CN107423961A - A kind of optimization common recognition method based on random correlation analysis - Google Patents
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Abstract
The invention discloses a kind of optimization common recognition method based on random correlation analysis, including step 1, is traded operation respectively at least two transaction blocks, and Transaction Information is stored in corresponding transaction block；Step 2, random correlation analysis, comparison and sequence are carried out to the Transaction Information at least two transaction blocks and is increased newly or is changed with the structure to Merkel tree, forms positive correlation Merkel's tree construction；Step 3, previous transaction block obtains Transaction Information positive correlation Merkel tree in process of exchange, the transactional operation of latter newly-generated transaction block is performed by the hash data structure of previous transaction block, caused new Transaction Information compares the association being traded between block by random correlation in newly-generated transaction block, at least two transaction blocks is formed a complete transaction block chain.It is short that the common recognition method of the present invention has the advantages that resource utilization is high, transaction block generation limitation is small, common recognition chain produces the cycle.
Description
Technical field
The present invention relates to a kind of common recognition method, more particularly to a kind of optimization common recognition method based on random correlation analysis.
Background technology
The origin of Byzantium's common recognition problem refers to that the Byzantine Empire wants one powerful enemy of attack, is sent for this
10 armies go to surround this enemy.Though this enemy is also enough to resist 5 conventional armies of Byzantium unlike the Byzantine Empire
While attack.Based on some reasons, this 10 armies can not gather together single-point breakthrough, it is necessary in separated state of siege
It is lower to attack simultaneously.They have no odds at any army's independent attack, and being attacked simultaneously unless there are at least 6 armies can just capture
Enemy state.They are dispersed in the surrounding of enemy state, are in communication with each other by signal corps to consult attack purpose and time of attack.Perplex these
The problem of general is that they do not know whether have traitor in them, and traitor may change attack purpose or time of attack without authority.
In this state, can Byzantium generals find a kind of distributed agreement they can remotely consult, so as to win
Fight is taken, Byzantium's problem is an imaginary common recognition problem, and here it is the common recognition mechanism next to be introduced.
Conventional common recognition algorithm is exactly the digging ore deposit book keeping operation mode of bit coin, i.e. proof of work (POW), is calculated by miner
The mode that machine calculation power carrys out deciphering topic fights for book keeping operation right, and gives victor's reward of 12.5 bit coin.Work
Amount proof mechanism fully relies on the modes of economic incentives to roll up book keeping operation participant, so as to dilute the ratio for node of doing evil,
The cost done evil is significantly increased in other words, the person of falsifying accounts needs to control or bribe more nodes.This is a kind of simple and crude
Common recognition mechanism, do not optimize algorithmically, but again very feasible, two maximum transaction block chains of the present scale of construction, than
Special coin and ether mill is all the mode that ore deposit is dug with POW.It is strictly now most practical although proof of work mechanism is not optimal
Feasible common recognition algorithm.
Proof of work mechanism can verify process of exchange in the case where not running complete network node, and user only needs
The most long work that retaining him can be obtained by inquiry proves the copy of the trading post build file of link, then will obtain
The Merkle Branch (Merkel branch) obtained are connected in the transaction block under the timestamp.User can not check the friendship of oneself
Easy process, but a place by being linked in chain, he can see a network node and has received it, and enter at it
One step confirms that network adds block after having received.
As shown in figure 1, the process of exchange of proof of work mechanism includes：A) transaction in a period of time is put into a friendship
In easy block；B) by constantly changing the random number in transaction block, to attempt to collide out a satisfactory transaction block
Hashed value；Once c) finding satisfactory hash matter, that is, think that completing one action amount proves, the friendship in the transaction block
It will easily be recorded in account book；D) hashed value of upper one transaction block is put into next transaction block, forms a chain
Bar (transaction block chain).
There is complete mathematical proof in POW security, this point is POS (point-of-sale terminal) and DPOS (share warrants
Bright mechanism) unrivaled advantage.Transaction block chain common recognition mechanism will typically consider (the distribution refusal that defends against DDOS attack simultaneously
Service attack) and dual payment attack, POW, which has 51% calculation power attack, to be threatened, and bit coin calculation power superpower at present to destroy
The system need to pay great cost.POS can also have the attack of 51% coin age, and DPOS securities depend entirely on the honesty of representative
Degree.NXT theories can realize fast transaction, but need forging node to expose the IP of oneself, thus easily become DDOS
The object of attack, DPOS representative also easily become the object of DDOS attack.
For using digging for the transaction block chain of ore deposit mechanism, proof of work (Proof-of-Work) is exactly a number
Word, we it be referred to as random number, when the data that it was hashed with other merge, one can be produced than regulation desired value more
Small value.Digging ore deposit causes hashing to turn into a kind of rapid computations, unidirectional irreversible algorithm.Finding an effective random number needs
The time is wanted, because (miner) helps them to find a sufficiently small hash without available clue, and uniquely finds one
Method less than desired value is exactly to calculate many hash.When a random number is found, verifying its time just needs 1
Second, then this New Transaction block can broadcast in a network, form newest common recognition and transaction block chain.
The participant that the design object of proof of work mechanism allows unlimited amount and has identity joins and departs from network, simultaneously
One side of attack is difficult to the undue influence by producing multiple identities to obtain to network.For a point-to-point peer-to-peer network
Network, volunteers can not or be not desired to promise to undertake any relation of long standing relation when not doing authentication also.The probability proved due to job search
Property, recruitment nature of testifying result in the need for quantized time piecemeal, and then these piecemeals must be arranged according to random correlation
Sequence.The proof mechanism that works is collided by Hash and obtains match trading block, and proof is calculated on network needs time-consuming, can cause
Resource utilization is low.Also, the work of bit coin is proved to be with very slow speed generation, few to be calculated in the same time
Come.Because transaction is possible to produce with a higher speed, it means that need work to prove that the multiple transaction of support can be strengthened,
Need block (and the proof mechanism that works can not be supported) of more merchandising.
Because transaction block chain is decentralization, there is no center accounting nodes, so needing the whole network to reach to account book altogether
Know.In proof of work mechanism, calculate power with computer by miner and fight for book keeping operation right come the mode of deciphering topic, but work
Amount proof mechanism has the shortcomings that apparent：Consume mass energy, dig ore deposit increasingly centralization, franchise only and calculate power about and and
It is unrelated whether to possess transaction block chain assets, causes the possessor for there are a large amount of bit coin not have the problem of franchise.
In addition, in tradition knows together algorithm model, each block of merchandising records the Transaction Information in 10 minutes, and every 10
A new transaction block is produced after minute, carries out subsequent transaction operation.Block of being merchandised at first completes Transaction Information record
Afterwards, cross and produce within 10 minutes the record that a new transaction block carries out subsequent transaction information, complete the transaction of n transaction block
The time of at least 10* (n-1) minutes is passed through in requirements of process, when colliding match trading block by Hash every time can expend substantial amounts of
Between and resource, such case it is particularly evident when a large amount of transaction occur simultaneously.
Before new transaction block generation, there may be many Transaction Informations etc. to be confirmed in bit coin network, due to having
Transaction be probably invalid, therefore they should all be examined one time.Some may not be related to any tranaction costs, so should
The decision allows such transaction to be recorded on earth.Additionally, it is possible to have certain collection for including two or more Transaction Informations
Close, the transaction inside the set can not effectively simultaneously, but the transaction inside some subsets can effectively simultaneously.Such as
Bag may spend identical bit coin twice, if this occurs, it is necessary to there is a kind of random selection machine in the same time
System determines that transaction is effective.
The content of the invention
To solve above-mentioned the problems of the prior art, the invention provides a kind of optimization based on random correlation analysis to be total to
Knowledge method, so that chain transaction can efficiently carry out common recognition analysis.
To achieve the above object, the particular technique of a kind of optimization common recognition method based on random correlation analysis of the invention
Scheme is as follows：
A kind of optimization common recognition method based on random correlation analysis, comprises the following steps：Step 1, handed at least two
Operation is traded in easy block respectively, and Transaction Information is stored in corresponding transaction block；Step 2, at least two
Transaction Information in transaction block carry out random correlation analysis, comparison and sequence with the structure of Merkel tree is carried out it is newly-increased or
Modification, builds and constrains the structure of Merkel tree, forms positive correlation Merkel's tree construction；Step 3, at least two trading posts
In block, previous transaction block obtains Transaction Information positive correlation Merkel tree in process of exchange, passes through the Kazakhstan of previous transaction block
Uncommon structure performs the transactional operation of latter newly-generated transaction block, and caused new Transaction Information leads in newly-generated transaction block
Cross random correlation and compare the association being traded between block, at least two transaction blocks is formed a complete trading post
Block chain, so as to complete transaction common recognition.
Further, during formation positive correlation Merkel's tree construction of step 2, the time is recorded using timestamp
In the cycle, account book information is recorded, in the time cycle, caused Transaction Information is stored in corresponding transaction block, sent out by after
Raw transaction generates coefficient correlation by carrying out random correlation analysis with the Transaction Information in previous transaction block, and according to
Random relative coefficient produces correlation factor, so as to build and constrain the structure of Merkel tree.
Further, in the random correlation comparison process of Transaction Information of step 2, strengthened by random correlation analysis
Incidence relation between transaction record, it is newly-generated transaction block in newly-increased Transaction Information by with Merkel's leaf child node
Existing Transaction Information carries out random correlation analysis to generate factor of influence, is selectively inserted into Merkel tree, and enhancing is handed over
Transaction common recognition between easy block.
Further, during the random relevance ranking method generation Merkel tree of use of step 2, each transaction
Block stores the hash function and coefficient correlation of previous transaction block respectively, will be random by the random correlation analysis of block of merchandising
The high transaction block interconnection of correlation, and be ranked up each transaction block by random correlation in closing the transaction.
Further, during the random relevance ranking method generation Merkel tree of use of step 2, multiple transaction
By adjacent record positive correlation sequence generation Merkel's leaf child node, and the structure of Merkel tree is constantly updated, form one
Individual positively related top layer tree root, to etc. Transaction Information to be confirmed screens in network, to reject invalid transaction note
Record, improve book keeping operation efficiency.
Further, during the formation transaction block chain of step 3, newly-generated transaction onblock executing identical is write from memory
The generation operation of Ke Er trees, latter newly-generated transaction block is performed by the hash data structure of previous transaction block, makes each trading post
Transaction Information in block obeys random correlation distribution, so as to form a transaction block chain structure.
Further, in step 3, Merkel's leaf child node in each transaction block is sorted by positive correlation, profit
A complete transaction block chain is formed with the mutual incidence relation of each transaction block, to improve the efficiency of common recognition method
And stability.
Further, in the transaction block of step 1, each block of merchandising includes multiple Transaction Informations, each Transaction Information
Represented by hash function, transaction association is judged by the Hash operation between Transaction Information, form positive correlation or negatively correlated silent
Ke Er trees are simultaneously stored in transaction block.
Further, in the transaction block comprising multiple Transaction Informations of step 1, multiple friendships in each block of merchandising
Easy information is ranked up by random correlation, when producing a large amount of Transaction Informations in each transaction block, each transaction block
Between reached common understanding by random correlation, wherein the random correlation highest of adjacent transaction block, in order to form mutual system
About, the transaction block chain mutually known together.
The common recognition method of the optimization based on random correlation analysis of the present invention can overcome block of being merchandised under common recognition mechanism to produce
The limitation of speed, random correlation analysis fundamentally is carried out to the Transaction Information on each transaction block, on this basis
Merkel tree is generated according to random relevance ranking, recorded in transaction block, each block of merchandising includes previous transaction block
Hash function, whenever thering is new Transaction Information to produce in block of merchandising, compared by random correlation and press Transaction Information
Necessarily it is linked in sequence in Merkel tree, due to the random correlation highest of adjacent transaction block, by building adjacent positive correlation
Model enables whole chain transaction more efficiently to carry out common recognition analysis, when screening typing information and reducing Transaction Information typing
Between the cycle, improve transaction block utilization rate.
The common recognition method of the optimization based on random correlation analysis of the present invention has advantages below：
1) compared by random correlation and be connected to Transaction Information in Merkle Tree by certain associated order, due to
The random correlation highest of adjacent transaction record, can more it be increased between block by building adjacent positive correlation model and merchandising
Effect stably carries out common recognition analysis；
2) correlation factor in transaction block can record for Transaction Information provide support, guiding Merkle Tree shape
Into；
3) Transaction Information each in transaction block is ranked up by random correlation, when a large amount of Transaction Informations occur
When, it can be reached common understanding by this random correlation mechanism between each transaction block, the adjacent transaction random correlation of block
Highest, form a complete transaction block chain for mutually restricting, mutually knowing together；
4) limitation that block of being merchandised under common recognition mechanism produces speed is overcome, avoids Hash collision match trading block
Complicated processes；
5) validity of Transaction Information to be confirmed that may be present in network is checked by random correlation analysis；
6) the random relevance ranking between transaction block can shorten the common recognition cycle, the stabilization of lifting transaction block chain
Property.
Brief description of the drawings
Fig. 1 is existing proof of work mechanism structure schematic diagram；
Fig. 2 is the flow chart of the optimization common recognition method based on random correlation analysis of the present invention；
Fig. 3 is that the random correlation in the optimization common recognition method based on random correlation analysis of the present invention compares flow
Figure.
Embodiment
In order to be best understood from the purpose of the present invention, structure and function, below in conjunction with the accompanying drawings, to the present invention based on random
The optimization common recognition method of correlation analysis does further detailed description.
In the present invention, Hash represents cryptographic Hash, and Merkle Tree refer to Merkel tree, namely Hash table is extensive,
Merkle Root refer to Merkle Tree table root, and Block refers to block of merchandising, and for store transaction information, Tx refers to hand over
Easy information.
Random correlation analysis refers to analyze two or more Transaction Informations for possessing random correlation, so as to weigh
Measure the related intimate degree of two Transaction Informations.Need certain contact or probability be present just between the element of random correlation
Random correlation analysis can be carried out.
The statistics of random correlation is a kind of method conventional in economics, in the present invention, random correlation with analysis
Refer to when contact between two Transaction Informations being present, a typical performance is：The generation of one transaction can be to another transaction
Have an impact.Related to be divided into positive correlation and negatively correlated two kinds of situations again, the positive correlation degree of two transaction is higher to represent association
Closer, the purpose of carrying out random correlation analysis is to carry out random correlation analysis to Transaction Information, forms one and is based on positive
The Merkel tree of distribution is closed, recurrence sequence is carried out between each transaction block, it is tight between transaction block so as to strengthen
Close property and stability, because transaction is possible to produce with a higher speed, traditional common recognition mechanism can not overcome reinforcing branch
The shortcomings that holding multiple transaction.
Fig. 2 shows the flow chart of the optimization common recognition method based on random correlation analysis of the present invention, the time in figure
Stab for recording the time cycle, parameter is used to record account book information, and in time period, the Transaction Information of generation is deposited
In a certain transaction block, the transaction occurred afterwards generates phase relation by carrying out random correlation analysis with previous Transaction Information
Number, and correlation factor is produced according to random relative coefficient, build and constrain the structure of Merkel tree.Thus, by transaction
The random correlation of information is ranked up, and the structure of Merkel tree is increased newly or changed, ultimately forms positive correlation Merkel
Tree construction.
In another time period, each transaction block carries out identical operation, passes through the Hash of previous transaction block
Structure performs subsequent operation, and this common recognition model may eventually form a transaction block chain structure, the friendship in block of each merchandising
Easy information obeys random correlation distribution, and the correlation degree highest between adjacent transaction block.
Fig. 3 shows that the random correlation in the optimization common recognition method based on random correlation analysis compares flow, in Fig. 3
In shown random correlation comparison process, by the incidence relation between random correlation analysis enhancing transaction record, and with
Transaction common recognition based on this between enhancing transaction block.In the random correlation comparison process of information is traded, increase newly
Transaction passes through and the existing Transaction Information of Merkel's leaf child node carries out random correlation analysis, and generates factor of influence, selects
Suitable position in Merkel tree is inserted into selecting property, such as the suitable position can be understood as newly-increased Transaction Information and pass through
With the correlation analysis of existing Transaction Information, it is inserted into according to the selection of the factor of influence of generation by the higher leaf node of correlation
Side, the purpose of leaf node positive correlation sequence, i.e., adjacent leaf node correlation highest, and if block of each merchandising are reached with this
Correlation highest between dry Transaction Information.
During using random relevance ranking method generation Merkel tree ,/n passes through adjacent note for transaction 1/2/3/ ...
Positive correlation sequence generation leaf node is recorded, the structure of Merkel tree is continuously updated within cycle regular hour, ultimately forms one
Individual positively related top layer tree root, the random correlation highest of adjacent node, i.e., adjacent Transaction Information are higher into positive correlation.Pass through
This sort algorithm can reject invalid transaction note effectively to etc. Transaction Information to be confirmed screens in network
Record, improve book keeping operation efficiency.
In the random correlation shown in Fig. 3 compares flow, previous transaction block obtains Transaction Information positive correlation Merkel
Set, caused new Transaction Information compares the pass being traded between block by random correlation in newly-generated transaction block
Connection (coefficient correlation is closer, and the correlation degree between block of merchandising is bigger), rather than the Hash of tradition common recognition algorithm collided
Journey.Newly-increased transaction onblock executing identical Merkel tree generation operation, the leaf node of Merkel tree all sort by positive correlation,
Between each other due to strong incidence relation, the efficiency and stability of common recognition method are effectively improved, and ultimately forms one completely
Transaction block chain.
Thus, the optimization common recognition method of the invention based on random correlation analysis can realize between Transaction Information, transaction
Random correlation analysis between block, wherein each transaction block includes some Transaction Informations, each Transaction Information Hash
Function representation, transaction association can be judged by the Hash operation between Transaction Information, form positive correlation or negatively correlated Merck
You set and be stored in transaction block in (Merkel tree can regard the extensive of Hash table as, and Hash table is considered as a kind of special
Merkel tree, the i.e. height of tree are 2 multi-fork Merkel tree, and wherein Merkle Root are top layer tree root).Between each transaction block
The hash function and coefficient correlation of previous transaction block are stored, it is by the random correlation analysis of block of merchandising that random correlation is high
The interconnection of transaction block, and in closing the transaction, each transaction block is ranked up by random correlation, adjacent transaction block
Random correlation highest, the random correlation of transaction block on both sides is minimum, forms a complete transaction block chain.The present invention
Based on random correlation analysis optimization common recognition method for tradition common recognition algorithm resource utilization it is low, transaction block generation
The problems such as limitation, long common recognition chain generation cycle or defect are improved well.
It is appreciated that the present invention is described by some embodiments, and what those skilled in the art knew, do not taking off
In the case of from the spirit and scope of the present invention, various changes or equivalence replacement can be carried out to these features and embodiment.Separately
Outside, under the teachings of the present invention, these features and embodiment can be modified with adapt to particular situation and material without
The spirit and scope of the present invention can be departed from.Therefore, the present invention is not limited to the particular embodiment disclosed, and is fallen with
Embodiment in the range of claims hereof belong to the present invention protect in the range of.
Claims (9)
- A kind of 1. optimization common recognition method based on random correlation analysis, it is characterised in that comprise the following steps：Step 1, operation is traded respectively at least two transaction blocks, and Transaction Information is stored in corresponding transaction In block；Step 2, random correlation analysis, comparison and sequence are carried out with to silent to the Transaction Information at least two transaction blocks The structure of Ke Er trees is increased newly or changed, and is built and is constrained the structure of Merkel tree, forms positive correlation Merkel's tree construction；Step 3, at least two transaction blocks, previous transaction block obtains Transaction Information positive correlation in process of exchange and write from memory Ke Er trees, the transactional operation of latter newly-generated transaction block, newly-generated friendship are performed by the hash data structure of previous transaction block Caused new Transaction Information compares the association being traded between block by random correlation in easy block, makes at least two Block of merchandising forms a complete transaction block chain, so as to complete transaction common recognition.
- 2. the optimization common recognition method according to claim 1 based on random correlation analysis, it is characterised in that in step 2 Formation positive correlation Merkel's tree construction during, using timestamp record the time cycle, record account book information, in the time In cycle, by caused Transaction Information be stored in corresponding to transaction block in, by the transaction of rear generation by with previous trading post Transaction Information in block carries out random correlation analysis, generates coefficient correlation, and according to random relative coefficient produce it is related because Son, so as to build and constrain the structure of Merkel tree.
- 3. the optimization common recognition method according to claim 2 based on random correlation analysis, it is characterised in that in step 2 The random correlation comparison process of Transaction Information in, by random correlation analysis strengthen transaction record between incidence relation, Newly-increased Transaction Information in newly-generated transaction block with the existing Transaction Information of Merkel's leaf child node by carrying out random phase The analysis of closing property is selectively inserted into Merkel tree, the transaction common recognition between enhancing transaction block with generating factor of influence.
- 4. the optimization common recognition method according to claim 3 based on random correlation analysis, it is characterised in that in step 2 Use random relevance ranking method generation Merkel tree during, each transaction block stores previous transaction block respectively Hash function and coefficient correlation, the high transaction block of random correlation is interconnected by the random correlation analysis of block of merchandising, And each transaction block is ranked up by random correlation in closing the transaction.
- 5. the optimization common recognition method according to claim 3 based on random correlation analysis, it is characterised in that in step 2 Use random relevance ranking method generation Merkel tree during, multiple transaction are sorted by adjacent record positive correlation and given birth to Into Merkel's leaf child node, and the structure of Merkel tree is constantly updated, a positively related top layer tree root is formed, with to net Etc. Transaction Information to be confirmed is screened in network, rejects invalid transaction record, improves book keeping operation efficiency.
- 6. the common recognition method of the optimization based on random correlation analysis according to claim 4 or 5, it is characterised in that in step During rapid three formation transaction block chain, the generation operation of newly-generated transaction onblock executing identical Merkel tree, pass through The hash data structure of previous transaction block performs latter newly-generated transaction block, make the Transaction Information in each transaction block obey with Machine correlation is distributed, so as to form a transaction block chain structure.
- 7. the optimization common recognition method according to claim 6 based on random correlation analysis, it is characterised in that in step 3 In, Merkel's leaf child node in each transaction block is sorted by positive correlation, it is mutual using each transaction block Incidence relation form a complete transaction block chain, to improve the efficiency of common recognition method and stability.
- 8. the optimization common recognition method according to claim 1 based on random correlation analysis, it is characterised in that in step 1 Transaction block in, each block of merchandising includes multiple Transaction Informations, and each Transaction Information is represented by hash function, passes through transaction Hash operation between information judges transaction association, forms positive correlation or negatively correlated Merkel tree and is stored in transaction block In.
- 9. the optimization common recognition method according to claim 8 based on random correlation analysis, it is characterised in that in step 1 The transaction block comprising multiple Transaction Informations in, multiple Transaction Informations in each block of merchandising are carried out by random correlation Sequence, when producing a large amount of Transaction Informations in each transaction block, reached altogether by random correlation between each transaction block Know, wherein the random correlation highest of adjacent transaction block, in order to form mutually restriction, the transaction block mutually known together Chain.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108062672A (en) * | 2017-12-07 | 2018-05-22 | 北京泛融科技有限公司 | A kind of process dispatch method based on block chain intelligence contract |
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| CN108491266A (en) * | 2018-03-09 | 2018-09-04 | 联想(北京)有限公司 | Data processing method, device based on block chain and electronic equipment |
| CN108712486A (en) * | 2018-05-11 | 2018-10-26 | 北京奇虎科技有限公司 | Proof of work method and device |
| CN108833353A (en) * | 2018-05-18 | 2018-11-16 | 中南大学 | The quantum Byzantium Agreement Methods participated in based on tripartite |
| CN109447795B (en) * | 2018-09-11 | 2021-06-04 | 中国人民解放军国防科技大学 | Byzantine consensus method supporting rapid achievement of final confirmation |
| CN109447795A (en) * | 2018-09-11 | 2019-03-08 | 中国人民解放军国防科技大学 | Byzantine consensus method supporting rapid achievement of final confirmation |
| CN109389502B (en) * | 2018-10-08 | 2019-12-06 | 莆田市烛火信息技术有限公司 | consensus method of block chains depending on related chain computing power |
| CN109389502A (en) * | 2018-10-08 | 2019-02-26 | 莆田市烛火信息技术有限公司 | A kind of common recognition method for depending on related chain and calculating the block chain of power |
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| CN113826355A (en) * | 2019-04-12 | 2021-12-21 | 区块链控股有限公司 | Short transaction identifier collision detection and coordination |
| CN114827165A (en) * | 2022-05-30 | 2022-07-29 | 蚂蚁区块链科技(上海)有限公司 | Method and block link point for grouping multiple transactions |
| CN114827165B (en) * | 2022-05-30 | 2024-01-23 | 蚂蚁区块链科技(上海)有限公司 | Method and block link point for grouping multiple transactions |
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