CN113239078B - Data rapid query method based on alliance chain - Google Patents

Data rapid query method based on alliance chain Download PDF

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CN113239078B
CN113239078B CN202110531938.6A CN202110531938A CN113239078B CN 113239078 B CN113239078 B CN 113239078B CN 202110531938 A CN202110531938 A CN 202110531938A CN 113239078 B CN113239078 B CN 113239078B
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authentication
block
query
data
equipment
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CN113239078A (en
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王心妍
蒋炜
郭少勇
贾峥
李东
邵苏杰
远方
张静
马圳江
朱贝贝
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State Grid Corp of China SGCC
Beijing University of Posts and Telecommunications
Information and Telecommunication Branch of State Grid Henan Electric Power Co Ltd
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State Grid Corp of China SGCC
Beijing University of Posts and Telecommunications
Information and Telecommunication Branch of State Grid Henan Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2453—Query optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2228—Indexing structures
    • G06F16/2246—Trees, e.g. B+trees
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2471—Distributed queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/44—Program or device authentication
    • 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

Abstract

The invention discloses a rapid data query method based on a alliance chain, which comprises the following steps: acquiring an authentication ID of equipment inquiring the alliance chain; confirming the number of layers of the authentication ID in a levelDB corresponding to the alliance chain, and filtering the authentication ID by using an improved bloom filter, wherein the improved bloom filter positions a cache line by bitwise AND operation when mapping and filtering equipment; using a block level index to take out block information corresponding to an authentication ID from a level DB, wherein the block level index comprises the authentication ID and a unique block identifier, and the block information comprises a block height and a block address; and positioning the authentication ID corresponding to the block in the alliance chain by using the block data B + tree index according to the block information, if the authentication IDs are consistent, judging that the equipment is trusted, and if not, judging that the equipment is untrusted and finishing the query. The invention can realize high-efficiency authentication data query, and quickly improve the efficiency of data query through a double-layer index structure.

Description

Data rapid query method based on alliance chain
Technical Field
The invention relates to the technical field of block chains, in particular to a rapid data query method based on a alliance chain.
Background
Data distribution in a block chain is that all data are stored in all nodes, along with the increase of the number of blocks and the wide application of a block chain technology, more and more query requests exist, but the storage of the blocks is a chain storage structure, and the query efficiency and the stored data amount are in a linear relation, so that how to ensure the rapid and reliable query of the block data becomes a problem which needs to be solved more and more.
Chinese patent (patent application number: 201810534022.4, granted announcement date: 20200110) discloses a method for storing and reading block chain blocks based on files, wherein when data is written in a block chain node, the last successfully submitted block number is read, the current file writing position is positioned according to the block number, and then the latest block information is written; and generating an index according to the written block information, and updating the block number successfully submitted for the last time. Based on the method, the efficiency of inquiring the whole block is improved under the condition of ensuring that the block information can be accurately and completely written. Although the method can greatly improve the query efficiency of the block data, the credibility of the query data is not considered.
Chinese patent (patent application No. 202011283224X, published as 20210129) discloses a block chain terminal data credibility query system and an implementation method thereof, where the implementation method of the block chain terminal data credibility query system is performed through a data synchronization model, a hierarchical Verification mechanism model and a data analysis model, and the block chain terminal data credibility query system is proposed for the problem of lack of data Verification in a block chain query scene, and the query system includes a full node, an MCV (media-Root Verification) node and an SCV (simple comparison Verification) node, and a hierarchical Verification mechanism is adopted, so that when a query request is received, only simple and fast comparison Verification work is performed, complex and time-consuming Calculation Verification work can be completed in advance, and data credibility and query efficiency are improved. Although the patent creatively adopts MCV and SCV nodes to check block data in advance, the efficiency is improved, and the reliability of the data can be ensured, however, the method brings a large amount of additional resource consumption of the system, and the performance is difficult to ensure along with the increase of the scale of the system.
Disclosure of Invention
The invention provides a rapid data query method based on a block chain, aiming at the technical problem that the alliance chain has low data query efficiency, query performance and data reliability when a plurality of data query optimizations are carried out by the block chain in a chain type storage structure.
A rapid data query method based on a federation chain comprises the following steps:
s1, acquiring the authentication ID of the equipment in the inquiry alliance chain;
s2, confirming the layer number of the authentication ID in the levelDB corresponding to the alliance chain, filtering the authentication ID by using an improved bloom filter, wherein the improved bloom filter adopts bitwise AND operation positioning cache line when mapping and filtering the equipment;
s3, using a block level index to take out the block information corresponding to the authentication ID from the level DB, wherein the block level index comprises the authentication ID and a unique block identifier, and the block information comprises a block height and a block address;
and S4, locating the authentication ID corresponding to the block in the alliance chain by using the block data B + tree index according to the block information obtained in the step S3, if the obtained authentication ID is consistent with the authentication ID in the step S1, determining that the equipment is trusted, and if not, determining that the equipment is untrusted and finishing the query.
The authentication ID is generated by adopting a snowflake algorithm, and the authentication ID corresponds to the equipment one by one.
The step S2 includes:
s2.1, searching each layer of the levelDB corresponding to the alliance chain, and confirming the layer number of the authentication ID;
s2.2, positioning a cache line where the authentication ID is located from the layer number confirmed in the step S2.1 by using an improved bloom filter;
s2.3, dividing the authentication ID into k sections, and respectively calculating a substitute function of the hash function corresponding to the segmented authentication ID by using bitwise AND operation;
and S2.4, respectively hashing each authentication ID by using a substitute function of the hash function obtained in the step S2.3, confirming whether the data on the corresponding bit in the cache line obtained in the step S2.2 are all 1 according to a calculation result, if so, executing the step S3, and if not, judging that the query equipment is not credible and finishing the query.
In step S2.2, the formula of the cache line where the location authentication ID is located is:
j＝hash(d)&(2 n -1)；
in the formula 2 n M denotes the total number of lines of the cache line, d denotes the authentication ID, and j denotes the jth cache line.
In step S2.3, a calculation formula of a substitute function of the hash function corresponding to the segmented authentication ID is:
Figure BDA0003068217440000021
in the formula, y s Alternative function to the hash function representing the authentication ID of the s-th segment, x s Represents the s-th authentication ID, s is more than or equal to 1 and less than or equal to k, k represents the total number of hash functions, t and i both represent variables, and i is more than or equal to 0 and less than or equal to i<k-1。
In step S3, the structure of the chunk level index is a key-value type, and the key-value type is an authentication ID and a chunk unique identifier, and the chunk unique identifier is a chunk height.
The invention has the beneficial effects that:
when an inquiry request is received, a corresponding authentication ID is firstly obtained, corresponding data can be quickly found from the level DB by using the improved bloom filter, meanwhile, according to block information corresponding to current data in the level DB, a block is quickly indexed through the block data B + tree index and then verified, efficient inquiry of the authentication data can be realized, the efficiency of data inquiry is quickly improved through a double-layer index structure of the block level index and the block data B + tree index, and the pressure of a CPU for searching the data is greatly reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 shows the structure of an improved bloom filter.
Fig. 2 is a block level index diagram.
Fig. 3 is a diagram illustrating an index data structure.
Fig. 4 is a schematic flow chart of device query.
Fig. 5 is a diagram illustrating the relationship between the number of Hash functions and MSPS in negative-direction query.
FIG. 6 is a diagram illustrating the relationship between the number of Hash functions and MSPS in forward query.
FIG. 7 is a diagram showing the relationship between the number of bits of the improved bloom filter and the number of bits of the conventional bloom filter and MSPS.
Fig. 8 is a diagram illustrating the relationship between the block creation time and the authentication data in four block construction methods.
FIG. 9 is a diagram illustrating the relationship between the query time and the authentication data in four block construction methods.
FIG. 10 is a schematic diagram of the relationship between QPS and delay of the SEDB according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
The structure, data mapping and query method of the traditional bloom filter comprises the following steps:
the conventional bloom filter is composed of an n-bit array and k mutually independent hash functions. When the element x is inserted, the element x is respectively hashed through k hash functions, a result of the element x after being hashed is obtained, the result is used as a bit of a cache line of a traditional bloom filter, and data in the corresponding bit in the cache line is set to be 1. When data is queried, firstly, mapping a key value to be queried to bits corresponding to a bit array through k hash functions, and if a certain bit is not 1, indicating that the element is not in a data range of the bloom filter.
A rapid data query method based on a federation chain comprises the following steps:
s1, acquiring the authentication ID of the equipment in the inquiry alliance chain;
the authentication ID is generated through a snowflake algorithm, and the authentication ID and the equipment are in one-to-one correspondence. Because the authentication ID is generated by adopting a snowflake algorithm, the generated authentication IDs are sequentially increased along with the increase of time, and the equipment for inquiring the alliance chain can be verified by utilizing the authentication IDs.
S2, confirming the layer number of the authentication ID in the levelDB corresponding to the alliance chain, filtering the authentication ID by using an improved bloom filter, wherein the improved bloom filter positions the cache line by bit and operation when mapping and filtering the device, and the method comprises the following steps:
s2.1, searching each layer of the levelDB corresponding to the alliance chain according to the authentication ID, and confirming the layer number of the authentication ID;
confirming whether the authentication ID is between the minimum value and the maximum value of the current layer or not according to the time in the authentication ID, if so, the layer number of the current layer is the layer number of the authentication ID stored in the step S1 in the level DB, and executing the step S2.2; if not, continuing to search the next layer until the layer number of the authentication ID is confirmed, and then executing the step S2.2, and if the authentication ID does not exist in any layer of the levelDB, judging that the inquiry equipment cannot be trusted to finish the inquiry.
S2.2, positioning a cache line where the authentication ID is located from the layer number confirmed in the step S2.1 by using an improved bloom filter;
as shown in fig. 1, the improved bloom filter includes m cache lines, the length of each of the m cache lines is L bits, all the cache lines are tightly arranged in the memory, and the cache lines are positioned by bitwise and operation when the authentication ID is mapped and filtered.
The formula of the cache line where the location authentication ID is located is as follows:
j＝hash(d)&(2 n -1)； (2)
in the formula (2) n M represents the total number of lines of the cache line, d represents the authentication ID, j represents the jth cache line, j is equal to or greater than 1 and equal to or less than m, and j is an integer.
Changing the randomly selected cache lines of the traditional bloom filter to positioning the cache lines by bitwise AND reduces the execution pressure of the CPU.
S2.3, dividing the authentication ID into k sections, and respectively calculating a substitute function of the hash function corresponding to the segmented authentication ID by using bitwise AND operation;
the calculation formula of the substitute function of the hash function corresponding to the segmented authentication ID is as follows:
Figure BDA0003068217440000041
in the formula, y s Alternative function to the hash function representing the authentication ID of the s-th segment, x s Represents the s-th authentication ID, s is more than or equal to 1 and less than or equal to k, k represents the total number of hash functions, t and i both represent variables, and i is more than or equal to 0 and less than or equal to i<k-1。
And S2.4, respectively hashing each authentication ID by using a substitute function of the hash function obtained in the step S2.3, confirming whether the data on the corresponding bit in the cache line obtained in the step S2.2 are all 1 according to a calculation result, if so, executing the step S3, and if not, judging that the query equipment is not credible and finishing the query.
y s &(2 l -1)； (4)
In the formula 2 l Representing the number of bits per cache line.
S3, using a block level index to take out the block information corresponding to the authentication ID from the level DB, wherein the block level index comprises the authentication ID and a unique block identifier, and the block information comprises a block height and a block address;
the block-level index is based on each block of the federation chain distributed storage, each block maintaining a respective index. As shown in fig. 2, the block-level index has a key-value structure, which is an authentication ID and a block unique identifier, respectively, and the block height is set as the block unique identifier, which is a pointer that can determine the block height and the block address according to the block unique identifier.
The specific blocks in the alliance chain can be quickly positioned according to the block information in the levelDB through the block level index, so that the query efficiency is accelerated, and the time complexity and the space complexity of the block level index are o (1) and o (n).
And S4, locating the certification ID corresponding to the block in the alliance chain by using the B + tree index of the block data according to the block information obtained in the step S3, if the obtained certification ID is consistent with the certification ID in the step S1, determining that the equipment is trusted, and if not, determining that the equipment is untrusted and finishing the inquiry.
As shown in fig. 3, the leaf node includes a binary group, which is (authentication ID, block address), and takes (300, Pm) as an example, the storage location of the authentication data in the block, which indicates that the authentication ID is 300, is Pm, and the query is performed from the root node according to the principle that the value of the left child node < the root node < the right child node.
The time complexity of the block data B + tree index is o (1+ c), wherein c represents the number of layers of the B + tree, and in an actual scene, the number of layers of the B + tree is very low and can be ignored, so that the efficiency of querying data from a alliance chain can be greatly improved.
As described above, the present invention first searches the persisted level DB layer by layer according to the time sequence. Before entering the current layer search, firstly judging whether the authentication ID is between the minimum value and the maximum value of the current layer, if not, directly continuing to inquire in the next layer. If the authentication ID is between the maximum value and the minimum value of the current layer, an improved bloom filter is adopted to filter the authentication ID, and if the authentication ID is filtered by the bloom filter, the authentication ID is searched in the layer, because the data of each layer is also associated with time, the authentication IDs corresponding to all the authentication data can be ensured to be stored in a certain layer in an increasing mode. If the corresponding ID is found, the corresponding data is obtained, the value stored by the level DB not only stores the data of the block, but also stores the height of the block where the data is located, the height is a main key in the block level index, the corresponding node storing the block can be directly located according to the index, and then the data is decrypted through a private key. Considering that the data in the block may be huge, a B + tree index of block data is established for the data in each block in units of blocks. And then, acquiring target data according to the B + tree index of the block data, and comparing the target data with the data in the persistent data levelDB, so as to finish the trusted query.
The invention is simulated and verified by selecting a plurality of devices, and the specific parameters of the devices are shown in the following table:
IP address Configuration of OS
192.168.1.122 Intel Core i5-8259U；MEM:32G；SSD:1T Windows10
192.168.1.70 Intel Core i7-8750H；MEM:32G；SSD:1T Windows10
192.168.1.123 IntelCore i7-8750H；MEM:32G；SSD:1T Windows10
192.168.1.54 Intel Core i5-8265U；MEM:64G；SSD:2T Windows10
192.168.1.77 Intel Core i5-5257U；MEM:64G；SSD:2T Windows10
192.168.1.100 Intel Core i5-5257U；MEM:64G；SSD:2T Windows10
1. Performance analysis of an improved bloom Filter Using Million Searches Per Second (MSPS) as Performance indicators for queries
Because the influence on the query performance of a certain bloom filter is the number of hash functions and the number of bits of the bloom filter, the invention reduces the access times and cache misses and maximally reduces the hash operation, thereby greatly reducing the cost of data search.
The calculation formula for searching the MSPS is as follows:
MSPS＝req/sec*10 6 ； (5)
where req represents the number of inquiry device requests per second, sec represents seconds;
as shown in fig. 5 and fig. 6, the improved bloom filter (MCBF) proposed by the present invention has far better performance than the original bloom filter regardless of the positive query or the negative query, in which the MSPS of the positive query is slightly lower than that of the negative query because the positive query has all bits calculated. It can be seen from the figure that, when the number of hash functions is 8, the performance MCBF for forward query is 3 times better than that of the conventional Bloom Filter (BF), while the performance MCBF for negative query is nearly 3 times better than that of BF, and with the increase of the number of hash functions, the degradation of the query performance of BF is much faster than that of MCBF, because the original hash function is replaced by bitwise and operation, the time overhead of hash operation is greatly reduced.
As shown in fig. 7, fig. 7 is a graph of the bit number of MCBF and the change relationship between the bit number of BF and MSPS, and it can be seen from the graph that when the bit number is smaller, the query performance of both bloom filters is hardly affected, but as the bit number is gradually increased, the query performance of both bloom filters is significantly reduced because the bit number of the bloom filter gradually exceeds the size of the cache, however, in any case, it can be seen that the query performance of MCBF is better than that of BF. Therefore, the invention can greatly improve the query efficiency and reduce the query cost.
2. Block query algorithm based on B + tree index
As shown in fig. 8, from the verification of the present invention by using the four comparison dimensions of the normal hyperridge building block, the block level index building block, the block data B + tree index building block, and the two indexes Mix, it can be seen from the figure that the generation time of the block using the normal hyperridge building block is almost the same as the generation time of the block using the block data B + tree index, because the B + tree index is inside the block, there is no extra consumption, which proves that the block creation time is hardly affected by using the intra-block index. As shown in fig. 9, the average query time for querying a certain authentication ID is compared, and the variable is the data amount in the block, so that it can be seen from the graph that the query efficiency of the three methods is obviously improved compared with the method of constructing the block by using a regular superhedger. When both indexes are used, the promotion is most, and only the B + tree index in the block is used, the promotion is not particularly obvious, and as the normal Hyperhedger building block (origin) is adopted, along with the increase of the blocks, the query cost is increased more and more, and the query efficiency is not effectively reduced because the search block still needs to be traversed, but when the block-level index is adopted, although the blocks are increased, the query efficiency is hardly influenced because the index can be directly positioned to the block for retaining data, and the query efficiency is greatly improved. This advantage will be greater as the amount of data increases.
3. Comprehensive consideration query efficiency
Firstly, 100000 authentication data are randomly simulated and stored in a block chain system in three parts, and for the query requirement in the scene of trusted authentication of the internet of things, the query efficiency of the invention and the SEBDB (semantic issued block chain) changes as shown in fig. 10, as the QPS (query rate per second) increases, the delay of the BSDS algorithm is lower than the query delay under the SEBDB, partly because the BSDS is more suitable for query of the authentication data, and the query efficiency can be improved by the improved bloom filter and the B + tree index, and as the QPS exceeds 1000, the delay of the SEBDB increases faster, that is, under the same hardware condition, the BSDS can process more authentication query requests, so the invention can greatly improve the query efficiency of the block and improve the authentication efficiency of the internet of things.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (6)

1. A rapid data query method based on a federation chain is characterized by comprising the following steps:
s1, acquiring the authentication ID of the equipment in the inquiry alliance chain;
s2, confirming the layer number of the authentication ID in the levelDB corresponding to the alliance chain, and filtering the authentication ID by using an improved bloom filter, wherein the improved bloom filter positions a cache line by bit and operation when mapping and filtering equipment;
s3, using a block level index to take out the block information corresponding to the authentication ID from the level DB, wherein the block level index comprises the authentication ID and a unique block identifier, and the block information comprises a block height and a block address;
and S4, locating the authentication ID corresponding to the block in the alliance chain by using the block data B + tree index according to the block information obtained in the step S3, if the obtained authentication ID is consistent with the authentication ID in the step S1, determining that the equipment is trusted, and if not, determining that the equipment is untrusted and finishing the query.
2. The federation chain-based data rapid query method of claim 1, wherein the authentication ID is generated by a snowflake algorithm, and the authentication IDs correspond to the devices one to one.
3. The federation chain-based data rapid query method of claim 1, wherein the step S2 comprises:
s2.1, searching each layer of the levelDB corresponding to the alliance chain, and confirming the layer number of the authentication ID;
s2.2, positioning a cache line where the authentication ID is located from the layer number confirmed in the step S2.1 by using an improved bloom filter;
s2.3, dividing the authentication ID into k sections, and respectively calculating a substitute function of the hash function corresponding to the segmented authentication ID by using bitwise AND operation;
and S2.4, respectively hashing each authentication ID by using a substitute function of the hash function obtained in the step S2.3, confirming whether the data on the corresponding bit in the cache line obtained in the step S2.2 are all 1 according to a calculation result, if so, executing the step S3, and if not, judging that the query equipment is not credible and finishing the query.
4. A federation chain-based data fast query method according to claim 3, wherein in step S2.2, the formula of the cache line where the location authentication ID is located is:
j＝hash(d)&(2 n -1)；
in the formula 2 n M denotes the total number of lines of the cache line, d denotes the authentication ID, and j denotes the jth cache line.
5. A federation chain-based data fast-query method according to claim 3, wherein in step S2.3, the calculation formula of the substitute function of the hash function corresponding to the segmented authentication ID is:
Figure FDA0003068217430000011
in the formula, y s Alternative function to the hash function representing the authentication ID of the s-th segment, x s Represents the s-th authentication ID, s is more than or equal to 1 and less than or equal to k, k represents the total number of hash functions, t and i both represent variables, and i is more than or equal to 0 and less than or equal to i<k-1。
6. The federation chain-based data fast-query method of claim 1, wherein in step S3, the chunk level index has a key-value structure, which is an authentication ID and a chunk unique identifier, and the chunk unique identifier is a chunk height.
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Family Cites Families (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101637854B1 (en) * 2015-10-16 2016-07-08 주식회사 코인플러그 Certificate issuance system and method based on block chain, certificate authentication system and method based on block chain
CN106055679A (en) * 2016-06-02 2016-10-26 南京航空航天大学 Multi-level cache sensitive indexing method
CN107247773B (en) * 2017-06-07 2018-05-15 北京邮电大学 A kind of method that inquiry is traded in distributed data base based on block chain
US10565192B2 (en) * 2017-08-01 2020-02-18 International Business Machines Corporation Optimizing queries and other retrieve operations in a blockchain
CN108829772B (en) * 2018-05-29 2020-01-10 杭州趣链科技有限公司 Block chain block storage and reading method based on file
CN109918398A (en) * 2019-02-26 2019-06-21 符安文 A kind of data search classification method based on block chain
KR102544628B1 (en) * 2019-03-08 2023-06-19 한국전자통신연구원 System for a data sharing platform in a block chain based distributed data sharing environment, method for searching data index in the system and method for providing seartch index in the system
US10936507B2 (en) * 2019-03-28 2021-03-02 Intel Corporation System, apparatus and method for application specific address mapping
US11636109B2 (en) * 2019-04-04 2023-04-25 American Express Travel Related Services Company, Inc. Data processing in an optimized analytics environment
US11093495B2 (en) * 2019-06-25 2021-08-17 International Business Machines Corporation SQL processing engine for blockchain ledger
CN111143371B (en) * 2019-12-27 2023-09-19 中国银联股份有限公司 Data query method, device, equipment, system and medium
CN111611315B (en) * 2020-05-25 2023-11-10 辽宁大学 Financial big data-oriented multi-fork tree structure block chain integrated optimization storage method
CN111782707B (en) * 2020-06-19 2024-04-16 京东科技信息技术有限公司 Data query method and system
CN112131227A (en) * 2020-09-29 2020-12-25 深圳前海微众银行股份有限公司 Data query method and device based on alliance chain
CN112286963B (en) * 2020-11-17 2023-05-26 北京物资学院 Block chain terminal data credible query system and implementation method thereof
CN112702346A (en) * 2020-12-24 2021-04-23 国网浙江省电力有限公司电力科学研究院 Distributed identity authentication method and system based on alliance chain

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