CN109189861A - Data stream statistics method, server and storage medium based on index - Google Patents
Data stream statistics method, server and storage medium based on index Download PDFInfo
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Abstract
The invention discloses provide a kind of data stream statistics method, server and storage medium based on index, which comprises target indicator information is obtained from indication information；The metric in the target indicator information is extracted in identification, and the dimension of single attribute is matched according to the target indicator metric, forms the combination of index dimension；Apply for that dimension index result table carries out intersection comparison, obtains cross-dimension index result table from multiple Data Marts by the combination of index dimension.The present invention is by for target indicator matching dimensionality, form the combination of index dimension, apply for dimension index result table in Data Mart from according to the combination of index dimension, cross-polymerization is carried out to the dimension index table applied, under the premise of improving application dimension result table efficiency, the higher dimension index result table of precision is obtained.
Description
Technical field
The present invention relates to data statistics field more particularly to a kind of data stream statistics method based on index, server and
Storage medium.
Background technique
With the development of mobile network, traditional performance statistics object, is no longer satisfied enterprise customer and refines
The requirement of operation, the user behavior analysis to come into being become the concern target of enterprise customer and improve the basis of profitability.
User behavior analysis can be counted by event log to user and media message content, these event logs and media
The content that message is included is united on the basis of event log and media message considerably beyond traditional performance statistics object
Meter and analysis can carry out depth analysis to a series of indexs such as system performance, user behavior, obtain more valuable information.
In the analysis application of user behavior, enterprise customer is required to from multiple dimensions or combination dimension, multi objective pair
User behavior is analyzed.
Statistics and analysis datamation one by one is carried out based on the behavior of single user in the prior art, it is not only cumbersome,
And cause analysis efficiency low.
Summary of the invention
The purpose of the present invention is in view of the above-mentioned drawbacks of the prior art, providing a kind of data stream statistics based on index
Method, server and storage medium.
The technical solution adopted by the present invention is that providing a kind of data stream statistics method based on index, the method first
Include:
Target indicator information is obtained from indication information；
The metric in the target indicator information is extracted in identification, is matched according to the target indicator metric single
The dimension of attribute forms the combination of index dimension；
Apply for that dimension index result table carries out intersection comparison, obtains and hands over from multiple Data Marts by the combination of index dimension
Pitch dimension index result table.
Preferably, the metric according to the target indicator matches the dimension of single attribute, forms index dimension
Combination includes:
According to the attributes correlation of the target indicator metric and dimension, screening matching is carried out to dimension, and configure
Corresponding priority；
The dimension that will match to is associated with the target indicator, forms the combination of index dimension, gained index dimension group
It closes and priority reconfiguration is carried out according to the priority of dimension.Pass through the attributes correlation of the target indicator metric and dimension
Screening matching is carried out to dimension, the screening operation to dimension can be greatly reduced, to improve matching speed.
Preferably, the attributes correlation according to the target indicator metric and dimension, screens dimension
Matching further include:
According to the selection temperature of the dimension and granularity, dimension matching granularity is related as by temperature.By the selection heat of granularity
Degree is matched, and selects the high granularity of temperature, the matching speed of granularity can be improved.
Preferably, the dimension that will match to is associated with the target indicator, is formed the combination of index dimension and is also wrapped
It includes:
The granularity of the dimension is associated with the index dimension combination.Granularity is associated with the index dimension combination
In, achievement data is preferably described by dimension.
Preferably, described to be combined before applying for dimension index result table in Data Mart by index dimension, the side
Method further include:
Valid data are screened from data warehouse constructs multiple Data Marts.Data Mart is constructed, number can be directly passed through
Data extraction is carried out according to fairground, with Data Mart as data relay, the speed of data extraction can be improved, and be significantly greatly increased
The efficiency and precision that data are extracted.
Preferably, the multiple Data Marts of valid data building that screen from data warehouse include:
The data in the data warehouse are screened by the attribute of dimension, and then filter out building Data Mart institute
The fact that need table and dimension table, the first Data Mart is constructed according to the fact that filter out table and dimension table.It can be in the fact
The achievement data of the target indicator is extracted in table, it can be to extract the dimension data of the dimension in dimension table.
The data in the data warehouse are screened by the priority of dimension, and then filter out building Data Mart
Required true table and dimension table construct the second Data Mart according to the fact that filter out table and dimension table.
Preferably, the data in Data Mart are managed and include the fact that table management and dimension table management.
Preferably, multiple dimension index results are preset in the Data Mart according to different dimensions attribute and indication information
Table.The multiple dimension index result table corresponds to multiple dimension indicator combinations, after dimension index result table is preset, Ke Yigen
It is directly called according to dimension indicator combination, improves the speed of application result table.
Secondly, also providing a kind of server, including processor and memory, at least one finger is stored in the memory
Enable, at least one section of program, code set or instruction set, at least one instruction, at least one section of program, the code set or
Described instruction collection is loaded by the processor and is executed to realize such as aforementioned described in any item data stream statistics based on index
Method.
Finally, also provide a kind of computer readable storage medium, at least one instruction, extremely is stored in the storage medium
Few one section of program, code set or instruction set, at least one instruction, at least one section of program, the code set or described
Instruction set is loaded by the processor and is executed to realize such as aforementioned described in any item data stream statistics methods based on index.
Compared with prior art, the present invention at least has the advantages that the present invention is tieed up by matching for target indicator
Degree forms the combination of index dimension, applies for dimension index result table in Data Mart from according to the combination of index dimension, arrives to application
Dimension index table carry out cross-polymerization, improve apply dimension result table efficiency under the premise of, obtain the higher dimension of precision
Index result table.
Detailed description of the invention
Fig. 1 is the implementation environment schematic diagram of the embodiment of the present invention；
Fig. 2 is the method flow diagram of the embodiment of the present invention；
Fig. 3 is the method flow diagram of the matching dimensionality of the embodiment of the present invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and examples.
Present invention firstly provides a kind of data stream statistics method, server and storage medium based on index.
As shown in Figure 1, the technical solution adopted by the present invention is that, a kind of data stream statistics method based on index is provided first
Implementation environment, the implementation environment of the method includes: terminal, and the terminal can be smart phone, intelligent robot, plate
And the smart machines such as computer, but it should be recognized that the terminal be not restricted to more than smart phone, intelligent robot,
The smart machines such as plate and computer, the terminal built-in have index pickup model, data extraction module and result display module.It removes
Outside terminal, the implementation environment further includes providing the data warehouse 1b of data basis, based on the data data in warehouse
The Data Mart 2b of formation, for request data and calculate data application layer 3b and for the presentation layer 4b of display data.
More preferably to illustrate the invention of the embodiment of the present invention to be intended to, the implementation environment can be specially that enterprise report is shown,
It, can be by being shown on the terminal (such as mobile phone) when the upward level display report of departmental staff.The terminal can lead to
The data warehouse 1b for crossing enterprise's setting extracts related data, constructs table according to the data of dimension and index, and at the terminal will
The table is shown.
As a kind of environment of possible implementation, the terminal can also extract related data as data by cloud database
Source, constructs Data Warehouse for Enterprises 1b according to the data source of extraction, constructs Data Mart further according to the data in the data warehouse
2b。
As shown in Fig. 2, the data stream statistics method based on index comprising steps of
S11, target indicator information is obtained from indication information；It is picked up from index by the indicator terminal pickup model
Target indicator out, and the information of target indicator is obtained, the information of the target indicator can be the information such as number, sales volume.
It further, is easier pickup target indicator, the indication information can be the label being arranged at the terminal
Or label cluster, target indicator information is obtained by picking up target labels, the different label corresponds to different indexs, and will
The label is associated in index dimensional relationships.Such setting can make what indication information intuitively changed to show at the terminal, side
Just it picks up.
As a kind of pickup target indicator mode of possible implementation, the pickup of the index can also be carried out using voice,
It is provided with speech recognition module in the terminal, the keyword in voice is identified by speech recognition module, thus
Target indicator is obtained, certainly, the semanteme of the keyword is associated with the indication information.For example " I wants to see to terminal input
Keyword in amount of access report " is " amount of access ", and corresponding index is " amount of access " after semanteme is identified, is visited then picking up
The amount of asking is target indicator.
S12, identification extract the metric in the target indicator information, are matched according to the target indicator metric
The dimension of single attribute forms the combination of index dimension；
Further, include the structure and unit of the target indicator data in the metric, can be described
Target indicator is matched to more accurately dimension.It should be noted that because index pair dimension more than one, such as user
It can include: time dimension, region dimension etc. with corresponding dimension that quantity, which increases index, so with the target indicator metric
Therefore the dimension more than one of matched single attribute forms the combination of index dimension and is more than one, but in these combinations,
Target indicator is constant always or number of users increases as target indicator.
For example, the metric in target indicator information is the number of visiting people measurement, can be with by this number of visiting people
It is matched to the number of visiting people of some time, the number of visiting people somewhere can also be matched to, can even be matched to some
The number of visiting people of age bracket.
Generally speaking, when needing to do a report to the number of visiting people, terminal can be issued using the number of visiting people as index
Instruction makes terminal obtain target indicator information the number of visiting people, but due to not having specified dimension attribute, so needing to the mesh
It marks indication information and carries out an association analysis, be the metric extracted in target indicator information, access people in above-mentioned example
Several metrics is number, then the dimensional attribute that can be distributed number can be matched according to number, for dividing
The most commonly used is time and regions for cloth number, that is to say, that the degree of association highest of time and region and the number of visiting people, it can be as
First priority is matched, and some degrees of association are lower, for example, the dimensions such as hobby, product can as the second priority, when
So, there are also the lower third priority of the degree of association and the 4th priority.
After matching dimensionality attribute, the combination of index dimension can be formed, application is carried out with index dimension and combines corresponding finger
Mark dimension result table.
S13, by the relationship between index and dimension, form the index dimension combination.Such as number ageing, people
Number place combination etc..Relationship between the index and dimension can be preset in the index dimensional relationships table in the data warehouse
In, certainly, faster to obtain index dimensional relationships, the index dimensional relationships can also be preset in the finger in the terminal
It marks in dimensional relationships table.
S14, apply for that dimension index result table carries out intersection comparison, obtains from multiple Data Marts by the combination of index dimension
Take cross-dimension index result table.
Further, multiple Data Marts are configured, apply for that dimension refers to from multiple Data Marts by the combination of index dimension
It, can be using correlation to keep the degree of correlation of the dimension index result table higher during mark result table carries out intersection comparison
Higher multiple Data Marts are spent to be applied.
It can be used as a kind of possible embodiment when needing to check the general geological coodinate system of the dimension index result table
Whether the not high multiple Data Marts of the degree of correlation are applied, and then the dimension index result table can be checked the multiple
It is general in Data Mart.
It should be noted that a target indicator may correspond to multiple dimensions, so that multiple index dimension combinations are formed,
And then can exist N number of to the multiple dimension index result tables of Data Mart application for example, a target indicator corresponds to N number of dimension
The combination of index dimension is S=N*M to the dimension index result table quantity of M Data Mart application.
It further, can be with when carrying out cross validation to the dimension index result table to keep the precision of result higher
It include: under the premise of identical dimensional attribute, because target indicator will not change, to the phase applied in different data fairground
Cross validation is carried out with dimensional attribute and the dimension index result table of granularity, obtains the result of cross validation.
Further, the result of the cross validation can be the intersection of multiple dimension index result tables, make the dimension
The precision of degree index result table is greatly improved.
As a kind of possible embodiment, need when carrying out Fuzzy Processing to data, the result of the cross validation
The union that can be multiple dimension index result tables keeps the covering scope of the dimension index result table bigger.
In the present embodiment, it should be noted that described to apply tieing up from multiple Data Marts by the combination of index dimension
Degree index result table intersect in the step of comparing, faster to obtain the dimension index table, the dimension index knot
Fruit table can be preset in the Data Mart.
In order to illustrate the embodiments of the present invention more clearly, need to the dimension index result table it is default further
It is bright, after carrying out permutation and combination between different indexs and different dimensional attributes, dimension index table is formed, not according to index
Corresponding factual data is extracted in data fact table, corresponding dimension is extracted in dimension table according to the difference of dimension
The factual data and dimension data are written and form dimension index result table in the dimension index table by data, and described
Classified index catalogue about the dimension index result table is set in Data Mart.
As shown in figure 3, in embodiments of the present invention, it is described that single attribute is matched according to the metric of the target indicator
Dimension, formed index dimension combination comprising steps of
S21, according to the attributes correlation of the target indicator metric and dimension, screening matching is carried out to dimension, and
Configure corresponding priority；It should be noted that the metric can be divided into absolute measure and opposite measurement, the measurement letter
Breath can be divided into absolute number measurement and relative number measurement, and what the absolute number measured reflection is the index of scale, such as population
Number, GDP, income, number of users, and relative number measurement is mainly used to reflect the index of quality, such as profit margin, retention ratio, covers
Lid rate etc..It may also be said that index is divided into absolute number index and relative number index, the absolute number index is aggregated data, such as
Population, GDP, income, number of users the time, place, range aggregated data, the relative number index be in absolute number index
Aggregated data on the basis of reprocessing polymerize to obtain, such as profit margin, retention ratio, coverage rate etc., in a profit margin formula:
In profit margin=profit ÷ cost × 100%, profit is an absolute number index, and cost is also an absolute number index, profit
Rate data are polymerizeing for profit data and cost data.
Further, in the step of carrying out screening matching to dimension described, according to the category of measure of criterions information and dimension
The property degree of correlation is screened, in measure of criterions information, including metric and linear module, from these metrics and linear module
Information can filter out the biggish dimension of the degree of correlation.For example, the measure of criterions information be profit margin, then can filter out with
The biggish such as time dimension of the profit margin index degree of correlation, place dimension, product dimension are matched, can also exclude it is some with it is sharp
The little such as gender dimension of the profit rate index degree of correlation, age dimension are rejected.
It further, is dimension configuration preference level, Ke Yi in described the step of being carried out to dimension and screening matching
Become orderly when forming the combination of index dimension, so that the priority for facilitating index dimension to combine is reconfigured.
In some possible embodiments, the attributes correlation of the measure of criterions information and dimension can be from the thing of user
It is extracted in part log, specifically, analyzing what measure of criterions information and dimension in event log were extracted by machine learning
Number obtains dimension of the extraction time under target indicator condition metric in range.
S22, the dimension that will match to and the target indicator are associated, and form the combination of index dimension, gained index dimension
Degree combination carries out priority reconfiguration according to the priority of dimension.Pass through the attribute phase of the target indicator metric and dimension
Guan Du carries out screening matching to dimension, can greatly reduce the screening operation to dimension, to improve matching speed.To institute
It states the combination of index dimension and carries out priority reconfiguration, the priority of the index dimension combination can be made to be different from the preferential of dimension
Grade, convenient for the management of index dimension data splitting and dimension data.
Further, by preset index dimensional relationships table, the dimension that will match to is closed with the target indicator
Connection forms the combination of index dimension.It, can be by the index dimensional relationships table in order to faster obtain the index dimensional relationships
It is preset in the terminal, to determine the relationship of the target indicator and dimension directly in the terminal, forms index dimension
Degree combination.
As a kind of possible embodiment, the index dimensional relationships table is preset in the Data Mart, by by institute
It states in the index dimensional relationships table that index dimensional relationships are mapped in the Data Mart, to extract index dimension group
It closes.The storage pressure of the terminal can be reduced in this way.
In embodiments of the present invention, the attributes correlation according to the target indicator metric and dimension, to dimension
Degree carries out screening matching further include:
According to the selection temperature of the dimension and granularity, dimension matching granularity is related as by temperature.By the selection heat of granularity
Degree is matched, and selects the high granularity of temperature, the matching speed of granularity can be improved.It should be noted that granularity is under dimension
A data unit of account, the granularities of data mainly for achievement data computer capacity, by taking the dimension of place as an example, such as population
This data item is using block range or a community as range statistics in statistical department.Demographic data degree of refinement is got over
Height, particle size fraction is just smaller, for example using community is that the range that granularity counts demographic data is greater than using residential building as granularity
The range that demographic data is counted；On the contrary, degree of refinement is lower, particle size fraction is bigger.
Further, in the attributes correlation according to the target indicator metric and dimension, dimension is screened
After matching, determine the attribute of dimension, screened in the granularity of the dimension, filter out the related higher granularity of temperature into
Row.Specific particle filter can be extraction common granularity unit under the dimensional attribute, and analyze these common grains
The number that is extracted in degree unit, carries out as these common granularity unit configuration preference levels, according to priority by particle size matching
Into dimension.
In some possible embodiments, the screening of granularity, which can also be according to the time, to be carried out, specifically, from finger
Mark in the step of obtaining target indicator information in information, while obtaining temporal information at that time, according to obtain it is described at that time when
Between information granularity is matched.For example, being certain month No.1 getting the time at that time, the report that may be done is about preceding
The various reports in one middle of the month of face in each week, then can match " week " is granularity；For another example, the time at that time is being got
It is December number, may is the various reports about front some months to do is to annual report form, then can match
" moon " is granularity.
In other possible embodiments, the screening of granularity can also by event log in conjunction with user and time come
Carry out, specifically, from indication information obtain target indicator information the step of in, while obtain user event log letter
Breath extracts the concrete behavior attribute of correlation time in the event log information of the user, passes through the concrete behavior attribute
To be matched to granularity.In this possible embodiment, it should be noted that the correlation time is in week regular hour
The interim time point being associated, for example target indicator information is obtained in certain month No.1, using the moon as the period, obtain No.1 last month
The event log of the user, and from the event log information of the user on the same day extract same day user concrete behavior category
Property, i.e., what some table made, granular information is obtained from these tables by this day in the month before, user, so that matching is corresponding
Granularity.
Certainly, in the setting in time point and period, due to report may need to do in advance or report show day delay
Pusher and so on, a Fuzzy Threshold can be arranged to the time point, for example, the Fuzzy Threshold can be with time point
Front and back two days, i.e., acquisition No.1 last month and its two days users in front and back event log, and from event in the past few days
The concrete behavior attribute that same day user is extracted in log information, to match corresponding granularity.
In embodiments of the present invention, the dimension that will match to is associated with the target indicator, forms index dimension
Degree combination further include:
The granularity of the dimension is associated with the index dimension combination.Granularity is associated with the index dimension combination
In, achievement data is preferably described by dimension.
Further, the granularity of the dimension is associated with index dimension combination comprising steps of
According to dimensional attribute, dimension table is set in the data warehouse, according to different attributes, in the dimension table
Granularity sublist is arranged in lower section, and the data about granularity are stored in the granularity sublist, are extracted from the granularity table relevant
Granularity data is mapped in the index dimension combination and is distributed.
In some possible embodiments, the dimension table and granularity sublist are arranged in the terminal in the form of catalogue
It stores, the data about granularity in the data and the granularity sublist about dimension in the dimension table are arranged in data bins
Among library, can according in terminal dimension table and granularity sublist the data among data warehouse are applied, improve shape
Under the premise of at index dimension group speed, moreover it is possible to reduce the storage pressure of the terminal.
In other possible embodiments, the dimension table and granularity sublist be can be only fitted in the Data Mart,
The granularity is associated in the Data Mart speed ratio of index dimension combination in the data warehouse
The speed that the granularity is associated with index dimension combination is fast, the storage capacity ratio of the Data Mart in the end
The storage capacity at end wants more excellent.
Certainly, it can also be configured in the terminal in conjunction with the above embodiments, the dimension table and granularity sublist, it is described
The data configuration about granularity in the data and the granularity sublist about dimension in dimension table the Data Mart it
In.
In embodiments of the present invention, dimension index result table is applied for from Data Mart by the combination of index dimension described
Before, the method also includes:
Valid data are screened from data warehouse constructs multiple Data Marts.Data Mart is constructed, number can be directly passed through
Data extraction is carried out according to fairground, with Data Mart as data relay, the speed of data extraction can be improved, and be significantly greatly increased
The efficiency and precision that data are extracted.
It further, can be according to the types of data and the building quickly to get dimension index result table
The requirement of Data Mart applies for the dimension index result table in the Data Mart；In the data warehouse
Relevant data are extracted as data source, corresponding Data Mart is constructed according to the data source extracted.
To keep the data extracted more accurate, the data in the Data Mart can be compared, specifically, by same
One dimension index table is applied in the data warehouse and the Data Mart respectively, compares the dimension index applied
As a result the data in table.
It should be noted that the Data Mart is equivalent to the data warehouse for data relay, compared to described
For data warehouse, the data specific aim of the Data Mart is stronger, and data search range is also smaller, so when extracting data
Speed can be than very fast.
In embodiments of the present invention, the multiple Data Marts of valid data building that screen from data warehouse include:
The data in the data warehouse are screened by the attribute of dimension, and then filter out building Data Mart institute
The fact that need table and dimension table, the first Data Mart is constructed according to the fact that filter out table and dimension table.It can be in the fact
The achievement data of the target indicator is extracted in table, and the dimension data of the dimension can be extracted in dimension table.
The data in the data warehouse are screened by the priority of dimension, and then filter out building Data Mart
Required true table and dimension table construct the second Data Mart according to the fact that filter out table and dimension table.
It should be noted that the fact table and dimension table are the data mediums in the data warehouse, the fact table
It is stored with the metric data about index, the dimension table, which is stored with, illustrates data about dimension.First Data Mart
With the construction strategy of second Data Mart and different, therefore, while from the first Data Mart and the second Data Mart
The result table that application comes out may also can have difference.Specifically it is based on the first Data Mart or with the second Data Mart
Main, user can self-defining.In addition, the building of Data Mart includes the first Data Mart and the second Data Mart, but not
It is limited to be the first Data Mart and the second Data Mart, third Data Mart, even more Data Marts can also be constructed.
Further, to obtain desired data, relevant data can be extracted in the data warehouse, with extracting
Data true table and dimension table are constructed in the Data Mart.
In some possible embodiments, the fact table and dimension table can be mentioned directly from the data warehouse
It takes, the building of the Data Mart can be made quicker in this way, moreover it is possible to avoid the shortage of data when constructing the Data Mart.
In embodiments of the present invention, the data in Data Mart are managed and include the fact that table management and dimension table pipe
Reason.
Further, table data, which update, to be included the fact that the true table management, increases or delete the data in true table
Type；It include the true data for increasing or deleting in dimension table to the dimension table management.It should be noted that the fact
Table and dimension table are formed into the key of the dimension index result table.
In some possible embodiments, the data in the Data Mart are only about the dimension index result table
Data, without related true table and dimension table is arranged, data source is in data warehouse, institute in the dimension index result table
The data for stating true table and the dimension table are polymerize in the data warehouse, form dimension index result table, the number
It is as needed according to fairground, the dimension index result table is directly extracted from the data warehouse to be stored, that is to say, that this
Data Mart in embodiment is only responsible for dimension index result table of the storage by polymerization, and configures index list, at the end
When the index dimension combination that end is formed according to target indicator information and dimension makes requests the Data Mart, institute is directly extracted
Dimension index result table is stated, without polymerizeing in the Data Mart to data.
In embodiments of the present invention, multiple dimensions are preset in the Data Mart according to different dimensions attribute and indication information
Spend index result table.The multiple dimension index result table corresponds to multiple dimension indicator combinations, and dimension index result table is preset
After good, it can be directly called according to dimension indicator combination, improve the speed of application result table.
Further, the rule that multiple dimension index result tables are preset in the Data Mart can be different dimensions
The dimension of attribute and different types of index carry out permutation and combination, obtain wanting preset the multiple dimension index result table.
It should be noted that also to be write in the multiple dimension index result table after presetting the multiple dimension index result table
Enter relevant data and explanation；The fact that the relevant data are index table data, the explanation is in the dimension
On data are illustrated to the achievement data.
In some possible embodiments, for the storage pressure for reducing the Data Mart, the relevant data and say
It is bright to go and extract again from the data warehouse after the terminal issues application.
Secondly, also providing a kind of server, including processor and memory, at least one finger is stored in the memory
Enable, at least one section of program, code set or instruction set, at least one instruction, at least one section of program, the code set or
Described instruction collection is loaded by the processor and is executed to realize such as aforementioned described in any item data stream statistics based on index
Method.
Processor in the server can be computing chip, to the dimension data in calculation processing database and refer to
The polymerization of data is marked, the memory may is that USB flash disk, read-only memory (ROM), random access memory (RAM), movement are hard
The various storage devices that can store program code such as disk, magnetic or disk.
Finally, also provide a kind of computer readable storage medium, at least one instruction, extremely is stored in the storage medium
Few one section of program, code set or instruction set, at least one instruction, at least one section of program, the code set or described
Instruction set is loaded by the processor and is executed to realize such as aforementioned described in any item data stream statistics methods based on index.
The computer readable storage medium includes: USB flash disk, read-only memory (ROM), random access memory (RAM), moves
The various media that can store program code such as dynamic hard disk, magnetic or disk.
Above-described embodiment is merely to illustrate a specific embodiment of the invention.It should be pointed out that for the general of this field
For logical technical staff, without departing from the inventive concept of the premise, several deformations and variation can also be made, these deformations and
Variation all should belong to protection scope of the present invention.
Claims (10)
1. a kind of data stream statistics method based on index, which is characterized in that the described method includes:
Target indicator information is obtained from indication information；
The metric in the target indicator information is extracted in identification, matches single attribute according to the target indicator metric
Dimension, formed index dimension combination；
Apply for that dimension index result table carries out intersection comparison, obtains cross dimension from multiple Data Marts by the combination of index dimension
Spend index result table.
2. the data stream statistics method based on index as described in claim 1, which is characterized in that described to be referred to according to the target
Target metric matches the dimension of single attribute, forms the combination of index dimension and includes:
According to the attributes correlation of the target indicator metric and dimension, screening matching is carried out to dimension, and configure corresponding
Priority；
The dimension that will match to is associated with the target indicator, forms the combination of index dimension, and gained index dimension combines root
Priority reconfiguration is carried out according to the priority of dimension.
3. the data stream statistics method based on index as claimed in claim 2, which is characterized in that described to be referred to according to the target
The attributes correlation for marking metric and dimension, carries out screening matching to dimension further include:
According to the selection temperature of the dimension and granularity, dimension matching granularity is related as by temperature.
4. the data stream statistics method based on index as claimed in claim 3, which is characterized in that the dimension that will match to
It is associated with the target indicator, forms the combination of index dimension further include:
The granularity of the dimension is associated with the index dimension combination.
5. the data stream statistics method based on index as described in claim 1, which is characterized in that described to pass through index dimension group
Close from multiple Data Marts apply dimension index result table carry out intersection comparison, obtain immediate dimension index result table it
Before, the method also includes:
Valid data are screened from data warehouse constructs multiple Data Marts.
6. the data stream statistics method based on index as claimed in claim 5, which is characterized in that described to be sieved from data warehouse
It selects valid data to construct multiple Data Marts to include:
The data in the data warehouse are screened by the attribute of dimension, and then are filtered out needed for building Data Mart
True table and dimension table construct the first Data Mart according to the fact that filter out table and dimension table；
The data in the data warehouse are screened by the priority of dimension, and then are filtered out needed for building Data Mart
The fact table and dimension table, the second Data Mart is constructed according to the fact that filter out table and dimension table.
7. the data stream statistics method based on index as claimed in claim 6, which is characterized in that the data in Data Mart
It is managed and includes the fact that table management and dimension table management.
8. the data stream statistics method based on index as claimed in claim 7, which is characterized in that according to different dimensions attribute and
Indication information presets multiple dimension index result tables in the Data Mart.
9. a kind of server, which is characterized in that including processor and memory, at least one finger is stored in the memory
Enable, at least one section of program, code set or instruction set, at least one instruction, at least one section of program, the code set or
Described instruction collection loaded by the processor and executed with realize as it is described in any item of the claim 1 to 8 based on index
Data stream statistics method.
10. a kind of computer readable storage medium, which is characterized in that be stored at least one instruction, extremely in the storage medium
Few one section of program, code set or instruction set, at least one instruction, at least one section of program, the code set or described
Instruction set is loaded by the processor and is executed to realize such as the data described in any item of the claim 1 to 8 based on index
Stream statistics method.
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