CN112256667A - Multi-biological characteristic normalization method - Google Patents
Multi-biological characteristic normalization method Download PDFInfo
- Publication number
- CN112256667A CN112256667A CN202010973725.4A CN202010973725A CN112256667A CN 112256667 A CN112256667 A CN 112256667A CN 202010973725 A CN202010973725 A CN 202010973725A CN 112256667 A CN112256667 A CN 112256667A
- Authority
- CN
- China
- Prior art keywords
- data
- biological characteristic
- node
- biometric
- establishing
- 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.)
- Granted
Links
- 238000010606 normalization Methods 0.000 title claims abstract description 22
- 238000013075 data extraction Methods 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims 1
- 238000000034 method Methods 0.000 abstract description 12
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000004061 bleaching Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 1
- 210000000554 iris Anatomy 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
- G06F16/21—Design, administration or maintenance of databases
-
- 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
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
-
- 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
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/288—Entity relationship models
-
- 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a multi-biological characteristic normalizing method, which comprises the following steps: s1, inputting the biological characteristic data into the collected database; s2, extracting newly added biological characteristic data and constructing a physical evidence entity relationship of the personnel; establishing a relationship between data of different biological characteristic acquisition record tables and data acquired in different batches, and establishing an entity relationship table; s3, constructing node and connection data; s4, constructing a tree structure by adopting a gallery algorithm; s5, establishing a suspect collection biological characteristic normalization data warehouse; and S6, establishing a biological person file page and displaying data. The invention can accurately group the biological characteristic data acquired by the same person for multiple times to establish a suspect biological characteristic acquisition normalization data warehouse, is convenient to inquire and display all entrance information of the history of the person and whether the acquired biological characteristics exist in comparison with the physical evidence of a certain case site every time, and assists in confirming the identity of the person with the unreal historical data identity card.
Description
Technical Field
The invention relates to the technical field of public security criminals, in particular to a multi-biological-characteristic normalizing method.
Background
In the daily work of police officers at present, when identity information of a certain case or other people entering the case is registered, biological characteristics such as fingerprints, irises, DNA and the like are collected. In the registration process, the police can confirm whether the identity number is a prior department or a suspect of a certain unexplored case by using the identity card carried by the other party or the spoken identity number.
At present, basic identity information and various biological characteristic data tables collected by suspect entering are stored, and the storage mode is roughly divided into: the personnel collect basic information tables and other professional information tables. The tables above are not capable of querying historical collected data of a certain person simply through identification numbers or other data item associations.
In the existing system, the inquiry is directly carried out according to the identity card number, but if the personnel report the identity card number in a hurry or the identity card used at this time is inconsistent with the identity card reported by the previous collected times, the inquired record is the collected record of a plurality of people, not just one person; the inquiry can not accurately acquire the previous case-involved information of the person, and all the historical records of the person can not be inquired through one identity card number. Even if the person enters the acquired biological characteristics in the past in the physical evidence comparison of a certain case, the identity card used in the past acquisition is not registered at this time, so that the person cannot be known to be a suspect in the inquiry at this time, and the suspect cannot be caught and solved in time.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a multi-biological-characteristic normalizing method to solve the problems that when a suspect is inquired at present, if the same person uses different identity cards for multiple times, interference cannot be accurately eliminated, the person cannot be known to be a suspect and the suspect cannot be timely caught to solve a case, so that the collected biological characteristic data of the suspect is accurately grouped and established into a data warehouse for normalizing the biological characteristics of the suspect, so that the phenomenon that all income information of the history of the person and whether the collected biological characteristics of each income are compared with the field identity card of a certain case is inquired and shown, and the identity of the person with the unreal historical data identity card is assisted to be confirmed.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
A multi-biometric normalization method comprising the steps of:
s1, inputting the biological characteristic data into the collected database;
s2, extracting newly added biological characteristic data and constructing a physical evidence entity relationship of the personnel; establishing a relationship between data of different biological characteristic acquisition record tables and data acquired in different batches, and establishing an entity relationship table;
s3, constructing node and connection data;
s4, constructing a tree structure by adopting a gallery algorithm;
s5, establishing a suspect collection biological characteristic normalization data warehouse;
and S6, establishing a biological person file page and displaying data.
Further optimizing the technical scheme, the main data items of the entity relationship table are as follows: entity number, entity source table, connection type, and associated time.
Further optimizing the technical solution, the step S2 includes the following steps:
s21, regularly carrying out technical duplicate checking comparison on the newly added biological characteristic data and the data of the historical biological characteristic database in the collected database, and storing the result in the comparison into an entity relation table;
and S22, periodically extracting each newly added biological characteristic data, associating the two biological characteristic acquisition records acquired at the same time, and storing the two biological characteristic acquisition records in an entity relation table.
In step S3, new entity relationship table data is periodically extracted, converted into node data and node connection data, and input into the graph database.
Further optimizing the technical scheme, the node data is as follows: node number, node name, node source table name; the node connection data is as follows: node number, connection time and connection type.
In the data extraction and conversion process, if the nodes with the same node number are encountered, the nodes are merged into one node.
In step S4, a graph library algorithm is used to construct data of a plurality of tree structures from all nodes and links in a graph database, where each tree is data of the same creature.
Further optimizing the technical solution, the step S5 includes the following steps:
s51, establishing a suspect acquisition data warehouse by using the greenplus distributed database, and establishing a personnel information acquisition record grouping table;
s52, generating a group ID for each tree, and storing the group ID and the personnel information inquired according to the node information entity relation table into a personnel information collection record grouping table of the suspect entering the collected database;
and S53, creating a source table structure on the suspect collection data warehouse, and storing the source data into a new source table structure.
In step S6, the identity number used by each biological person and the record of the field case evidence in the acquired data and the biological characteristic ratio are displayed by establishing a biological person archive page according to the database of the suspect acquired data established in step S5.
Due to the adoption of the technical scheme, the technical progress of the invention is as follows.
The method is used for carrying out multi-biological-characteristic normalization on the biological characteristic data collected by the suspect, establishing a data warehouse and displaying the data through a system page. The invention can accurately group the biological characteristic data acquired by the same person for multiple times to establish a suspect biological characteristic acquisition normalization data warehouse, is convenient to inquire and display all entrance information of the history of the person and whether the acquired biological characteristics exist in comparison with the physical evidence of a certain case site every time, and assists in confirming the identity of the person with the unreal historical data identity card.
Compared with the traditional inquiry according to the ID card number, the method can eliminate the interference of ID card bleaching and faking, and groups all biological characteristic data collected from the same suspect history to establish a data warehouse. The method can realize that when a suspect enters the collected biological characteristics, the biological characteristic comparison result is put into a warehouse in a short time, then the records are grouped into the corresponding biological characteristic group, whether the person has an unbroken case in the historical collection record ratio or not is inquired, and the case is supported to be detected in time.
Drawings
FIG. 1 is an architectural flow diagram of the present invention;
fig. 2 is a schematic diagram of the tree structure constructed by using the gallery algorithm in step S4 according to the present invention.
Detailed Description
The invention will be described in further detail below with reference to the figures and specific examples.
A multi-biological-feature normalization method is combined with the methods shown in the figures 1-2, a suspect collection biological-feature normalization data warehouse is established for data collected by suspects according to biological features by establishing data relations, adopting a gallery algorithm (Neo4j gallery algorithm) for operation and utilizing a distributed data storage technology, and the warehouse data is displayed through a biological-feature archive page.
The invention comprises the following steps:
and S1, inputting the biological characteristic data into the collected database.
S2, extracting newly added biological characteristic data and constructing a physical evidence entity relationship of the personnel; establishing data relation between different biological characteristic collection record tables and data collected in different batches, and establishing an entity relation table.
The main data items of the entity relationship table are: entity number, entity source table, connection type, and associated time.
The entity number is the material evidence number. The entity number comprises an entity A number and an entity B number.
The entity source table comprises an entity A source table and an entity B source table. The entity A source table is the source table name of the entity A number, and the source table data can be conveniently backtracked. The entity B source table is the source table name of the entity B number.
Step S2 includes the following steps:
and S21, performing technical duplicate ratio comparison on the newly added biological characteristic data and the data in the historical biological characteristic library in the acquired database regularly. And judging whether the ratio is in the middle, and if so, storing the result in the ratio in an entity relation table.
And S22, periodically extracting each new biological characteristic data by using an ETL tool, associating two biological characteristic acquisition records acquired at the same time with the same personnel number, and storing the two biological characteristic acquisition records in an entity relationship table.
And S3, constructing node and connection data.
In step S3, an ETL tool (i.e., a data extraction and conversion tool) is used to periodically extract new entity relationship table data, convert the new entity relationship table data into node data and node connection data, and input the node data and the node connection data into a graph database.
The node data is: node number, node name, node source table name. The node connection data is as follows: node number, connection time and connection type. The node number and the node name are material evidence numbers. The node number may be a node a number, a node B number, etc.
In the data extraction and conversion process, if the nodes with the same node number are encountered, the nodes are merged into one node.
And S4, constructing a tree structure by adopting a gallery algorithm.
In step S4, a graph library algorithm is used to construct data of a tree structure from all nodes and links in a graph database, where each tree is data of the same creature, as shown in fig. 2. The gallery algorithm specifically adopted is Neo4j gallery algorithm.
The specific steps of step S4 include:
s41, inputting the constructed node and connection data into a graph database of the Neo4j algorithm;
and S42, processing the associated data by using the graph database of the Neo4j algorithm, and connecting the nodes through the relationship to form a relational network structure.
In fig. 2, the node C, the node D, and the node E in the fingerprint ratio of the node a are connected, and the node B and the node G collected at the same time as the node a and the node F in the DNA ratio of the node a are connected to form a tree structure of the biological person.
And S5, establishing a suspect collection biological characteristic normalization data warehouse.
Step S5 includes the following steps:
s51, establishing a suspect collection data warehouse by using the greenplus distributed database, and establishing a personnel information collection record grouping table. The main data items are: group ID, personnel number, name, physical evidence number, physical evidence type, acquisition time, acquisition unit, and the like.
According to the invention, 5 servers are utilized to build and deploy a greenplus distributed database cluster as a database for storing data acquired by suspects.
And S52, traversing each tree generated in the graph database in the step S4 by using a depth-first algorithm, generating a group ID for each tree, and storing the group ID and the personnel information inquired according to the node information entity relation table into a personnel information collection record grouping table of the suspect entering the collected database. The personnel information comprises information such as personnel identification numbers, names, acquisition time, acquisition units and the like.
And S53, creating a source table structure on the suspect collection data warehouse, and storing the source data into a new source table structure.
And S6, establishing a biological person file page and displaying data.
In step S6, the identity number used by each biological person and the record of the field case physical evidence in the acquired data and biological characteristic ratio each time are displayed by establishing a biological person archive page according to the suspect acquisition data warehouse established in step S5.
The invention can accurately group the biological characteristic data acquired by the same person for multiple times to establish a suspect biological characteristic acquisition normalization data warehouse, is convenient to inquire and display all entrance information of the history of the person and whether the acquired biological characteristics exist in comparison with the physical evidence of a certain case site every time, and assists in confirming the identity of the person with the unreal historical data identity card.
Compared with the traditional inquiry according to the ID card number, the method can eliminate the interference of ID card bleaching and faking, and groups all biological characteristic data collected from the same suspect history to establish a data warehouse. The method can realize that when a suspect enters the collected biological characteristics, the biological characteristic comparison result is put into a warehouse in a short time, then the records are grouped into the corresponding biological characteristic group, whether the person has an unbroken case in the historical collection record ratio or not is inquired, and the case is supported to be detected in time.
Claims (9)
1. A multi-biometric normalization method, comprising the steps of:
s1, inputting the biological characteristic data into the collected database;
s2, extracting newly added biological characteristic data and constructing a physical evidence entity relationship of the personnel; establishing a relationship between data of different biological characteristic acquisition record tables and data acquired in different batches, and establishing an entity relationship table;
s3, constructing node and connection data;
s4, constructing a tree structure by adopting a gallery algorithm;
s5, establishing a suspect collection biological characteristic normalization data warehouse;
and S6, establishing a biological person file page and displaying data.
2. The multi-biometric normalization method of claim 1, wherein the main data items of the entity relationship table are: entity number, entity source table, connection type, and associated time.
3. The multi-biometric normalization method according to claim 1, wherein the step S2 includes the steps of:
s21, regularly carrying out technical duplicate checking comparison on the newly added biological characteristic data and the data of the historical biological characteristic database in the collected database, and storing the result in the comparison into an entity relation table;
and S22, periodically extracting each newly added biological characteristic data, associating the two biological characteristic acquisition records acquired at the same time, and storing the two biological characteristic acquisition records in an entity relation table.
4. The multi-biometric normalization method of claim 1, wherein in step S3, additional entity relationship table data is periodically extracted, converted into node data and node connection data, and input into the graph database.
5. The multi-biometric normalization method of claim 4, wherein the node data is: node number, node name, node source table name; the node connection data is as follows: node number, connection time and connection type.
6. The multi-biometric normalization method of claim 5, wherein nodes with the same node number are merged into one node during the data extraction transformation.
7. The multi-biometric normalization method according to claim 1, wherein in step S4, all the nodes and links are constructed into data of a plurality of tree structures in the graph database by using a graph library algorithm, and each tree is the same biometric data.
8. The multi-biometric normalization method according to claim 7, wherein the step S5 includes the steps of:
s51, establishing a suspect acquisition data warehouse by using the greenplus distributed database, and establishing a personnel information acquisition record grouping table;
s52, generating a group ID for each tree, and storing the group ID and the personnel information inquired according to the node information entity relation table into a personnel information collection record grouping table of the suspect entering the collected database;
and S53, creating a source table structure on the suspect collection data warehouse, and storing the source data into a new source table structure.
9. The multi-biometric normalization method of claim 8, wherein in step S6, the identity card number used by each biometric person and the record of the on-site case physical evidence in the collected data and biometric ratio are displayed by creating a biometric file page according to the database of suspect collection data created in step S5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010973725.4A CN112256667B (en) | 2020-09-16 | 2020-09-16 | Multi-biological characteristic normalization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010973725.4A CN112256667B (en) | 2020-09-16 | 2020-09-16 | Multi-biological characteristic normalization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112256667A true CN112256667A (en) | 2021-01-22 |
| CN112256667B CN112256667B (en) | 2024-03-22 |
Family
ID=74232595
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010973725.4A Active CN112256667B (en) | 2020-09-16 | 2020-09-16 | Multi-biological characteristic normalization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112256667B (en) |
Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714139A (en) * | 2013-12-20 | 2014-04-09 | 华南理工大学 | Parallel data mining method for identifying a mass of mobile client bases |
| CN104765768A (en) * | 2015-03-09 | 2015-07-08 | 深圳云天励飞技术有限公司 | Mass face database rapid and accurate retrieval method |
| CN105069130A (en) * | 2015-08-14 | 2015-11-18 | 安徽新华博信息技术股份有限公司 | Suspect object prediction method |
| CN106095862A (en) * | 2016-06-02 | 2016-11-09 | 四川大学 | The storage method of centralized expansible pattern of fusion multi-dimensional complicated structural relation data |
| CN106339428A (en) * | 2016-08-16 | 2017-01-18 | 东方网力科技股份有限公司 | Identity identification method and device for suspects based on large video data |
| CN106649464A (en) * | 2016-09-26 | 2017-05-10 | 深圳市数字城市工程研究中心 | Method of building Chinese address tree and device |
| CN106682990A (en) * | 2016-12-09 | 2017-05-17 | 武汉中软通证信息技术有限公司 | Method and system for establishing interpersonal relationship model of suspect |
| CN107346435A (en) * | 2017-06-15 | 2017-11-14 | 浙江捷尚视觉科技股份有限公司 | A kind of suspicion fake-licensed car catching method based on vehicle characteristics storehouse |
| CN107895026A (en) * | 2017-11-17 | 2018-04-10 | 联奕科技有限公司 | A kind of implementation method of campus user portrait |
| CN109284312A (en) * | 2018-08-27 | 2019-01-29 | 山东威尔数据股份有限公司 | A kind of heterogeneous database change real-time informing method |
| CN109522342A (en) * | 2018-11-30 | 2019-03-26 | 北京百度网讯科技有限公司 | Police affairs management method, device, equipment and storage medium |
| CN109635003A (en) * | 2018-12-07 | 2019-04-16 | 南京华苏科技有限公司 | A method of the Community Population information association based on multi-data source |
| CN109993966A (en) * | 2018-01-02 | 2019-07-09 | 中国移动通信有限公司研究院 | A kind of method and device of building user portrait |
| CN110059177A (en) * | 2019-04-24 | 2019-07-26 | 南京传唱软件科技有限公司 | A kind of activity recommendation method and device based on user's portrait |
| CN110175217A (en) * | 2019-05-16 | 2019-08-27 | 武汉数矿科技股份有限公司 | It is a kind of for determining the perception data analysis method and device of suspect |
| WO2019184775A1 (en) * | 2018-03-30 | 2019-10-03 | 华为技术有限公司 | Management data storage method and device, and storage medium |
| CN110619002A (en) * | 2019-09-12 | 2019-12-27 | 北京百度网讯科技有限公司 | Data processing method, device and storage medium |
| CN110874369A (en) * | 2019-10-25 | 2020-03-10 | 广州纳斯威尔信息技术有限公司 | Multidimensional data fusion investigation system and method thereof |
| CN111241305A (en) * | 2020-01-16 | 2020-06-05 | 北京明略软件系统有限公司 | Data processing method and device, electronic equipment and computer readable storage medium |
-
2020
- 2020-09-16 CN CN202010973725.4A patent/CN112256667B/en active Active
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103714139A (en) * | 2013-12-20 | 2014-04-09 | 华南理工大学 | Parallel data mining method for identifying a mass of mobile client bases |
| CN104765768A (en) * | 2015-03-09 | 2015-07-08 | 深圳云天励飞技术有限公司 | Mass face database rapid and accurate retrieval method |
| CN105069130A (en) * | 2015-08-14 | 2015-11-18 | 安徽新华博信息技术股份有限公司 | Suspect object prediction method |
| CN106095862A (en) * | 2016-06-02 | 2016-11-09 | 四川大学 | The storage method of centralized expansible pattern of fusion multi-dimensional complicated structural relation data |
| CN106339428A (en) * | 2016-08-16 | 2017-01-18 | 东方网力科技股份有限公司 | Identity identification method and device for suspects based on large video data |
| CN106649464A (en) * | 2016-09-26 | 2017-05-10 | 深圳市数字城市工程研究中心 | Method of building Chinese address tree and device |
| CN106682990A (en) * | 2016-12-09 | 2017-05-17 | 武汉中软通证信息技术有限公司 | Method and system for establishing interpersonal relationship model of suspect |
| CN107346435A (en) * | 2017-06-15 | 2017-11-14 | 浙江捷尚视觉科技股份有限公司 | A kind of suspicion fake-licensed car catching method based on vehicle characteristics storehouse |
| CN107895026A (en) * | 2017-11-17 | 2018-04-10 | 联奕科技有限公司 | A kind of implementation method of campus user portrait |
| CN109993966A (en) * | 2018-01-02 | 2019-07-09 | 中国移动通信有限公司研究院 | A kind of method and device of building user portrait |
| WO2019184775A1 (en) * | 2018-03-30 | 2019-10-03 | 华为技术有限公司 | Management data storage method and device, and storage medium |
| CN109284312A (en) * | 2018-08-27 | 2019-01-29 | 山东威尔数据股份有限公司 | A kind of heterogeneous database change real-time informing method |
| CN109522342A (en) * | 2018-11-30 | 2019-03-26 | 北京百度网讯科技有限公司 | Police affairs management method, device, equipment and storage medium |
| CN109635003A (en) * | 2018-12-07 | 2019-04-16 | 南京华苏科技有限公司 | A method of the Community Population information association based on multi-data source |
| CN110059177A (en) * | 2019-04-24 | 2019-07-26 | 南京传唱软件科技有限公司 | A kind of activity recommendation method and device based on user's portrait |
| CN110175217A (en) * | 2019-05-16 | 2019-08-27 | 武汉数矿科技股份有限公司 | It is a kind of for determining the perception data analysis method and device of suspect |
| CN110619002A (en) * | 2019-09-12 | 2019-12-27 | 北京百度网讯科技有限公司 | Data processing method, device and storage medium |
| CN110874369A (en) * | 2019-10-25 | 2020-03-10 | 广州纳斯威尔信息技术有限公司 | Multidimensional data fusion investigation system and method thereof |
| CN111241305A (en) * | 2020-01-16 | 2020-06-05 | 北京明略软件系统有限公司 | Data processing method and device, electronic equipment and computer readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 聂伟: "从关系数据到树形数据", 《程序员》, no. 08, pages 93 - 96 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112256667B (en) | 2024-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109767322B (en) | Suspicious transaction analysis method and device based on big data and computer equipment | |
| CN106897285B (en) | Data element extraction and analysis system and data element extraction and analysis method | |
| Farooqui et al. | Design of a data warehouse for medical information system using data mining techniques | |
| CN106021545A (en) | Method for remote diagnoses of cars and retrieval of spare parts | |
| CN112836809A (en) | Device characteristic extraction method and fault prediction method of convolutional neural network based on differential feature fusion | |
| CN111598753A (en) | Suspect recommendation method and device, electronic equipment and storage medium | |
| CN116842142B (en) | Intelligent retrieval system for medical instrument | |
| Soni et al. | Reducing risk in KYC (know your customer) for large Indian banks using big data analytics | |
| CN112256667B (en) | Multi-biological characteristic normalization method | |
| CN109242431A (en) | A kind of enterprise management method and its system based on data system | |
| KR101671890B1 (en) | apparatus for analyzing business traction information and method fornalyzing business traction information | |
| Shi et al. | A general neural framework for classification rule mining | |
| CN113779110B (en) | Family relation network extraction method, device, computer equipment and storage medium | |
| CN110010231A (en) | A kind of data processing system and computer readable storage medium | |
| Sengupta | Applications of data mining in library & information centres: an overview | |
| Liu et al. | Research on application of data mining in hospital management | |
| US20150324813A1 (en) | System and method for determining by an external entity the human hierarchial structure of an rganization, using public social networks | |
| CN112100202A (en) | Product identification and product information completion method, storage medium and robot | |
| TWI767192B (en) | Application method of intelligent analysis system | |
| Penning et al. | Information quality assessment and improvement of student information in the university environment | |
| Amer et al. | A Proposed Model to Measure of the Performance of ATM Machine | |
| Raju | Getting Useful Information From Cricket Data Set Using Data Analytics Techniques Odi Cricket Team Performance Analysis Using Data Mining Classification Techniques | |
| Jiang et al. | Medical insurance medication anomaly detection based on isolated forest proximity matrix | |
| Panwar | Role of Data Warehousing & Data Mining in E-Goverance | |
| CN117151879A (en) | Funds transaction accompanying recognition method based on big data |
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 |