CN112488708B - Block chain account relevance query method and false transaction screening method - Google Patents
Block chain account relevance query method and false transaction screening method Download PDFInfo
- Publication number
- CN112488708B CN112488708B CN202011376615.6A CN202011376615A CN112488708B CN 112488708 B CN112488708 B CN 112488708B CN 202011376615 A CN202011376615 A CN 202011376615A CN 112488708 B CN112488708 B CN 112488708B
- Authority
- CN
- China
- Prior art keywords
- transaction
- account
- transfer
- party
- nodes
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000012216 screening Methods 0.000 title claims abstract description 9
- 238000012546 transfer Methods 0.000 claims abstract description 4
- 238000004590 computer program Methods 0.000 claims description 6
- ANBQYFIVLNNZCU-CQCLMDPOSA-N alpha-L-Fucp-(1->2)-[alpha-D-GalpNAc-(1->3)]-beta-D-Galp-(1->3)-[alpha-L-Fucp-(1->4)]-beta-D-GlcpNAc-(1->3)-beta-D-Galp Chemical compound O[C@H]1[C@H](O)[C@H](O)[C@H](C)O[C@H]1O[C@H]1[C@H](O[C@H]2[C@@H]([C@@H](O[C@@H]3[C@@H]([C@@H](O)[C@@H](O)[C@@H](CO)O3)NC(C)=O)[C@@H](O)[C@@H](CO)O2)O[C@H]2[C@H]([C@H](O)[C@H](O)[C@H](C)O2)O)[C@@H](NC(C)=O)[C@H](O[C@H]2[C@H]([C@@H](CO)O[C@@H](O)[C@@H]2O)O)O[C@@H]1CO ANBQYFIVLNNZCU-CQCLMDPOSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/389—Keeping log of transactions for guaranteeing non-repudiation of a transaction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/9032—Query formulation
-
- 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
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
-
- 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
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Computer Security & Cryptography (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a blockchain account relevance query method and a false transaction screening method, comprising the following steps: acquiring a plurality of pieces of original transaction data; setting a transaction amount threshold, obtaining transaction data with transaction amount higher than the transaction amount threshold, and generating a transaction database; traversing each transaction data in the transaction database, and inserting the transaction data into the transaction database as the relationship of two account nodes; querying the relevance between two accounts through the CQL statement; traversing each account node, and judging whether the account is directly transferred into the account or not through CQL sentences; if the direct transfer account exists, all transaction paths to the account node are found, and if the direct transfer account is an effective transaction loop, the transaction on the loop is regarded as false transaction. The method screens out false transactions through account relationships, has quick inquiry time and high efficiency, can process a plurality of transactions in judging false transactions of accounts, and can process multiple transaction loops.
Description
Technical Field
The invention relates to the technical field of blockchain transaction paths, in particular to a blockchain account relevance query method and a false transaction screening method.
Background
Neo4j is a high performance NoSQL (non-relational database) graph database. In Neo4j, data is stored in the network rather than in a table, and the data is saved as nodes in the graph and relationships between the nodes. For some application data, such as traffic network data, social network data, etc., the concept of nodes and relationships in a graph may be represented very directly. It is natural to use Neo4j to store these data and the efficiency of use will be higher than that of a relational database. Currently, neo4j is used by many large companies such as Facebook, aleb, hundred degrees, etc.
The data storage model of Neo4j is Node and Relationship, and Property is included. Property uses a bi-directional list of Key-Value pairs to save. The Node stores the first attribute and the first relationship ID. The Relationship holds a start node ID, an end node ID, and an attribute.
With the continued development of blockchain technology, blockchain transactions are also accompanied, and spurious transactions also occupy a high proportion of blockchain transactions. According to the fit degree of the Neo4j database and the blockchain transaction, each account is used as a node, each transaction is used as a relation between the nodes and stored into the Neo4j database, and the blockchain transaction is processed in the Neo4j database. In the prior art, a blockchain transaction account and a transaction hash are directly stored through a relational database MySQL, the transaction path of a single account is queried, and the obvious false transaction is simply judged. However, the search time for searching the relevance of the accounts on the blockchain is long, the efficiency is low, and the situation that many transactions cannot be processed in the false transaction of the accounts and the situation that multiple transaction loops cannot be processed can be judged.
Disclosure of Invention
The invention aims to solve the technical problems of providing a blockchain account relevance query method and a false transaction screening method, which screen false transactions through account relations, have quick query time and high efficiency, can process a plurality of transactions in judging false transactions of accounts, and can process multiple transaction loops.
In order to solve the technical problems, the invention provides a blockchain account relevance query method, which comprises the following steps:
s1, acquiring a plurality of pieces of original transaction data, wherein each piece of original transaction data comprises a transfer-out party account hash, a transfer-in party account hash, a transaction amount and a transaction time stamp;
s2, setting a transaction amount threshold, obtaining transaction data with transaction amount higher than the transaction amount threshold, and generating a transaction database;
s3, traversing each transaction data in the transaction database, and inserting the transaction data into the transaction database as the relationship between two account nodes;
s4, inquiring the relevance between the two accounts through the CQL statement.
Preferably, in the step S1, the obtained original transaction data is stored in MySQL.
Preferably, the setting a transaction amount threshold in S2 specifically includes:
calculating the average transaction amount of all the original transaction data, setting an amount coefficient, and obtaining a transaction amount threshold value, wherein the transaction amount threshold value is equal to the average transaction amount multiplied by the amount coefficient.
Preferably, the S3 includes:
traversing each transaction data in a transaction database, and if a transfer-in party exists and a transfer-out party exists, inserting the transaction data into the database as the relationship of two account nodes; if the transfer-in party exists and the transfer-out party does not exist, the transfer-out party account hash is used as a node name creation node, and transaction data is used as a relation between two account nodes to be inserted into a database; if the transfer-in party does not exist, the transfer-out party exists, the transfer-in party account hash is used as a node name creation node, and transaction data is used as the relation between two account nodes to be inserted into a database; if the transfer-in party does not exist, the transfer-out party does not exist, the transfer-in party account hash and the transfer-out party account hash are respectively used as node names to create nodes, and transaction data is used as the relation of the two account nodes to be inserted into a database.
Preferably, in the step S3, when the transaction data is inserted, if the two account nodes have a transaction relationship, the relationship between the two account nodes is read first, and a new transaction timestamp and a transaction hash are formed into an array, and are inserted into the relationship between the two account nodes.
Preferably, in S4, S4 includes: the relationship within the two account nodes 8 paths is queried by the CQL statement.
The invention also discloses a false transaction screening method based on the block chain account relevance query method, which comprises the following steps: traversing each account node, and judging whether the account is directly transferred into the account or not through CQL sentences; if the account is not directly transferred into, the account is regarded as a normal account; if the direct transfer account exists, all transaction paths to the account node are checked out, and whether the transaction paths are effective transaction loops or not is judged through the time stamp; if it is a valid transaction loop, then transactions on that loop are all considered spurious transactions.
Preferably, the method for judging whether the transaction path is a valid transaction loop through the time stamp comprises the following steps:
each account on the transaction loop correspondingly establishes a transaction list, the transaction list stores transaction information of received transactions of the corresponding account, and the transaction information in each transaction list is ordered according to time sequence;
establishing an empty stack, and moving first transaction information in an initial account on a transaction loop into the stack, wherein the initial account is the first account on the transaction loop;
sequentially judging whether the transaction time of the transaction information in the transaction list corresponding to the account except the initial account is behind the first transaction information in the stack, moving the transaction information meeting the requirements into the stack, and deleting the transaction information before the transaction information meeting the requirements in the corresponding transaction list;
if the number of elements in the stack is equal to the number of accounts, the transaction path is regarded as an effective transaction loop, otherwise, the transaction path is regarded as an ineffective transaction loop;
and (3) emptying the stack, and performing the operation on the rest transaction information of the transaction list in the primary account until no transaction information exists in the transaction list of the primary account.
The invention discloses a computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor implements the steps of the above method when executing the program.
The present invention discloses a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method.
The invention has the beneficial effects that:
the invention solves the problems of account relation query efficiency and complex transaction path processing, and screens false transactions through account relation, has fast query time and high efficiency, can process a plurality of transactions in judging false transactions of accounts, and can process multiple transaction loops.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and specific examples, which are not intended to be limiting, so that those skilled in the art will better understand the invention and practice it.
Referring to fig. 1, the invention discloses a blockchain account relevance query method, which comprises the following steps:
step one, obtaining a plurality of pieces of original transaction data, wherein each piece of original transaction data comprises a transfer-out party account hash, a transfer-in party account hash, a transaction amount and a transaction time stamp. The acquired original transaction data is stored in MySQL.
Setting a transaction amount threshold, obtaining transaction data with transaction amount higher than the transaction amount threshold, and generating a transaction database. The setting method of the transaction amount threshold comprises the following steps: calculating the average transaction amount of all the original transaction data, setting an amount coefficient, and obtaining a transaction amount threshold value, wherein the transaction amount threshold value is equal to the average transaction amount multiplied by the amount coefficient.
Step three, traversing each transaction data in the transaction database through neo4j, and firstly judging whether a transfer-in party and a transfer-out party exist in the database or not, wherein the conditions are as follows:
if the transfer-in party exists and the transfer-out party exists, the transaction data is used as the relation of two account nodes to be inserted into a database; when transaction data are inserted, if the two account nodes have a transaction relationship, the relationship of the two account nodes is read first, a new transaction timestamp and a transaction hash form an array, and the array is inserted into the relationship of the two account nodes;
if the transfer-in party exists and the transfer-out party does not exist, the transfer-out party account hash is used as a node name creation node, and transaction data is used as a relation between two account nodes to be inserted into a database;
if the transfer-in party does not exist, the transfer-in party account hash is used as a node name creation node, and transaction data is used as a relation between two account nodes to be inserted into a database;
and fourthly, if the transfer-in party does not exist and the transfer-out party does not exist, respectively taking the transfer-in party account hash and the transfer-out party account hash as node name creation nodes, and inserting transaction data into a database as the relation between two account nodes.
And fourthly, inquiring the relevance between the two accounts through the CQL statement. The invention can inquire the relationship within 8 paths of two account nodes through CQL sentences.
The invention also discloses a false transaction screening method, which is based on the block chain account relevance query method, traverses each account node and judges whether the account is directly transferred into the account or not through CQL sentences: if the account is not directly transferred into, the account is regarded as a normal account, and the transaction is regarded as a normal transaction; if the transaction path is a valid transaction loop, the transaction on the loop is regarded as false transaction.
The method for judging whether the transaction path is a valid transaction loop through the time stamp comprises the following steps:
1. each account on the transaction loop correspondingly establishes a transaction list, the transaction list stores transaction information of received transactions of the corresponding account, and the transaction information in each transaction list is ordered according to time sequence;
2. establishing an empty stack, and moving first transaction information in an initial account on a transaction loop into the stack, wherein the initial account is the first account on the transaction loop;
3. sequentially judging whether the transaction time of the transaction information in the transaction list corresponding to the account except the initial account is behind the first transaction information in the stack, moving the transaction information meeting the requirements into the stack, and deleting the transaction information before the transaction information meeting the requirements in the corresponding transaction list;
4. if the number of elements in the stack is equal to the number of accounts, the transaction path is regarded as an effective transaction loop, otherwise, the transaction path is regarded as an ineffective transaction loop;
5. and (3) emptying the stack, and performing the operation on the rest transaction information of the transaction list in the primary account until no transaction information exists in the transaction list of the primary account.
For example, the account A is transferred to the account B and three transactions are stored in the list a, the account B is transferred to the account C and three transactions are stored in the list B, the account C is transferred to the account A and three transactions are stored in the list C, the transactions are all ordered in time sequence, an empty stack is established, the first transaction information in the a is moved to the stack, whether the transaction time in the B is after the stack top transaction information is judged in sequence, the first transaction information meeting the requirement in time is moved to the stack, the previous transaction information is deleted from the list B, and similarly, the transaction information meeting the requirement in the list C is found and stored in the stack. If the number of elements in the stack is equal to the number of accounts, then the transaction path is considered to be an active transaction loop. And (5) putting the stack empty, and performing the operation on the rest elements of the list by the same method until no element exists in the list a.
The invention also discloses a computer device comprising a memory, a processor and a computer program stored on the memory and running on the processor, characterized in that the processor implements the steps of the above method when executing the program.
The invention also discloses a computer readable storage medium having stored thereon a computer program which when executed by a processor realizes the steps of the above method.
The above-described embodiments are merely preferred embodiments for fully explaining the present invention, and the scope of the present invention is not limited thereto. Equivalent substitutions and modifications will occur to those skilled in the art based on the present invention, and are intended to be within the scope of the present invention. The protection scope of the invention is subject to the claims.
Claims (9)
1. The block chain account relevance query method is characterized by comprising the following steps of:
s1, acquiring a plurality of pieces of original transaction data, wherein each piece of original transaction data comprises a transfer-out party account hash, a transfer-in party account hash, a transaction amount and a transaction time stamp;
s2, setting a transaction amount threshold, obtaining transaction data with transaction amount higher than the transaction amount threshold, and generating a transaction database;
s3, traversing each transaction data in the transaction database, and inserting the transaction data into the transaction database as the relationship between two account nodes; wherein, through neo4j traversing each transaction data in the transaction database, firstly judging whether the transfer-in party and the transfer-out party exist in the database, the situation is as follows:
if the transfer-in party exists and the transfer-out party exists, the transaction data is used as the relation of two account nodes to be inserted into a database; when transaction data are inserted, if the two account nodes have a transaction relationship, the relationship of the two account nodes is read first, a new transaction timestamp and a transaction hash form an array, and the array is inserted into the relationship of the two account nodes;
if the transfer-in party exists and the transfer-out party does not exist, the transfer-out party account hash is used as a node name creation node, and transaction data is used as a relation between two account nodes to be inserted into a database;
if the transfer-in party does not exist, the transfer-in party account hash is used as a node name creation node, and transaction data is used as a relation between two account nodes to be inserted into a database;
if the transfer-in party does not exist, the transfer-out party does not exist, the transfer-in party account hash and the transfer-out party account hash are respectively taken as node name creation nodes, and transaction data are taken as the relation of the two account nodes to be inserted into a database;
s4, inquiring the relevance between the two accounts through the CQL statement.
2. The blockchain account relevance query method of claim 1, wherein in S1, the obtained raw transaction data is stored in MySQL.
3. The blockchain account association query method of claim 1, wherein the setting of the transaction amount threshold in S2 specifically includes:
calculating the average transaction amount of all the original transaction data, setting an amount coefficient, and obtaining a transaction amount threshold value, wherein the transaction amount threshold value is equal to the average transaction amount multiplied by the amount coefficient.
4. The blockchain account association query method of claim 1, wherein in S3, when transaction data is inserted, if a transaction relationship exists between two account nodes, the relationship between the two account nodes is read first, and a new transaction timestamp and a transaction hash are formed into an array, and are inserted into the relationship between the two account nodes.
5. The blockchain account relevance query method of claim 1, wherein in S4, the S4 includes: the relationship within the two account nodes 8 paths is queried by the CQL statement.
6. A method of screening for spurious transactions based on the blockchain account relevance query method of any of claims 1-5, comprising: traversing each account node, and judging whether the account is directly transferred into the account or not through CQL sentences;
if the account is not directly transferred into, the account is regarded as a normal account;
if the direct transfer account exists, all transaction paths to the account node are checked out, and whether the transaction paths are effective transaction loops or not is judged through the time stamp; if it is a valid transaction loop, then transactions on that loop are all considered spurious transactions.
7. The method for screening false transaction according to claim 6, wherein said method for judging whether the transaction path is a valid transaction loop by means of a time stamp comprises the steps of:
each account on the transaction loop correspondingly establishes a transaction list, the transaction list stores transaction information of received transactions of the corresponding account, and the transaction information in each transaction list is ordered according to time sequence;
establishing an empty stack, and moving first transaction information in an initial account on a transaction loop into the stack, wherein the initial account is the first account on the transaction loop;
sequentially judging whether the transaction time of the transaction information in the transaction list corresponding to the account except the initial account is behind the first transaction information in the stack, moving the transaction information meeting the requirements into the stack, and deleting the transaction information before the transaction information meeting the requirements in the corresponding transaction list;
if the number of elements in the stack is equal to the number of accounts, the transaction path is regarded as an effective transaction loop, otherwise, the transaction path is regarded as an ineffective transaction loop;
and (3) emptying the stack, and performing the operation on the rest transaction information of the transaction list in the primary account until no transaction information exists in the transaction list of the primary account.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of claim 6 or 7 when executing the program.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method of claim 6 or 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011376615.6A CN112488708B (en) | 2020-11-30 | 2020-11-30 | Block chain account relevance query method and false transaction screening method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011376615.6A CN112488708B (en) | 2020-11-30 | 2020-11-30 | Block chain account relevance query method and false transaction screening method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112488708A CN112488708A (en) | 2021-03-12 |
| CN112488708B true CN112488708B (en) | 2024-04-05 |
Family
ID=74937719
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011376615.6A Active CN112488708B (en) | 2020-11-30 | 2020-11-30 | Block chain account relevance query method and false transaction screening method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112488708B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113722361A (en) * | 2021-04-06 | 2021-11-30 | 京东城市(北京)数字科技有限公司 | Information processing method and device and computer equipment |
| CN115689761B (en) * | 2023-01-03 | 2023-03-21 | 华侨大学 | Multithreading parallel detection method and equipment for electronic commerce flow direction path |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014143786A1 (en) * | 2013-03-15 | 2014-09-18 | Informatica Corporation | Data tokenization in an intermediary node |
| WO2018121113A1 (en) * | 2016-12-30 | 2018-07-05 | 中国银联股份有限公司 | Abnormal account transfer detection method and device |
| CN109003089A (en) * | 2018-06-28 | 2018-12-14 | 中国工商银行股份有限公司 | risk identification method and device |
| CN109035010A (en) * | 2018-05-25 | 2018-12-18 | 中国地质大学（武汉） | For the suspicious transaction of block chain password currency and suspicious account analysis method and system |
| CN110209826A (en) * | 2018-02-06 | 2019-09-06 | 武汉观图信息科技有限公司 | A kind of financial map construction and analysis method towards bank risk control |
| CN110414961A (en) * | 2019-06-21 | 2019-11-05 | 深圳壹账通智能科技有限公司 | Prevent transfer account method, device, equipment and the storage medium of the track transactions side of producing |
| WO2020057567A1 (en) * | 2018-09-19 | 2020-03-26 | 苏宁云计算有限公司 | Graph information retrieval method and device |
| CN111353176A (en) * | 2020-05-22 | 2020-06-30 | 支付宝(杭州)信息技术有限公司 | Method and system for inquiring block chain data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10719557B2 (en) * | 2018-01-16 | 2020-07-21 | Palantir Technologies Inc. | Concurrent automatic adaptive storage of datasets in graph databases |
-
2020
- 2020-11-30 CN CN202011376615.6A patent/CN112488708B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014143786A1 (en) * | 2013-03-15 | 2014-09-18 | Informatica Corporation | Data tokenization in an intermediary node |
| WO2018121113A1 (en) * | 2016-12-30 | 2018-07-05 | 中国银联股份有限公司 | Abnormal account transfer detection method and device |
| CN110209826A (en) * | 2018-02-06 | 2019-09-06 | 武汉观图信息科技有限公司 | A kind of financial map construction and analysis method towards bank risk control |
| CN109035010A (en) * | 2018-05-25 | 2018-12-18 | 中国地质大学（武汉） | For the suspicious transaction of block chain password currency and suspicious account analysis method and system |
| CN109003089A (en) * | 2018-06-28 | 2018-12-14 | 中国工商银行股份有限公司 | risk identification method and device |
| WO2020057567A1 (en) * | 2018-09-19 | 2020-03-26 | 苏宁云计算有限公司 | Graph information retrieval method and device |
| CN110414961A (en) * | 2019-06-21 | 2019-11-05 | 深圳壹账通智能科技有限公司 | Prevent transfer account method, device, equipment and the storage medium of the track transactions side of producing |
| CN111353176A (en) * | 2020-05-22 | 2020-06-30 | 支付宝(杭州)信息技术有限公司 | Method and system for inquiring block chain data |
Non-Patent Citations (2)
| Title |
|---|
| 基于Neo4j图谱的信用卡欺诈检测;张芸芸;方勇;黄诚;;信息与电脑(理论版)(第21期);23-25 * |
| 基于图数据库Neo4j的信用卡反欺诈系统的设计与实现;范倬瑜;中国优秀硕士学位论文全文数据库信息科技辑(第2期);I138-863 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112488708A (en) | 2021-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111046034B (en) | Method and system for managing memory data and maintaining data in memory | |
| CN101009516B (en) | A method, system and device for data synchronization | |
| CN104699718B (en) | Method and apparatus for being rapidly introduced into business datum | |
| US8224807B2 (en) | Enhanced utilization of query optimization | |
| WO2020192064A1 (en) | Incremental data consistency implementation method and device | |
| US8924373B2 (en) | Query plans with parameter markers in place of object identifiers | |
| US20240126817A1 (en) | Graph data query | |
| CN112488708B (en) | Block chain account relevance query method and false transaction screening method | |
| US8380663B2 (en) | Data integrity in a database environment through background synchronization | |
| CN102169491B (en) | Dynamic detection method for multi-data concentrated and repeated records | |
| WO2023083237A1 (en) | Graph data management | |
| US8799329B2 (en) | Asynchronously flattening graphs in relational stores | |
| US20090132607A1 (en) | Techniques for log file processing | |
| CN111046106A (en) | Cache data synchronization method, device, equipment and medium | |
| CN102193988A (en) | Method and system for retrieving node data in graphic database | |
| WO2020192663A1 (en) | Data management method and related device | |
| CN112948898A (en) | Method for preventing application data from being tampered in block chain and security module | |
| CN115309789B (en) | Method for generating associated data graph in real time based on intelligent dynamic business object | |
| CN105608103A (en) | Transforming method and device of data storing structure | |
| CN109408462A (en) | A kind of document storage management method and electronic equipment based on educational system | |
| CN110678854B (en) | Data query method and device | |
| CN113157736B (en) | Query plan optimization method and system in distributed database | |
| CN112182028B (en) | Data line number query method and device based on table of distributed database | |
| CN115794842B (en) | Data processing method, device, electronic equipment and medium | |
| CN110110211A (en) | Data query method and apparatus based on universal model |
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 |