CN110706049B - Data processing method and device - Google Patents
Data processing method and device Download PDFInfo
- Publication number
- CN110706049B CN110706049B CN201810752707.6A CN201810752707A CN110706049B CN 110706049 B CN110706049 B CN 110706049B CN 201810752707 A CN201810752707 A CN 201810752707A CN 110706049 B CN110706049 B CN 110706049B
- Authority
- CN
- China
- Prior art keywords
- data
- user
- purchase
- model
- brand
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 15
- 238000012545 processing Methods 0.000 claims abstract description 33
- 238000004458 analytical method Methods 0.000 claims abstract description 32
- 238000000034 method Methods 0.000 claims description 19
- 238000005192 partition Methods 0.000 claims description 6
- 238000012946 outsourcing Methods 0.000 claims description 2
- 230000011218 segmentation Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 8
- 230000008901 benefit Effects 0.000 description 7
- 238000013507 mapping Methods 0.000 description 6
- 238000013499 data model Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000005553 drilling Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000035699 permeability Effects 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000004141 dimensional analysis Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides a data processing method, which comprises the following steps: establishing basic order data according to an order form; generating the re-purchase data based on the base order data and the re-purchase model, and generating the first-purchase data based on the base order data and the first-purchase model; and generating first summary data based on the buyback data, the first purchase data and the summary model, the first summary data including a plurality of analysis indicators based on the user concept. Basic order data, first purchase data, second purchase data and first summary data are sequentially obtained through three-level data processing, and the data processing pressure is relieved through the data processing of the level processing, so that the data processing efficiency is improved. The embodiment of the invention also provides a data processing device and a corresponding computer readable storage medium.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data processing method and apparatus.
Background
With the rapid development of business, the more important the electronic commerce companies pay attention to the marketing of customers, the more important the product can support the macroscopic data consideration. The business department provides various data reports in a targeted way by constructing a data model, and thus various data models are generated. Existing data models, such as order detail models, which can characterize sales order cases at commodity granularity; a sales commodity model that characterizes information of already sold commodities at a commodity sku (Stock Keeping Unit, stock unit) granularity, and the like.
However, the inventors have found that for the subject matter of the class brands, the relevant models do not support the business sector's newspaper data analysis requirements. For example, when the business department analyzes the combination of multi-level class+brand+Beijing shared value segments, data processing is needed from the bottom layer, and report data of class brands is finally generated through a plurality of steps, so that waste of manpower and material resources is caused. Moreover, since the amount of order data generated by the market every day is huge, the change of the brand of the product is frequent, and therefore, the processing efficiency is also considered in the process of generating report data.
Accordingly, there is a need to provide a more efficient data processing method to support the data analysis requirements for brands of goods.
Disclosure of Invention
In view of this, the embodiments of the present invention provide an efficient data processing method and apparatus to support the data analysis requirements of the brand.
According to a first aspect of an embodiment of the present invention, there is provided a data processing method, including:
establishing basic order data according to an order form;
generating re-purchase data based on the base order data and a re-purchase model, and generating first-purchase data based on the base order data and a first-purchase model; and
First summary data is generated based on the buyback data, the first purchase data and a summary model, the first summary data including a plurality of analysis indicators based on user concepts.
Preferably, the establishing the basic order data according to the order table includes:
and establishing the basic order data according to an order detail list, a sales commodity list and a brand list.
Preferably, the generating of the buyback data comprises:
generating the re-purchase data based on an order detail list, a user integral grade list and a three-level product first-time purchase model;
The generating first purchase data includes:
the first purchase data is generated based on an order detail table, a user score level table, and a three-level category first purchase model.
Preferably, the method further comprises: based on the first summary data, self-correlation of the summary model is employed to generate second summary data comprising historical and current pluralities of analysis indicators based on user concepts.
Preferably, the user concept is a user class comprising an overall user, an off-site new user, an on-site new user, an old user.
Preferably, the first summary data and the second summary data are stored in a combined partition based on time and brand of the category.
According to a second aspect of an embodiment of the present invention, there is provided a data processing apparatus including:
The data preparation unit is used for establishing basic order data according to the order form;
The first creating unit is used for generating the re-purchase data based on the basic order data and the re-purchase model and generating the first purchase data based on the basic order data and the first purchase model;
and the second creating unit is used for generating first summarized data based on the outsourcing data, the first purchasing data and a summarized model, wherein the first summarized data comprises a plurality of analysis indexes based on user concepts.
Preferably, the data preparing unit includes:
and establishing the basic order data according to an order detail list, a sales commodity list and a brand list.
Preferably, the first creation unit includes:
Generating the re-purchase data based on an order detail list, a user integral grade list and a three-level product first-time purchase model; and
The first purchase data is generated based on an order detail table, a user score level table, and a three-level category first purchase model.
Preferably, the method further comprises: and the self-association unit is used for generating second summarized data based on the first summarized data by adopting self-association of a summarized model, wherein the second summarized data comprises a plurality of historical and current analysis indexes based on user concepts.
Preferably, the user concept is a user class comprising an overall user, an off-site new user, an on-site new user, an old user. .
Preferably, the first summary data and the second summary data are partitioned in combination based on time and brand of the category.
According to a third aspect of embodiments of the present invention, there is provided a computer readable storage medium storing computer instructions which, when executed, implement the method of any one of claims 1 to 6.
According to a fourth aspect of an embodiment of the present invention, there is provided a data processing apparatus including:
a memory for storing computer instructions;
A processor coupled to the memory, the processor configured to perform a method implementing the above based on computer instructions stored by the memory.
Embodiments of the present invention have the following advantages or benefits: basic order data, first purchase data, second purchase data and first summary data are sequentially obtained through three-level data processing, and the data processing pressure is relieved through the data processing of the level processing, so that the data processing efficiency is improved.
The preferred embodiments of the present invention have the following advantages or benefits: the historical analysis index and the current analysis index are presented in the second summarized data, so that trend analysis of the brand subjects is facilitated.
The preferred embodiments of the present invention have the following advantages or benefits: the user concept comprising the whole user, the off-site new user, the on-site new user and the old user is provided, and the first summarized data and the second summarized data are obtained based on the user concept, so that index management based on the user concept is improved.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing embodiments thereof with reference to the following drawings in which:
FIG. 1 is a flow chart of a data processing method of an embodiment of the present invention;
FIG. 2 is an example of a data model of an embodiment of the present invention;
FIGS. 3a and 3b are schematic diagrams of summary models and self-associated summary models of embodiments of the present invention;
FIG. 4 is a block diagram of a data processing apparatus according to an embodiment of the present invention;
Fig. 5 is a block diagram of a data processing apparatus according to another embodiment of the present invention.
Detailed Description
The present invention is described below based on examples, but the present invention is not limited to only these examples. In the following detailed description of the present invention, certain specific details are set forth in detail. The present invention will be fully understood by those skilled in the art without the details described herein. Well-known methods, procedures, and flows have not been described in detail so as not to obscure the nature of the invention. The figures are not necessarily drawn to scale.
FIG. 1 is a flow chart of a data processing method according to an embodiment of the present invention. The method specifically comprises the following steps.
In step S101, basic order data is established according to an order table.
The data tables related to the subject of the class brands are numerous, and the order tables are important data therein. In addition, brand data and merchandise information in the data dictionary are also included. The data may be stored in different data sources, e.g., in different databases, which may be integrated into the same data source. In this step, base order data is created from the order form and other brand subject related forms. The outgoing base order data contains only critical and important data items relative to the original incoming order table.
In step S102, the repurchase data is generated based on the base order data and the repurchase model, and the first purchase data is generated based on the base order data and the first purchase model.
In the step, according to a pre-designed first purchase model, a mapping relation between a basic model corresponding to basic order data and the first purchase model is established according to an interface provided by a database platform, so that first purchase data is generated, and according to a pre-designed second purchase model, a mapping relation between the basic model corresponding to the basic order data and the second purchase model is established according to the interface provided by the database platform, so that second purchase data is generated. The repurchase model contains data items relating to the repurchase of the brand. The first purchase model contains data items related to the first purchase of the brand of the category. It should be understood that the interfaces provided on different database platforms are not identical, so after the repurchase model and the first purchase model are designed, different interfaces need to be called, and codes reflecting the mapping relation are written.
In step S103, a summary model is obtained from the repurchase model and the first purchase model.
The summary model contains a plurality of analysis indicators based on user concepts. The user concept is a classification based on a user's purchase settings for a category brand good. For example, the analysis index based on the user concept includes the following analysis index: effective order user number, re-purchase rate, effective order amount, category permeability, effective order user sub-channel occupancy, effective order user trend.
According to the embodiment of the invention, the basic order data, the first purchase data, the second purchase data and the first summary data are sequentially obtained through three-level data processing, and the data processing pressure is reduced through the data processing of the level processing, so that the data processing efficiency is improved.
In a preferred embodiment, the above embodiment further comprises step S104, in which step S104, based on the first summary data, self-correlation of the summary model is employed, generating second summary data comprising a plurality of analysis indicators based on the history and the current of the user concept. Since the first summary data represents the current analysis index, the periodic change rule of the analysis index cannot be displayed. In step S104, the second summary data generated by the self-associated summary model includes both the historical analysis index and the current analysis index. The historical analysis index and the current analysis index are both presented in the second summarized data, and can be directly output to the report.
Fig. 2 is an example of a data processing method to which an embodiment of the present invention is applied.
Corresponding to step S101, an order details table is obtained from the order details table (gdm_m04_ord_det_sum), the sales commodity model (gdm_m03_ sold _item_sku_da) and the brand table (fdm_forest_ brands _chain). Specifically, the order detail width table (gdm_m04_ord_det_sum) is taken as a main table, and a sales commodity table (gdm_m03_ sold _item_sku_da) and a brand table (fdm_forest_ brands _chain) are associated to obtain basic order data of dimensions and indexes such as a parent order ID, a child order ID, an order placing user, a primary order, a secondary order, a tertiary order, a brand, an order placing amount and the like.
Optionally, prior to step S101, invalid orders in the order detail table are purged, thereby improving the reliability of the underlying order data.
Corresponding to step S102, a mapping relation is established among the order detail list, the user integral grade list, the three-level product first purchase model and the repurchase model, so that repurchase data are obtained. And meanwhile, establishing a mapping relation among the order detail list, the user integral grade list, the three-level class first purchase model and the first purchase model, so as to obtain first purchase data. The re-purchase model and the first purchase model are stored by adopting two-stage combination partition: time (dt) +combination (tp). According to the permutation and combination of category+brand+Beijing share value segments, the list of tp is as follows: 00: full user; 01: a first stage; 02: primary+secondary; 03: primary, secondary and tertiary; 04: first-class + brand; 05: primary + secondary + brand; 06 primary, secondary, tertiary and brand; 07: segmentation of the Beijing share value; 08, first-level+Beijing shared value segmentation; 09: primary+secondary+Beijing shared value segmentation; 10: primary + secondary + tertiary + Beijing shared value segmentation; 11: first-order +brand +Beijing shared value segmentation; 12: primary+secondary+brand +Beijing shared value segmentation; 13: first-level, second-level, third-level, brand and Beijing share value segmentation, and 14 combination cases are obtained. In building the mapping, a window function of hive is technically used: GROUPING SETS, which are typically used in OLAP, cannot be accumulated and need to be varied. And index statistics of dimension up-drilling and down-drilling are used, specifically, in one GROUP BY query, aggregation is performed according to different dimension combinations, which is equivalent to performing UNION ALL on GROUP BY result sets of different dimensions. Wherein GROUPING __ ID indicates which set of packets the result belongs to. For example tp= '00' full user is calculated by case conv (GROUPING __ ID,10, 2) ehen 01000011 then '00'. The user purchase three-level product brand re-purchase table model is a slight summary of information such as primary product, secondary product, three-level product, brand, beijing share value segmentation, basic order quantity and amount under the granularity of the user. The first purchase list model of the user purchase product brands is basic information such as first purchase child orders (effective), first purchase father orders (effective), first purchase shopping time (effective), global first order marks and the like under the granularity of primary product, secondary product, tertiary product, brand, beijing sharing value segmentation and users.
Corresponding to step S103, association conditions are set between the repurchase model, the first purchase model and the summary model according to the interface function provided by the database platform, and an association relationship is established, so as to generate first summary data. And continuing to use a two-stage partition mode, and integrating the arrangement and combination of the category+brand+Beijing share value segments to obtain a summary model reflecting multi-dimensional category brand summary information, wherein the summary model comprises various analysis indexes based on user concepts, as shown in fig. 3 a. Notably, the user concept is defined in this example as a user class that includes the entire user, the off-site new user, the on-site new user, and the old user. Wherein, the whole user refers to the effective order users of the category in the counting period; the off-site new user refers to that in the statistics period, the user purchases the product for the first time in the effective order of the product, and the user is the first effective order of the user in Jingdong; a new user in the station means that in the statistics period, the user purchases the product for the first time in the effective order users of the product, but the user does not have the effective order for the first time in the Beijing east; old users refer to users who did not first purchase the category among the active order users of the category during the statistics period.
In the above example, a summary model of the brand-name topics is implemented based on the user concepts of the four dimensions of the overall user, off-site new user, on-site new user, old user, which model facilitates macro analysis of the brand-name topics.
Corresponding to step S104, self-correlation of the summary model is employed to generate second summary data containing a multi-dimensional historical analysis index and a multi-dimensional analysis index of the current time, as shown in fig. 3 b. The model adopts a two-stage partition mode, performs permutation and combination processing on category+brand+Beijing sharing value segments, takes category+brand+Beijing sharing value segments as units, and analyzes and summarizes indexes such as effective order user number, re-purchase rate, effective order amount, category permeability, effective order user sub-channel occupation ratio, effective order user trend and the like through four dimensions of an integral user, an off-site new user, an on-site new user and an old user. The self-association of the summarization model mainly comprises associating the same period of the last year, time partition of the last month and respectively obtaining analysis indexes of the same ratio and the ring ratio.
Fig. 3a and 3b are schematic diagrams of summary models and self-associated summary models of embodiments of the present invention. Not all data items are shown because of the limited space of the drawing.
Referring to fig. 3a, the dimension data items of the summary model include date, primary class, secondary class, tertiary class, brand code, and Beijing share value level code, and the index data items of the summary model include the number of users purchased in the month, the relative first time, the number of users purchased again three times, and so on. Referring to fig. 3b, the dimension data items of the comparison model are the same as those of the summary model, and the index data items of the comparison model include the index of the last period, the index of the last year, and the like in addition to the index of the current time. The self-associated summary model is a data model derived from the summary model and comprises historical analysis indexes and current analysis indexes based on the same dimension. The data generated by the self-associated summarization model can be directly output to a report, which is helpful for analyzing and displaying the periodical change trend of the brand.
Fig. 4 is a block diagram of a data processing apparatus 400 according to an embodiment of the present invention. Specifically, the data preparation unit 401, the first creation unit 402, and the second creation unit 403 are included.
The data preparation unit 401 is used to build basic order data from an order table. In an alternative embodiment, the data preparation unit 401 includes creating base order data from an order detail list, a sales item list, and a brand list.
The first creating unit 402 generates the repurchase data based on the base order data and the repurchase model, and generates the first purchase data based on the base order data and the first purchase model. Optionally, the first creating unit includes: generating re-purchase data based on the order detail list, the user integral grade list and the three-level product first purchase model; and generating first purchase data based on the order detail list, the user integral grade list and the third-level class first purchase model.
The second creating unit 403 generates first summary data including a plurality of analysis indexes based on the user concept based on the buyback data, the first purchase data, and the summary model.
In an alternative embodiment, the data processing apparatus 400 further includes: and a self-association unit for generating second summary data based on the first summary data by self-association of the summary model, wherein the second summary data comprises a plurality of historical and current analysis indexes based on user concepts.
In an alternative embodiment, the apparatus 400 may further include a self-association unit that self-associates the summary model to obtain a comparison model, where the comparison model includes a plurality of analysis indexes that are based on the history and the current of the user concept.
Fig. 5 is a block diagram of a data processing apparatus according to another embodiment of the present invention. The apparatus shown in fig. 5 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention in any way.
Referring to fig. 5, the data processing apparatus includes a processor 501, a memory 502, and an input-output device 503 connected by a bus. Memory 502 includes Read Only Memory (ROM) and Random Access Memory (RAM), and memory 502 stores various computer instructions and data required to perform system functions, processor 501 reads various computer instructions from memory 502 to perform various appropriate actions and processes. The input-output device includes an input section of a keyboard, a mouse, etc.; an output section including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage section including a hard disk or the like; and a communication section including a network interface card such as a LAN card, a modem, and the like. Memory 502 also stores the following computer instructions to perform the operations specified by the data processing method of embodiments of the present invention: establishing basic order data according to an order form; constructing a re-purchase model and a first purchase model based on order data; and obtaining a summary model according to the repurchase model and the first purchase model, wherein the summary model comprises a plurality of analysis indexes based on user concepts.
Accordingly, embodiments of the present invention provide a computer readable storage medium storing computer instructions that when executed perform operations specified by a data processing method.
The flowcharts, block diagrams in the figures illustrate the possible architectural framework, functions, and operations of the systems, methods, apparatus of the embodiments of the present invention, and the blocks in the flowcharts and block diagrams may represent a module, a program segment, or a code segment, which is an executable instruction for implementing the specified logical function(s). It should also be noted that the executable instructions that implement the specified logic functions may be recombined to produce new modules and program segments. The blocks of the drawings and the order of the blocks are thus merely to better illustrate the processes and steps of the embodiments and should not be taken as limiting the invention itself.
The various modules or units of the system may be implemented in hardware, firmware, or software. The software includes, for example, code programs formed using various programming languages such as JAVA, C/C++/C#, SQL, and the like. Although steps and sequences of steps of embodiments of the present invention are presented in terms of methods and apparatus, executable instructions for implementing the specified logical function(s) of the steps may be rearranged to produce new steps. The order of the steps should not be limited to only the order of the steps in the method and method illustration, but may be modified at any time as required by the function. For example, some of the steps may be performed in parallel or in reverse order.
Systems and methods according to the present invention may be deployed on a single or multiple servers. For example, different modules may be deployed on different servers, respectively, to form a dedicated server. Or the same functional units, modules, or systems may be distributed across multiple servers to relieve load pressure. The server includes, but is not limited to, a plurality of PCs, PC servers, blades, supercomputers, etc. connected on the same local area network and through the Internet.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of data processing, comprising:
Taking the order detail list as a main list, and associating the brand-related list of the product types to form an order detail list containing brand-type information as basic order data;
Generating repurchase data based on the basic order data and a repurchase model, wherein the repurchase data is grouping summarized data based on time, category, brand and order quantity and amount under user dimension indicated by the repurchase model;
generating first purchase data based on the basic order data and a first purchase model, wherein the first purchase data is basic information data of first purchase obtained under the time, the category, the brand and the user dimension indicated by the first purchase model;
First summary data is generated based on the repurchase data, the first purchase data, and a summary model, the first summary data including a plurality of analysis indicators under category and brand dimensions derived based on user concepts.
2. The data processing method of claim 1, wherein the method further comprises: based on the first summary data, self-correlation of the summary model is employed to generate second summary data comprising historical and current pluralities of analysis indicators based on user concepts.
3. The data processing method of claim 1, wherein the user concept is a user class including an overall user, an off-site new user, an on-site new user, an old user.
4. The data processing method of claim 2, wherein the first summary data and the second summary data type are stored in a combined partition based on time and brand of the category.
5. A data processing apparatus, comprising:
A data preparation unit for associating the brand-related list of the product types with the order detail list as a main list, and forming an order detail list containing brand-type information as basic order data;
A first creating unit, configured to generate, based on the basic order data and a purchased data, and generate, based on the basic order data and a first purchase model, first purchase data, where the purchased data is grouping summary data based on time, category, brand, and number of orders and amount in user dimension indicated by the purchased data, and the first purchase data is basic information data of first purchase obtained based on time, category, brand, and user dimension indicated by the first purchase model;
And the second creating unit is used for generating first summarized data based on the outsourcing data, the first purchasing data and a summarized model, wherein the first summarized data comprises a plurality of analysis indexes based on user concepts under category and brand dimensions.
6. The data processing apparatus of claim 5, further comprising: and the self-association unit is used for generating second summarized data based on the first summarized data by adopting self-association of a summarized model, wherein the second summarized data comprises a plurality of historical and current analysis indexes based on user concepts.
7. The data processing apparatus of claim 5, wherein the user concept is a user class including an overall user, an off-site new user, an on-site new user, an old user.
8. The data processing apparatus of claim 6, wherein the first aggregate data and the second aggregate data type are partitioned in combination based on time and category brands.
9. A computer readable storage medium storing computer instructions which, when executed, implement the method of any one of claims 1 to 4.
10. A data processing apparatus, comprising:
a memory for storing computer instructions;
A processor coupled to the memory, the processor configured to perform the method of any of claims 1-4 based on computer instructions stored by the memory.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810752707.6A CN110706049B (en) | 2018-07-10 | 2018-07-10 | Data processing method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810752707.6A CN110706049B (en) | 2018-07-10 | 2018-07-10 | Data processing method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110706049A CN110706049A (en) | 2020-01-17 |
| CN110706049B true CN110706049B (en) | 2024-08-20 |
Family
ID=69192620
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810752707.6A Active CN110706049B (en) | 2018-07-10 | 2018-07-10 | Data processing method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110706049B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113139835B (en) * | 2021-04-29 | 2024-08-20 | 北京深演智能科技股份有限公司 | Data processing method, device, nonvolatile storage medium and processor |
| CN113781106B (en) * | 2021-08-26 | 2023-11-21 | 唯品会(广州)软件有限公司 | Commodity operation data analysis method, device, equipment and computer readable medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107563816A (en) * | 2017-09-08 | 2018-01-09 | 携程计算机技术（上海）有限公司 | The Forecasting Methodology and system of the customer loss of e-commerce website |
| CN108021588A (en) * | 2016-10-28 | 2018-05-11 | 北京京东尚科信息技术有限公司 | A kind of user of Electronic Commerce purchases data integration method and device first |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2375630A (en) * | 2001-02-07 | 2002-11-20 | Unilever Plc | Consumer interaction system |
| CN101706926A (en) * | 2009-11-25 | 2010-05-12 | 河南省烟草公司鹤壁市公司 | Method for investigating and processing cigarette consumption information |
| CN104134108A (en) * | 2014-06-25 | 2014-11-05 | 上海艾瑞市场咨询有限公司 | Sales data analysis method of electronic commerce website |
| TWI666598B (en) * | 2016-12-01 | 2019-07-21 | 財團法人資訊工業策進會 | Inventory management system and inventory management method |
| CN106779961A (en) * | 2016-12-29 | 2017-05-31 | 江西博瑞彤芸科技有限公司 | Sales information statistical method |
| CN107273454B (en) * | 2017-05-31 | 2020-11-03 | 北京京东尚科信息技术有限公司 | User data classification method, device, server and computer readable storage medium |
-
2018
- 2018-07-10 CN CN201810752707.6A patent/CN110706049B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108021588A (en) * | 2016-10-28 | 2018-05-11 | 北京京东尚科信息技术有限公司 | A kind of user of Electronic Commerce purchases data integration method and device first |
| CN107563816A (en) * | 2017-09-08 | 2018-01-09 | 携程计算机技术（上海）有限公司 | The Forecasting Methodology and system of the customer loss of e-commerce website |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110706049A (en) | 2020-01-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113342821B (en) | Report configuration method, device, equipment and computer storage medium | |
| US20230316206A1 (en) | Methods and apparatus for the formatting of data values that may be arbitrary or indeterminate collected from a plurality of sources | |
| US10121157B2 (en) | Recommending user actions based on collective intelligence for a multi-tenant data analysis system | |
| US8190992B2 (en) | Grouping and display of logically defined reports | |
| US11436264B2 (en) | Graphical user interface for a database system | |
| US20140181728A1 (en) | Device, Method and User Interface for Switching between Graphical Representations of Data | |
| US20070254740A1 (en) | Concerted coordination of multidimensional scorecards | |
| US9639596B2 (en) | Processing data in a data warehouse | |
| US20210090105A1 (en) | Technology opportunity mapping | |
| US20170075896A1 (en) | System and method for analyzing popularity of one or more user defined topics among the big data | |
| US10922328B2 (en) | Method and system for implementing an on-demand data warehouse | |
| CN110706049B (en) | Data processing method and device | |
| CN107368501B (en) | Data processing method and device | |
| Bhaskara et al. | Data warehouse implemantation to support batik sales information using MOLAP | |
| US20070136352A1 (en) | Reporting row structure | |
| Goar et al. | Business decision making by big data analytics | |
| CN111027924A (en) | Project management system | |
| US20180357227A1 (en) | System and method for analyzing popularity of one or more user defined topics among the big data | |
| CN113052616A (en) | Cold chain product tracing method, device and system | |
| US20230052352A1 (en) | Systems and methods for assigning attribution weights to nodes | |
| JPH11259564A (en) | Sales prediction supporting system | |
| US20150081356A1 (en) | Dynamic multi-dimensional business reports | |
| CN106294494B (en) | Item set mining method and device | |
| US9489438B2 (en) | Systems and methods for visualizing master data services information | |
| Moreno et al. | Analysis of productivity, efficiency, and their explanatory factors in the fast fashion sector: New evidence from the international environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |