CN110209711B - Enterprise data mining system based on big data and use method thereof - Google Patents
Enterprise data mining system based on big data and use method thereof Download PDFInfo
- Publication number
- CN110209711B CN110209711B CN201910506831.9A CN201910506831A CN110209711B CN 110209711 B CN110209711 B CN 110209711B CN 201910506831 A CN201910506831 A CN 201910506831A CN 110209711 B CN110209711 B CN 110209711B
- Authority
- CN
- China
- Prior art keywords
- data
- user
- consumption
- value
- module
- 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.)
- Expired - Fee Related
Links
- 238000007418 data mining Methods 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000007405 data analysis Methods 0.000 claims abstract description 18
- 238000012216 screening Methods 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims description 19
- 239000003054 catalyst Substances 0.000 claims description 3
- 101000878595 Arabidopsis thaliana Squalene synthase 1 Proteins 0.000 claims 1
- 238000001914 filtration Methods 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 5
- 238000002372 labelling Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000012958 reprocessing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Fuzzy Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The utility model provides an enterprise data mining system based on big data and application method thereof, belongs to data mining technical field, including data acquisition module, database, data analysis module and treater, data input port is used for artifical received data, data acquisition module: for collecting user data; the data acquisition module and the data input port both transmit the acquired user data to the database; a database: receiving user data and performing hierarchical storage; a data analysis module: for data analysis of user data, the client screening module: the data screening device is used for screening data; a processor: the storage mode is used for sending data to the data pushing module and feeding back the adjustment database; the data pushing module: the method and the system are used for matching and pushing the data with the corresponding account, and the data mining depth and accuracy are improved by integrating a plurality of data sources and then performing multi-aspect and multi-dimensional dynamic analysis on the user data, so that an enterprise can match a proper strategic plan in time.
Description
Technical Field
The invention belongs to the technical field of data mining, and particularly relates to an enterprise data mining system based on big data and a using method thereof.
Background
Data Mining is a new business information processing technology, and the main characteristic of the technology is to extract, convert, analyze and otherwise model a large amount of business Data in a business database, and extract key Data for assisting business decisions from the business Data. In recent years, data mining has attracted great attention in the information industry, mainly because of the wide use of enterprise databases, there is a large amount of data, and there is an urgent need for knowledge to obtain useful information from these data. The information and knowledge obtained has a wide range of applications, for example: business management, production management, market control, market analysis, engineering design, scientific exploration and the like. More and more IT enterprises see the attractive market, join the development of data mining tools and obtain rich returns.
Data mining software commonly used in the market at present has a single data source, and the data analysis pertinence of users is not accurate enough, so that enterprises cannot make a proper strategic plan in time.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings and provides a big data-based enterprise data mining system and a using method thereof.
In order to solve the technical problems, the following technical scheme is adopted:
an enterprise data mining system based on big data comprises a data acquisition module, a database, a data analysis module and a processor, wherein the data input port is used for manually receiving data, and the data acquisition module is used for acquiring user data; the user data includes internet data, personal asset data, and operator data; the internet data comprises catalyst habits, interest preferences, search keywords and user IDs of users; the personal asset data comprises garage asset data and user consumption data, wherein the user consumption data comprises consumed products, consumption money, consumption times and consumption time; the operator data comprises user behavior data, user geographic position data and user social data, and the data acquisition module and the data input port both transmit the acquired user data to the database; the database receives the user data of the data acquisition module and the data input port and stores the user data in a grading way; the data analysis module is used for carrying out data analysis on user data to obtain first processing data, and the client screening module is used for screening the first processing data and pushing the obtained second processing data to the processor; the processor is used for receiving second processing data and sending the second processing data to the data pushing module and the feedback adjustment database storage mode; the data pushing module is used for receiving the data pushed by the processor and matching the data with the corresponding account for pushing.
Further, the specific steps of analyzing the user data by the data analysis module are as follows:
(1) labeling users through internet dataDetermining, namely calibrating the directivity through traces left by the user on the internet; marking T according to the corresponding degree of user and enterprise productsiN, N is 1,2,3 … 10, where (N is 1,2,3 … 10, the larger the value, the higher the correspondence between the user and the enterprise product).
(2) Determining the consumption capacity of the individual according to the personal asset data;
(2-1) first, the vehicle room asset is evaluated, wherein the data of the vehicle room asset is marked as Qi、TiI 1,2,3 … n, setting the impact factor η, and then using the formula:
M＝(Qi+Ti)×η＝(Q1+Q2+…Qi)×η+(T1+T2+…Ti)×η；
m represents the total value of the assessment assets, so as to preliminarily determine the consumption capacity of the user;
(2-2) comprehensively analyzing individual consuming ability of the user in conjunction with the analysis of the consuming data of the user, and labeling the consuming data of the user as RiThe consumer product in the consumption data is marked as ZiAnd the consumption amount XiConsumption number CiAnd consumption time ViI is 1,2,3 … n, and a predetermined scaling factor H1,H2；
① sum the sum of the spending amounts X ═ X1+X2+…Xi；
② consumption times are summed, C ═ C1+C2+…Ci；
Thirdly, counting the sum of all adjacent consumption time differences:
F＝(Ci-Ci-1)+…+(C2-C1)；
fourthly, calculating a formula according to the consumption capacity:
b represents the consumption capacity embodied in the consumption data, wherein the more the consumption times are, the larger the consumption capacity is, the larger the consumption amount is, and the stronger the consumption capacity is;
(3) comprehensively evaluating the consumption capacity L of the user according to the corresponding degree of the user and the enterprise and the consumption data of the useriI is 1,2,3 … n, and a standard value L of consumption level is presetbWhen L is presenti>LbAnd listing the user as the user to be selected.
Further, the customer screening module grades the users to be selected, and the specific screening steps are as follows:
(1) setting a heat of interest V for a producti，i＝1,2,3…n；
(2) Using the formula SC ═ Li×μ+Vi×ξ obtaining the customer success value SC, wherein, mu, ξ is the proportionality coefficient, the consumption ability L is obtained by the formulaiThe larger the value is, the larger the success value SC of the customer is, the larger the success rate of purchasing the marketing product on behalf of the customer is; heat of interest V for productiThe higher the customer success value SC is;
(3) dividing the client success value SC into ten grades according to intervals;
further, the data pushing module specifically comprises the following steps:
(1) acquiring the capacity value of a marketing staff, and reasonably pushing corresponding clients;
(1-1) calculating the working time T of the staff according to the working time T of the marketing staff by the ability value of the marketing staff, and setting the overtime coefficient
(1-2) recording the number of the allocated clients as O; the ability value of the marketing staff is recorded as K and is calculated by a formula
Calculating the ability value K of each marketing staff;
(1-3) Using the formula
Obtaining a matching value J of the marketing staff, wherein a1、a2And a3The matching values are all preset proportionality coefficients which can be obtained through a formula, and the longer the working time is, the larger the matching value is; the smaller the number of the distributed clients is, the larger the matching value is, the larger the ability value of the marketing staff is, and the larger the matching value is;
(1-4) corresponding the to-be-selected customers to the matching value J of the marketing staff according to the grades, and distributing the preferred customer with the highest customer grade to the name of the marketing staff with the largest matching value J;
(2) meanwhile, each piece of information which is automatically matched is sent to an enterprise planning department, so that the strategic objectives of the enterprise can be adjusted in time.
A big data-based enterprise data mining method comprises the following steps:
the method comprises the following steps: obtaining raw data packets from a plurality of data sources;
step two: the obtained original data packet is stored in a grading way according to the corresponding recognition;
step three: preprocessing the client data stored in a grading way, finding out the data needing to be associated, and obtaining first processing data;
step four: analyzing the first processed data to obtain second processed data;
step five: matching and screening the second processing data;
step six: sending the screened customer data to corresponding account numbers of related workers;
step seven: sending the unmatched data to a processor for reprocessing to obtain third processed data, and adjusting the hierarchical storage condition of the database;
step eight: the related staff logs in the corresponding account number and is familiar with the pushed related customer data information, so that strategic layout can be performed in a targeted manner;
step nine: and finishing the whole data mining process.
Due to the adoption of the technical scheme, the method has the following beneficial effects:
a plurality of data sources are integrated, and then multi-aspect and multi-dimensional dynamic analysis is carried out on user data, so that the depth and the accuracy of data mining are improved, and enterprises can match appropriate strategic plans in time.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a functional block diagram of a big data based enterprise data mining system of the present invention;
FIG. 2 is a block flow diagram of a big data based enterprise data mining system and method of using the same of the present invention.
In the figure: 100-a data acquisition module; 101-a data input port; 102-a database; 103-a data analysis module; 104-a customer screening module; 105-a processor; 106-a data push module; 200-step one; 201-step two; 202-step three; 203-step four; 204-step five; 205-step six; 206-step seven; 207-step eight; 208-step nine.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The first embodiment is as follows:
one big data based enterprise data mining system, as shown in fig. 1, includes a data collection module 100, a database 102, a data analysis module 103, and a processor 105.
The data input port 101 is used for manually receiving data, and the data acquisition module 100 is used for acquiring user data; wherein the user data comprises internet data, personal asset data and operator data.
The internet data comprises catalyst habits, interest preferences, search keywords and user IDs of users; the personal asset data comprises garage asset data and user consumption data, wherein the user consumption data comprises consumed products, consumption money, consumption times and consumption time;
the operator data includes user behavior data, user geolocation data, and user social data.
The data acquisition module 100 and the data input port 101 both transmit acquired user data to the database 102; the database 102 receives the user data of the data acquisition module 100 and the data input port 101 for hierarchical storage; the data analysis module 103 performs data analysis on the user data to obtain first processing data, and the specific steps are as follows:
(1) firstly, label determination is carried out on a user through internet data, namely, the directivity calibration is carried out through traces left on the internet by the user; marking T according to the corresponding degree of user and enterprise productsiN, N is 1,2,3 … 10, where (N is 1,2,3 … 10, the larger the value, the higher the correspondence between the user and the enterprise product).
(2) Determining the consumption capacity of the individual according to the personal asset data;
(2-1) first, the vehicle room asset is evaluated, wherein the data of the vehicle room asset is marked as Qi、TiI 1,2,3 … n, setting the impact factor η, and then using the formula:
M＝(Qi+Ti)×η＝(Q1+Q2+…Qi)×η+(T1+T2+…Ti)×η；
m represents the total value of the assessment assets, so as to preliminarily determine the consumption capacity of the user;
(2-2) comprehensively analyzing individual consuming ability of the user in conjunction with the analysis of the consuming data of the user, and labeling the consuming data of the user as RiThe consumer product in the consumption data is marked as ZiAnd the consumption amount XiConsumption number CiAnd consumption time ViI is 1,2,3 … n, and a predetermined scaling factor H1,H2；
① sum the sum of the spending amounts X ═ X1+X2+…Xi；
② consumption times are summed, C ═ C1+C2+…Ci；
Thirdly, counting the sum of all adjacent consumption time differences:
F＝(Ci-Ci-1)+…+(C2-C1)；
fourthly, calculating a formula according to the consumption capacity:
b represents the consumption capacity embodied in the consumption data, wherein the more the consumption times are, the larger the consumption capacity is, the larger the consumption amount is, and the stronger the consumption capacity is;
(3) comprehensively evaluating the consumption capacity L of the user according to the corresponding degree of the user and the enterprise and the consumption data of the useriI is 1,2,3 … n, and a standard value L of consumption level is presetbWhen L is presenti>LbAnd listing the user as the user to be selected.
The client screening module 104 is configured to screen the first processing data and push the obtained second processing data to the processor 105; namely, the client screening module 104 grades the users to be selected, and the specific screening steps are as follows:
(1) setting a heat of interest V for a producti，i＝1,2,3…n；
(2) Using the formula SC ═ Li×μ+Vi×ξ obtaining the customer success value SC, wherein, mu, ξ is the proportionality coefficient, the consumption ability L is obtained by the formulaiThe larger the value is, the larger the success value SC of the customer is, the larger the success rate of purchasing the marketing product on behalf of the customer is; heat of interest V for productiThe higher the customer success value SC is;
(3) dividing the client success value SC into ten grades according to intervals;
the processor 105 is configured to receive the second processing data, and send the second processing data to the data pushing module 106 and the storage manner of the feedback adjustment database 102; the data pushing module 106 is configured to receive data pushed by the processor 105 and push the data in a matching manner with a corresponding account, and includes the following specific steps:
(1) acquiring the capacity value of a marketing staff, and reasonably pushing corresponding clients;
(1-1)the ability value of the marketing staff calculates the working time T of the staff according to the working time T of the marketing staff, and sets the overtime coefficient
(1-2) recording the number of the allocated clients as O; the ability value of the marketing staff is recorded as K and is calculated by a formula
Calculating the ability value K of each marketing staff;
(1-3) Using the formula
Obtaining a matching value J of the marketing staff, wherein a1、a2And a3The matching values are all preset proportionality coefficients which can be obtained through a formula, and the longer the working time is, the larger the matching value is; the smaller the number of the distributed clients is, the larger the matching value is, the larger the ability value of the marketing staff is, and the larger the matching value is;
(1-4) corresponding the to-be-selected customers to the matching value J of the marketing staff according to the grades, and distributing the preferred customer with the highest customer grade to the name of the marketing staff with the largest matching value J;
(2) meanwhile, each piece of information which is automatically matched is sent to an enterprise planning department, so that the strategic objectives of the enterprise can be adjusted in time.
Fig. 2 shows a big data-based enterprise data mining method, which includes the following steps:
step one 200: obtaining raw data packets from a plurality of data sources;
step two 201: the obtained original data packet is stored in a grading way according to the corresponding recognition;
step three 202: preprocessing the client data stored in a grading way, finding out the data needing to be associated, and obtaining first processing data;
step four 203: analyzing the first processed data to obtain second processed data;
step five 204: matching and screening the second processing data;
step six 205: sending the screened customer data to corresponding account numbers of related workers;
step seven 206: sending the unmatched data to a processor for reprocessing to obtain third processed data, and adjusting the hierarchical storage condition of the database;
step eight 207: the related staff logs in the corresponding account number and is familiar with the pushed related customer data information, so that strategic layout can be performed in a targeted manner;
step nine 208: and finishing the whole data mining process.
The present invention has been described in terms of embodiments, and several variations and modifications can be made to the device without departing from the principles of the present invention. It should be noted that all the technical solutions obtained by means of equivalent substitution or equivalent transformation, etc., fall within the protection scope of the present invention.
Claims (3)
1. An enterprise data mining system based on big data comprises a data acquisition module (100), a database (102), a data analysis module (103), a client screening module (104) and a processor (105), wherein a data input port (101) is used for manually receiving data, and the data acquisition module (100) is used for acquiring user data; the user data includes internet data, personal asset data, and operator data; the internet data comprises catalyst habits, interest preferences, search keywords and user IDs of users; the personal asset data comprises garage asset data and user consumption data, wherein the user consumption data comprises consumed products, consumption money, consumption times and consumption time; the operator data comprises user behavior data, user geographic position data and user social data, and the data acquisition module (100) and the data input port (101) both transmit acquired user data to the database (102); the database (102) receives the user data of the data acquisition module (100) and the data input port (101) for hierarchical storage; the data analysis module (103) is used for performing data analysis on user data to obtain first processing data, and the client screening module (104) is used for screening the first processing data and pushing the obtained second processing data to the processor (105); the processor (105) is used for receiving second processing data and sending the second processing data to the data pushing module (106) and the storage mode of the feedback adjustment database (102); specifically, the customer filtering module (104) filters the following steps:
s1 setting the attention heat V to the producti，i＝1，2，3…n；
The S1 further includes the following:
s1-1: the ability value of the marketing staff calculates the working time T of the staff according to the working time T of the marketing staff, and sets the overtime coefficient
The specific formula is as follows:
s1-2: the number of clients allocated to the job so far is recorded as O; the ability value of the marketing staff is recorded as K and is calculated by a formula
Calculating the ability value K of each marketing staff;
s1-3: using formulas
Obtaining a matching value J of the marketing staff, wherein a1、a2And a3The matching values are all preset proportionality coefficients which can be obtained through a formula, and the longer the working time is, the larger the matching value is; the smaller the number of the distributed clients is, the larger the matching value is, the larger the ability value of the marketing staff is, and the larger the matching value is;
s1-4: the method comprises the steps of enabling customers to be selected to correspond to a matching value J of marketing staff according to the grade, and distributing the preferred customer with the highest customer grade to the marketing staff with the largest matching value J;
s2: using the formula SC ═ Li×μ+Vi×ξ obtaining the customer success value SC, wherein, mu, ξ is the proportionality coefficient, the consumption ability L is obtained by the formulaiThe larger the value is, the larger the success value SC of the customer is, the larger the success rate of purchasing the marketing product on behalf of the customer is; heat of interest V for productiThe higher the customer success value SC is;
s3: dividing the client success value SC into ten grades according to intervals;
then, the data pushing module (106) is configured to receive the data pushed by the processor (105) and match the data with a corresponding account for pushing, where the pushing specifically includes:
SS 1: acquiring the capacity value of a marketing staff, and reasonably pushing corresponding clients;
SS 2: meanwhile, each piece of information which is automatically matched is sent to an enterprise planning department, so that the strategic objectives of the enterprise can be adjusted in time.
2. The big-data-based enterprise data mining system according to claim 1, wherein the data analysis module (103) performs data analysis on user data to obtain first processed data, and the specific steps are as follows:
the method comprises the following steps: firstly, label determination is carried out on a user through internet data, namely, the directivity calibration is carried out through traces left on the internet by the user; marking T according to the corresponding degree of user and enterprise productsiN, N is 1,2,3 …, wherein the larger the value of N, the higher the correspondence between the user and the enterprise product;
step two: determining the consumption capacity of the individual according to the personal asset data;
step three: comprehensively evaluating the consumption capacity L of the user according to the corresponding degree of the user and the enterprise and the consumption data of the useriI is 1,2,3 …, and a standard value L of consumption level is presetbWhen L is presenti＞LbAnd listing the user as the user to be selected.
3. The big-data based enterprise data mining system of claim 2, wherein step two comprises the following:
i: first, the vehicle room assets are evaluated, wherein the data of the vehicle room assets are marked as Qi、TiI 1,2,3 …, setting the impact factor η, and then using the formula:
M＝(Qi+Ti)×η＝(Q1+Q2+…Qi)×η+(T1+T2+…Ti)×η；
wherein M represents the total value of the assessment assets, thereby preliminarily determining the consumption capacity of the user;
II: comprehensively analyzing the individual consumption capacity of the user by combining the consumption data analysis of the user, and marking the user consumption data as RiThe consumer product in the consumption data is marked as ZiAnd the consumption amount XiConsumption number CiAnd consumption time ViI is 1,2,3 …, and a predetermined scaling factor H1，H2(ii) a The method comprises the following specific steps:
① sum the sum of the spending amounts X ═ X1+X2+…Xi；
② consumption times are summed, C ═ C1+C2+…Ci；
Thirdly, counting the sum of all adjacent consumption time differences;
F＝(Ci-Ci-1)+…+(C2-C1)；
fourthly, calculating a formula according to the consumption capacity:
wherein, B represents the consumption ability embodied in the consumption data, wherein, the more the consumption times, the larger the consumption ability, the larger the consumption amount, and the stronger the consumption ability.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910506831.9A CN110209711B (en) | 2019-06-12 | 2019-06-12 | Enterprise data mining system based on big data and use method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910506831.9A CN110209711B (en) | 2019-06-12 | 2019-06-12 | Enterprise data mining system based on big data and use method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110209711A CN110209711A (en) | 2019-09-06 |
| CN110209711B true CN110209711B (en) | 2020-08-28 |
Family
ID=67792363
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910506831.9A Expired - Fee Related CN110209711B (en) | 2019-06-12 | 2019-06-12 | Enterprise data mining system based on big data and use method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110209711B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113886126A (en) * | 2021-10-13 | 2022-01-04 | 中里大数据服务(上海)有限公司 | Fault-tolerant server-based intelligent management and control system and method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101141484A (en) * | 2007-10-09 | 2008-03-12 | 华为技术有限公司 | Method, device and system of processing network directional data |
| CN103309990A (en) * | 2013-06-18 | 2013-09-18 | 上海晶樵网络信息技术有限公司 | User multidimensional analysis and monitoring method based on public information of Internet user |
| CN105069666A (en) * | 2015-09-14 | 2015-11-18 | 浙江工商大学 | E-commerce personalized recommendation method integrated with user implicit information |
| CN105869015A (en) * | 2016-03-28 | 2016-08-17 | 联想(北京)有限公司 | Information processing method and system |
| CN106408377A (en) * | 2016-08-31 | 2017-02-15 | 广东华邦云计算股份有限公司 | Shopping recommended method and system |
| CN106817384A (en) * | 2015-11-27 | 2017-06-09 | 亿阳信通股份有限公司 | A kind of analysis method and system that behavior is accessed based on user's telecommunications |
| CN106980991A (en) * | 2017-04-06 | 2017-07-25 | 百度在线网络技术（北京）有限公司 | A kind of method for pushing of marketing message, device, equipment and storage medium |
| CN107392656A (en) * | 2017-07-07 | 2017-11-24 | 芜湖恒天易开软件科技股份有限公司 | The formulation of the industry user that hires a car portrait and marketing effectiveness tracking |
| CN109359241A (en) * | 2018-09-18 | 2019-02-19 | 湖北函数科技有限公司 | User behavior big data analysis system Internet-based |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040034539A1 (en) * | 2002-08-14 | 2004-02-19 | Zitler Justin A. | Original way to sell new sound recordings |
| US8831972B2 (en) * | 2007-04-03 | 2014-09-09 | International Business Machines Corporation | Generating a customer risk assessment using dynamic customer data |
| CN107220777A (en) * | 2017-06-08 | 2017-09-29 | 合肥工业大学 | The social capital measure of Network brand community users based on entropy model |
-
2019
- 2019-06-12 CN CN201910506831.9A patent/CN110209711B/en not_active Expired - Fee Related
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101141484A (en) * | 2007-10-09 | 2008-03-12 | 华为技术有限公司 | Method, device and system of processing network directional data |
| CN103309990A (en) * | 2013-06-18 | 2013-09-18 | 上海晶樵网络信息技术有限公司 | User multidimensional analysis and monitoring method based on public information of Internet user |
| CN105069666A (en) * | 2015-09-14 | 2015-11-18 | 浙江工商大学 | E-commerce personalized recommendation method integrated with user implicit information |
| CN106817384A (en) * | 2015-11-27 | 2017-06-09 | 亿阳信通股份有限公司 | A kind of analysis method and system that behavior is accessed based on user's telecommunications |
| CN105869015A (en) * | 2016-03-28 | 2016-08-17 | 联想(北京)有限公司 | Information processing method and system |
| CN106408377A (en) * | 2016-08-31 | 2017-02-15 | 广东华邦云计算股份有限公司 | Shopping recommended method and system |
| CN106980991A (en) * | 2017-04-06 | 2017-07-25 | 百度在线网络技术（北京）有限公司 | A kind of method for pushing of marketing message, device, equipment and storage medium |
| CN107392656A (en) * | 2017-07-07 | 2017-11-24 | 芜湖恒天易开软件科技股份有限公司 | The formulation of the industry user that hires a car portrait and marketing effectiveness tracking |
| CN109359241A (en) * | 2018-09-18 | 2019-02-19 | 湖北函数科技有限公司 | User behavior big data analysis system Internet-based |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110209711A (en) | 2019-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106651424B (en) | Power user portrait establishing and analyzing method based on big data technology | |
| CN111062757B (en) | Information recommendation method and system based on multipath optimizing matching | |
| Dejnega | Method time driven activity based costing–literature review | |
| CN111612549B (en) | Construction method of platform operation service system | |
| CN110135504B (en) | Undergraduate part-time accurate matching method based on artificial intelligence | |
| US20120143816A1 (en) | Method and System of Information Matching in Electronic Commerce Website | |
| CN112269805B (en) | Data processing method, device, equipment and medium | |
| Greenberg et al. | Strategies for measuring risk in public use microdata files | |
| CN111026801A (en) | Method and system for assisting operation quick decision-making work of insurance type e-commerce | |
| CN101706926A (en) | Method for investigating and processing cigarette consumption information | |
| US8078616B2 (en) | Method of quantitative analysis of corporate communication performance | |
| CN111144831B (en) | Accurate selection screening system and method suitable for recruitment | |
| CN112632405A (en) | Recommendation method, device, equipment and storage medium | |
| Seah et al. | Design and implementation of data warehouse with data model using survey-based services data | |
| CN115601063A (en) | Advertisement putting method for shared equipment | |
| CN110209711B (en) | Enterprise data mining system based on big data and use method thereof | |
| CN117217634B (en) | Enterprise cooperation community discovery method based on complex network | |
| MacEachern | Measuring the added value of library and information services: the New Zealand approach | |
| Wowczko | A case study of evaluating job readiness with data mining tools and CRISP-DM methodology | |
| Das et al. | A Review of Data Warehousing Using Feature Engineering | |
| Macpherson | New product development among small industrial firms: A comparative assessment of the role of technical service linkages in Toronto and Buffalo | |
| CN113298560A (en) | Big data industry internet system | |
| Sulova | Integration of Structured and Unstructured Data in the Analysis of E-commerce Customers | |
| CN111311331A (en) | RFM analysis method | |
| CN107122125B (en) | Data processing method and system |
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 | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 310000 1-206, 206M, 5g Innovation Park, 1818-1 Wenyi West Road, Yuhang District, Hangzhou City, Zhejiang Province Applicant after: ZHEJIANG HUAKUN DAOWEI DATA TECHNOLOGY Co.,Ltd. Address before: 310016 Room 2404, Building A, Hualian Times Building, Jianggan District, Hangzhou City, Zhejiang Province Applicant before: ZHEJIANG HUAKUN DAOWEI DATA TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20200828 |