described to be checked.
4. data processing method according to claim 2, is characterized in that,
Before the first derived value of parameter to be checked described in Mass production, described data processing method also comprises: described in Mass production parameter to be checked for representing that described parameter to be checked meets described pre-conditioned the first sign,
Wherein, parameter to be checked described in Mass production for after representing that described parameter to be checked meets pre-conditioned the first sign, the first derived value of parameter to be checked comprises described in Mass production: in batches will described first identify and be converted into described the first derived value.
5. data processing method according to claim 4, is characterized in that,
In batches described the first sign being converted into described the first derived value comprises: in batches described the first sign is converted into 1,
According to described the first derived value bulk statistics, meeting described pre-conditioned parameter described to be checked comprises: add up 1, be met the quantity of described destination object corresponding to described pre-conditioned parameter described to be checked.
6. for a data processing equipment for cube, it is characterized in that, comprising:
Query unit, the parameter to be checked of concentrating for batch query multidimensional data, wherein, the parameter that described parameter to be checked is destination object;
The first generation unit, for the derived value of parameter to be checked described in Mass production, wherein, described derived value is used for representing whether described parameter to be checked meets pre-conditioned; And
Statistic unit, for meeting described pre-conditioned destination object corresponding to parameter described to be checked according to described derived value bulk statistics.
7. data processing equipment according to claim 6, is characterized in that, described data processing equipment also comprises:
Judging unit, before the derived value in parameter to be checked described in Mass production, judges whether described parameter to be checked meets described pre-conditioned in batches,
Wherein:
If described the first generation unit also for judge in batches described parameter to be checked meet described pre-conditioned, the first derived value of parameter to be checked described in Mass production,
Described statistic unit is also for meeting described destination object corresponding to described pre-conditioned parameter described to be checked according to described the first derived value bulk statistics.
8. data processing equipment according to claim 7, is characterized in that,
Described the first generation unit also for judge in batches described parameter to be checked whether meet described pre-conditioned after, if judge in batches described parameter to be checked do not meet described pre-conditioned, the second derived value of parameter to be checked described in Mass production; And
Described statistic unit is also for meeting described destination object corresponding to described pre-conditioned parameter described to be checked according to described the first derived value and described the second derived value bulk statistics.
9. data processing equipment according to claim 7, is characterized in that, described data processing equipment also comprises:
The second generation unit, for before the first derived value of parameter to be checked described in Mass production, described in Mass production parameter to be checked for representing that described parameter to be checked meets described the first pre-conditioned sign,
Wherein, described the first generation unit also for parameter to be checked described in Mass production for after representing that described parameter to be checked meets pre-conditioned the first sign, in batches will described first identify and be converted into described the first derived value.
10. data processing equipment according to claim 9, is characterized in that,
Described the first generation unit is also for being converted into 1 by described the first sign in batches; And
Described statistic unit also, for adding up 1, is met the quantity of destination object corresponding to described pre-conditioned parameter described to be checked.
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