CN104317789A - Method for building passenger social network - Google Patents
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- CN104317789A CN104317789A CN201410167418.1A CN201410167418A CN104317789A CN 104317789 A CN104317789 A CN 104317789A CN 201410167418 A CN201410167418 A CN 201410167418A CN 104317789 A CN104317789 A CN 104317789A
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- 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
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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Abstract
The invention relates to a method for building a passenger social network. The method comprises the following steps of: A, building a unified passenger information file; B, building a passenger event information record; C, loading event data to an hdfs event record buffer region through Hive, and building a table structure; D, cleaning the data; E, converting the file information of a passenger into node data; F, calculating directed edges between two different passenger nodes; G, selecting a relationship intensity attribute calculation algorithm; H, calculating the relationship type and the probability; and I, providing the visual display of the passenger social relationship network. The method provided by the invention has the advantages that the expansion can be realized through adopting an event addition and weight index calculation mode, so that the adaptation to the aviation service change can be realized; and the speed acceleration exceeds hundred times compared with that of a non-distributed relationship type database application mode.
Description
Technical field
The present invention relates to social network relationships constructing technology, particularly relate to a kind of method building passenger's social networks.
Background technology
The Aviation Industry of China, through the high speed development of 20 years, airline have accumulated a large amount of passenger data resources, the ecommerce of aeronautical product also from traditional pure sale of ticket, air ticket till now, hotel, hire a car, management mode that Additional Services and travelling products diversification combine.Day by day perfect along with air market, airline and online tourism provider start to introduce new marketing methods, and building and analyze passenger's social networks becomes the social relation network marketing method become more and more important.
Functionally, the building mode of the social networks network based on pure network social intercourse media more prevailing at present, and be not suitable for Aviation Industry.
Simultaneously technically, the data of the accumulative TB rank of airline, utilize traditional relevant database build such one more than 200,000,000 nodes, the social networks network collection of illustrative plates on 3,000,000,000 limits, unacceptable in performance.
Summary of the invention
The object of the present invention is to provide a kind of method building passenger's social networks, utilize the passenger data of the existing magnanimity of airline, build passenger's social networks network.
Technical scheme of the present invention is as follows.
Build the method for passenger's social networks, comprise the following steps:
Steps A, set up unified Customer information archives
A.1, according to the passenger of airline seize the opportunity record, with the perfect instrument of passenger number for uniquely to identify sign, the Customer information archives that system of setting up is unique, are stored in passenger's archives table of HBase.Each passenger's archives will be a node of the final passenger's social networks formed;
A.2, the Customer information of other system of association airline, according to perfect instrument or other identifiable design fields, identify the passenger of other system, and unifiedly form single Client view, the passenger in single Client view has a unique ID to identify client.
Wherein, the detailed implementation step of steps A:
(1) in HBase, set up two groups of tables, store master data information and the identity identification information of client respectively.In client's master data information, save unique ID of client and main ascribed characteristics of population record; The identity identification information of client comprises certificate/account type, perfect instrument number/account (containing mobile phone, QQ, Email, I.D., member's card number, microblogging, login ID, login Cookie etc.);
(2) api interface is provided, imports any identifying information of client into, by effective information coupling and Cookie coupling, find corresponding client, return the master data information of client.
(3) backstage provides the inquiry of client, management and duplicate removal function.
When subsequent step information enters, need from this step the unique ID obtaining client.
Step B, set up passenger's event information record
B.1, on HBase, the data structure of passenger's event information is set up;
B.2 the data dictionary of passenger's event, is set up;
B.3, by Hive set up event information list structure, and the data structure of HBase sets up mapping relations.
Step C, set up list structure by Hive load events data to hdfs episode record buffer district, event data comprises:
1., passenger PNR record in same flight, with PNR passenger produce colleague's relation;
2., the mileage integration of member passenger is assigned data;
3., the Companies Registry record of passenger;
4., other associated passenger events.
Step C specifically comprises:,
Load events data are in hdfs episode record buffer district
C.1, from the passenger PNR record that airline preserves, according to same flight, the colleague's relation produced with PNR passenger, extracts and is loaded in passenger's event information log buffer.PNR event of going together is the significant data source of civil aviaton's social networks;
C.2, from passenger's member system of airline, extract the mileage integration of member and to assign data, be loaded into passenger's episode record buffer district;
C.3, from passenger's crm system of airline, obtain the Companies Registry record of passenger, information is loaded in passenger's event information record, as the special event of " be colleague at some time points ";
C.4, extract airline's other relevant passenger's events inner, be loaded into episode record buffer district, system does not refuse there is the data message of power-assisted to building social networks network;
C.5, above-mentioned information uses Hive to set up list structure in hdfs.
ETL instrument described in step C is one or more in Storm, Kafka, Flume, Kettle, Sqoop.
Step D, cleaning data, and by the logout in buffer zone, in conjunction with the information in basic dictionary, be loaded as in the HBase Event Log Table that complete event data sets up to step B.This step uses Hive, PIG, SQL to carry out the cleaning of data, conversion and loading work.
Step e, the archive information of passenger is converted into node data, be stored in the distributed data base based on figure, each passenger is a node, and the attribute that node stores comprises: the network social intercourse media information (optional) of the membership information of the trip information of the identity information of passenger, the demographic attributes of passenger, passenger, the consumption information of passenger, passenger, the company information of passenger, passenger.
Step F, pass through distributed arithmetic, calculate two internodal directed edges of different passenger: if there is event between passenger A and passenger B relation can be produced, and also there is no existing limit, then newly-built two different directed edge A->B and B->A in Titan, the attribute on limit stores this relation; If existing limit, be then stored into this event relation on the attribute on limit.
Step G, choice relation Intensity attribute computational algorithm: in the system starting stage, the relationship strength algorithm on configuration limit, when system cloud gray model, system adopts the relationship strength attribute configured to perform calculating; Calculate by Job execution, its parameter is the number of times etc. occurred the type of event, the algorithm weights of event and time.
Step H, calculated relationship type and possibility: relationship type comprises: Peer Relationships, family relationship, friend relation etc., possibility is produced according to specific rule-based algorithm by the parameter of the type of event, time that event occurs, event, relationship strength and weight.
Step I, provide the visual presentation of passenger's social networks network.
Wherein, preferential enforcement ground, step event information b.1 comprises following information: the time that event occurs, the type of event, the channel of event, the contact point of event, the main body of event, type of subject, the action of event, the object of event, object type, the relationship type of event, the weight of relation, the parameter of event.
Wherein, preferentially implement ground, step event data dictionary b.2 comprises: the type of event, channel information, contact point, event Subjective and Objective type, the type of action of event, relationship type, weight type, parameter type.
Wherein, preferential enforcement ground, social networks network data source, internet is supplemented: add to as data source in system using social networks networks such as Sina's microbloggings, the social networks network friend relation of internet, as the side information of the final relational network node set up in step H.
The present invention adopts the hdfs distributed storage of Hadoop platform as mass data storage, adopt distributed column storage database HBase as the storage medium of Customer information and passenger's contact point event, adopt Hive to set up distributed data list structure, adopt distributed chart database Titan to make the present invention have following beneficial effect as the storage medium of relation node, limit and attribute:
1, utilize angle from industry data, the social networks network built by the inventive method, effectively make use of the existing mass data of airline;
2, the inventive method, and the practical business demand of airline is pressed close to, and can be expanded by the mode of increase event and calculating weight index, to adapt to the business change of aviation;
3, use distributed architectural framework, compare the mode of non-distributed relation database application, on the handling property more than 100,000,000 airline passengers, more than 1,000,000,000 event informations, speed is lifted beyond hundred times.
Below, the present invention is further elaborated in conjunction with the embodiments.
Accompanying drawing explanation
Fig. 1 is embodiment of the present invention flow chart of data processing figure;
Fig. 2 is the storage mode schematic diagram of passenger's relational network result that the embodiment of the present invention draws;
Embodiment
As shown in Figure 1, passenger seizes the opportunity record, PNR information and the data from other system and belongs to structural data, ETL instrument is used to enter into the Hdfs memory buffer of Hadoop platform, through job scheduling tool drives, after cleaning, conversion, loading, enter in distributed Nosql database HBase, form logout information; Equally, from the Customer information of multiple data after identification, HBase is entered into.These data, through E, F, G, H, I step, are stored in the distributed data base based on figure.Front end Web applies directly from distributed chart database Query Result.
As shown in Figure 2, result stores in the mode of digraph, comprising:
Node---each node stores a passenger, and nodal information comprises " attribute information " comprises the identity information, demographic attributes, trip information, consumption information, membership information, company information, network social intercourse media information etc. of passenger;
The intensity of relation---relation of inclusion, event, dependency degree, direction etc.;
Attribute---by node and relation comprise.
Steps A, set up unified passenger information archives
A.1, according to the passenger of airline seize the opportunity record, with the perfect instrument of passenger number for uniquely to identify sign, the passenger information archives that system of setting up is unique, are stored in passenger's archives table of HBase.Each passenger's archives can be a node of the final passenger's social networks formed;
A.2, the passenger information of other system of association airline, according to perfect instrument or other identifiable design fields, identify the passenger of other system, and unifiedly form single Client view, the passenger in single Client view has a unique ID to identify client.
The detailed implementation step of steps A:
(4) in HBase, set up two groups of tables, store master data information and the identity identification information of client respectively.In client's master data information, save unique ID of client and main ascribed characteristics of population record; The identity identification information of client comprises certificate/account type, perfect instrument number/account (containing mobile phone, QQ, Email, I.D., member's card number, microblogging, login ID, login Cookie etc.);
(5) api interface is provided, imports any identifying information of client into, by effective information coupling and Cookie coupling, find corresponding client, return the master data information of client;
(6) backstage provides the inquiry of client, management and duplicate removal function.
When subsequent step information enters, need from this step the unique ID obtaining client.
For step B, the process of this enforcement is as follows:
1, image data server, finds other operation system generation data variation (or selecting regular delta to extract) of airline, by data flow to large data platform rear end;
2, after large data platform receives data stream, be written to the buffer zone of hdfs, now do not change the structure of data itself;
3, Sub01-Sub04 is four different MapReduce Distributed Calculation operations, by job scheduling instrument scheduled for executing;
4,360 passenger identities in the Customer information in event and system are carried out identification unified, the information such as the classification of event, channel, parameter are carried out standardization according to metadata, is formed than more complete logout by Sub01 and Sub02 cleaning and process raw data;
5, Sub03 is by the logout after process, is loaded in HBase distributed data base, for follow-up real-time query;
6, event is added up by Sub04, adds the dimension required for analysis and parameter, forms the statistical information of event, directly transfers statistics for subsequent builds algorithm.
Step C, load events data are in hdfs episode record buffer district
C.1, from the passenger PNR record that airline preserves, according to same flight, the colleague's relation produced with PNR passenger, extracts and is loaded in passenger's event information log buffer.PNR event of going together is the significant data source of civil aviaton's social networks;
C.2, from passenger's member system of airline, extract the mileage integration of member and to assign data, be loaded into passenger's episode record buffer district;
C.3, from passenger's crm system of airline, obtain the Companies Registry record of passenger, information is loaded in passenger's event information record, as the special event of " be colleague at some time points ";
C.4, extract airline's other relevant passenger's events inner, be loaded into episode record buffer district, system does not refuse there is the data message of power-assisted to building social networks network;
C.5, above-mentioned information uses Hive to set up list structure in hdfs.
ETL instrument described in step C is one or more in Storm, Kafka, Flume, Kettle, Sqoop.
Step D, cleaning data, and by the logout in buffer zone, in conjunction with the information in basic dictionary, be loaded as in the HBase Event Log Table that complete event data sets up to step B.This step uses Hive, PIG, SQL to carry out the cleaning of data, conversion and loading work.
For step e, F, the process of the present embodiment is as follows:
(for PNR network, explanation builds the method for network chart to this part, and actual PNR is only the class in relation event)
1, inquire increment passenger, get the node of unique ID as figure of this passenger in 360 degree of Client view;
2, demonstrate,prove incremental nodes whether to exist in Titan chart database, if there is no, be written in chart database;
3, the attribute information of passenger is write on the nodes;
4, increment PNR event is inquired, association passenger ID; Total data PNR1:A; PNR2:B...;
5, filter out same PNR detailed containing the order record of two or more passenger, associate out unique ID (PNR1:A of passenger; PNR1:B; PNR2:C; PNR2:D; PNR2:E);
6, PNR group (PNR1:A, B is produced; PNR2:C, D, E);
7, colleague's associated record (PNR1 A-B of passenger is produced; PNR2 C-D; PNR2 D-E; PNR2 C-E);
8, the directed edge (A->B of figure is produced, C->D, D->E, C->E) (A<-B, C <-D, D <-E, C <-E);
9, judge whether directed edge exists in the drawings, if do not existed, write directed edge;
If 10 directed edges exist, the overall situation is needed to recalculate the statistical attribute of directed edge.
Step G, choice relation Intensity attribute computational algorithm: in the system starting stage, the relationship strength algorithm on configuration limit, when system cloud gray model, system adopts the relationship strength property calculation algorithm configured to perform.
Step H, the algorithm selected in step G, by Job execution, its parameter is the number of times etc. occurred the type of event, the algorithm weights of event and time.
Step I, calculated relationship type and possibility: relationship type comprises: Peer Relationships, family relationship, friend relation etc., possibility is produced according to specific rule-based algorithm by the parameter of the type of event, time that event occurs, event, relationship strength and weight.
Step J, supplementary social networks network data source, internet (optional): system can allow user to add in system social networks networks such as Sina's microbloggings as data source, the social networks network friend relation of internet, as the side information of the final relational network node set up.
Step K, provide the visual presentation of passenger's social networks network.
Claims (4)
1. build the method for passenger's social networks, it is characterized in that, comprise the following steps:
Steps A, set up unified Customer information archives
A.1, according to the passenger of airline seize the opportunity record, with the perfect instrument of passenger number for uniquely to identify sign, the Customer information archives that system of setting up is unique, are stored in passenger's archives table of HBase; Each passenger's archives will be a node of the final passenger's social networks formed;
A.2, the Customer information of other system of association airline, according to perfect instrument or other identifiable design fields, identify the passenger of other system, and unifiedly form single Client view, the passenger in single Client view has a unique ID to identify client;
Step B, set up passenger's event information record
B.1, on HBase, the data structure of passenger's event information is set up;
B.2 the data dictionary of passenger's event, is set up;
B.3, by Hive set up event information list structure, and the data structure of HBase sets up mapping relations;
Step C, set up list structure by Hive load events data to hdfs episode record buffer district, event data comprises:
1., passenger PNR record in same flight, with PNR passenger produce colleague's relation;
2., the mileage integration of member passenger is assigned data;
3., the Companies Registry record of passenger;
4., other associated passenger events;
Step D, cleaning data, and by the logout in buffer zone, in conjunction with the information in basic dictionary, be loaded as in the HBase Event Log Table that complete event data sets up to step B;
Step e, the archive information of passenger is converted into node data, be stored in the distributed data base based on figure, each passenger is a node, and the attribute that node stores comprises: the network social intercourse media information (optional) of the membership information of the trip information of the identity information of passenger, the demographic attributes of passenger, passenger, the consumption information of passenger, passenger, the company information of passenger, passenger;
Step F, pass through distributed arithmetic, calculate two internodal directed edges of different passenger: if there is event between passenger A and passenger B relation can be produced, and also there is no existing limit, then newly-built two different directed edge A->B and B->A in Titan, the attribute on limit stores this relation; If existing limit, be then stored on the attribute on limit by this event relation;
Step G, choice relation Intensity attribute computational algorithm: in the system starting stage, the relationship strength algorithm on configuration limit, when system cloud gray model, system adopts the relationship strength attribute configured to perform calculating; Calculate by Job execution, its parameter is the number of times etc. occurred the type of event, the algorithm weights of event and time;
Step H, calculated relationship type and possibility: relationship type comprises: Peer Relationships, family relationship, friend relation etc., possibility is produced according to specific rule-based algorithm by the parameter of the type of event, time that event occurs, event, relationship strength and weight;
Step I, provide the visual presentation of passenger's social networks network.
2. the method building passenger's social networks as claimed in claim 1, it is characterized in that, step event information b.1 comprises following information: the time that event occurs, the type of event, the channel of event, the contact point of event, the main body of event, type of subject, the action of event, the object of event, object type, the relationship type of event, the weight of relation, the parameter of event.
3. the method building passenger's social networks as claimed in claim 1, it is characterized in that, step event data dictionary b.2 comprises: the type of event, channel information, contact point, event Subjective and Objective type, the type of action of event, relationship type, weight type, parameter type.
4. the method building passenger's social networks as claimed in claim 1, it is characterized in that, social networks network data source, internet is supplemented: add in system social networks networks such as Sina's microbloggings as data source in step H, the social networks network friend relation of internet, as the side information of the final relational network node set up.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105512773A (en) * | 2015-12-25 | 2016-04-20 | 中国民航信息网络股份有限公司 | Passenger travel destination prediction method and device |
| CN105577832A (en) * | 2016-02-05 | 2016-05-11 | 腾讯科技（深圳）有限公司 | Method and device for establishing network communication |
| CN105740335A (en) * | 2016-01-22 | 2016-07-06 | 山东合天智汇信息技术有限公司 | Titan-based enterprise information analysis platform and construction method thereof |
| CN107291712A (en) * | 2016-03-30 | 2017-10-24 | 阿里巴巴集团控股有限公司 | The generation method and device of data |
| CN107292424A (en) * | 2017-06-01 | 2017-10-24 | 四川新网银行股份有限公司 | A kind of anti-fraud and credit risk forecast method based on complicated social networks |
| CN107704620A (en) * | 2017-10-27 | 2018-02-16 | 北京锐安科技有限公司 | A kind of method, apparatus of file administration, equipment and storage medium |
| CN108021670A (en) * | 2017-12-06 | 2018-05-11 | 中国南方航空股份有限公司 | Multi-source heterogeneous data fusion system and method |
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| CN110008272A (en) * | 2019-04-10 | 2019-07-12 | 张绿儿 | The NoSQL database evaluating system and its construction method of facing sensing device data |
| CN110247947A (en) * | 2018-03-07 | 2019-09-17 | 本田技研工业株式会社 | Decision maker, decision-making system and determination method |
| CN110321935A (en) * | 2019-06-13 | 2019-10-11 | 上海上湖信息技术有限公司 | Business event relationship determines method and device, computer readable storage medium |
| CN111274495A (en) * | 2020-01-20 | 2020-06-12 | 平安科技（深圳）有限公司 | Data processing method and device for user relationship strength, computer equipment and storage medium |
| CN113806450A (en) * | 2021-05-21 | 2021-12-17 | 西南交通大学 | Fusion type passenger relationship network construction method based on comprehensive traffic big data |
| WO2024041368A1 (en) * | 2022-08-23 | 2024-02-29 | 中国民航信息网络股份有限公司 | Method and apparatus for processing passenger names in civil aviation open passenger reservation system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101084496A (en) * | 2004-05-04 | 2007-12-05 | 波士顿咨询集团公司 | Method and apparatus for selecting, analyzing, and visualizing related database records as a network |
| US20080148366A1 (en) * | 2006-12-16 | 2008-06-19 | Mark Frederick Wahl | System and method for authentication in a social network service |
| CN101501672A (en) * | 2006-05-05 | 2009-08-05 | 索尼在线娱乐有限公司 | Determining influential/popular participants in a communication network |
| CN101911066A (en) * | 2008-01-04 | 2010-12-08 | 雅虎公司 | Identifying and employing social network relationships |
| CN103024017A (en) * | 2012-12-04 | 2013-04-03 | 武汉大学 | Method for distinguishing important goals and community groups of social network |
| CN103475717A (en) * | 2013-09-11 | 2013-12-25 | 杭州东信北邮信息技术有限公司 | Method and system for recommending friends based on social network |
-
2014
- 2014-04-24 CN CN201410167418.1A patent/CN104317789B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101084496A (en) * | 2004-05-04 | 2007-12-05 | 波士顿咨询集团公司 | Method and apparatus for selecting, analyzing, and visualizing related database records as a network |
| CN101501672A (en) * | 2006-05-05 | 2009-08-05 | 索尼在线娱乐有限公司 | Determining influential/popular participants in a communication network |
| US20080148366A1 (en) * | 2006-12-16 | 2008-06-19 | Mark Frederick Wahl | System and method for authentication in a social network service |
| CN101911066A (en) * | 2008-01-04 | 2010-12-08 | 雅虎公司 | Identifying and employing social network relationships |
| CN103024017A (en) * | 2012-12-04 | 2013-04-03 | 武汉大学 | Method for distinguishing important goals and community groups of social network |
| CN103475717A (en) * | 2013-09-11 | 2013-12-25 | 杭州东信北邮信息技术有限公司 | Method and system for recommending friends based on social network |
Cited By (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105512773A (en) * | 2015-12-25 | 2016-04-20 | 中国民航信息网络股份有限公司 | Passenger travel destination prediction method and device |
| CN105740335A (en) * | 2016-01-22 | 2016-07-06 | 山东合天智汇信息技术有限公司 | Titan-based enterprise information analysis platform and construction method thereof |
| CN105577832A (en) * | 2016-02-05 | 2016-05-11 | 腾讯科技（深圳）有限公司 | Method and device for establishing network communication |
| CN105577832B (en) * | 2016-02-05 | 2019-05-03 | 腾讯科技（深圳）有限公司 | Establish the method and device of network communication |
| CN107291712A (en) * | 2016-03-30 | 2017-10-24 | 阿里巴巴集团控股有限公司 | The generation method and device of data |
| CN107291712B (en) * | 2016-03-30 | 2020-05-08 | 阿里巴巴集团控股有限公司 | Data generation method and device |
| CN107292424B (en) * | 2017-06-01 | 2020-01-21 | 四川新网银行股份有限公司 | Anti-fraud and credit risk prediction method based on complex social network |
| CN107292424A (en) * | 2017-06-01 | 2017-10-24 | 四川新网银行股份有限公司 | A kind of anti-fraud and credit risk forecast method based on complicated social networks |
| CN107704620A (en) * | 2017-10-27 | 2018-02-16 | 北京锐安科技有限公司 | A kind of method, apparatus of file administration, equipment and storage medium |
| CN107704620B (en) * | 2017-10-27 | 2020-12-01 | 北京锐安科技有限公司 | Archive management method, device, equipment and storage medium |
| CN108021670A (en) * | 2017-12-06 | 2018-05-11 | 中国南方航空股份有限公司 | Multi-source heterogeneous data fusion system and method |
| US11192511B2 (en) | 2018-03-07 | 2021-12-07 | Honda Motor Co., Ltd. | Determination device, determination system, and determination method |
| CN110247947A (en) * | 2018-03-07 | 2019-09-17 | 本田技研工业株式会社 | Decision maker, decision-making system and determination method |
| CN110247947B (en) * | 2018-03-07 | 2022-07-12 | 本田技研工业株式会社 | Determination device, determination system, and determination method |
| CN108876244A (en) * | 2018-06-22 | 2018-11-23 | 珠海格力电器股份有限公司 | Storage query system and method for bill of material (BOM) |
| CN109558399A (en) * | 2018-11-22 | 2019-04-02 | 北京邮电大学 | A kind of date storage method and device |
| CN110008272B (en) * | 2019-04-10 | 2020-01-31 | 张绿儿 | NoSQL database evaluation system for sensor data and construction method thereof |
| CN110008272A (en) * | 2019-04-10 | 2019-07-12 | 张绿儿 | The NoSQL database evaluating system and its construction method of facing sensing device data |
| CN110321935A (en) * | 2019-06-13 | 2019-10-11 | 上海上湖信息技术有限公司 | Business event relationship determines method and device, computer readable storage medium |
| CN110321935B (en) * | 2019-06-13 | 2022-03-15 | 上海上湖信息技术有限公司 | Method and device for determining business event relation and computer readable storage medium |
| CN111274495A (en) * | 2020-01-20 | 2020-06-12 | 平安科技（深圳）有限公司 | Data processing method and device for user relationship strength, computer equipment and storage medium |
| CN111274495B (en) * | 2020-01-20 | 2023-08-25 | 平安科技（深圳）有限公司 | Data processing method, device, computer equipment and storage medium for user relationship strength |
| CN113806450A (en) * | 2021-05-21 | 2021-12-17 | 西南交通大学 | Fusion type passenger relationship network construction method based on comprehensive traffic big data |
| WO2024041368A1 (en) * | 2022-08-23 | 2024-02-29 | 中国民航信息网络股份有限公司 | Method and apparatus for processing passenger names in civil aviation open passenger reservation system |
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