CN105574113B - A kind of data managing method under big data environment - Google Patents

A kind of data managing method under big data environment Download PDF

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CN105574113B
CN105574113B CN201510927508.0A CN201510927508A CN105574113B CN 105574113 B CN105574113 B CN 105574113B CN 201510927508 A CN201510927508 A CN 201510927508A CN 105574113 B CN105574113 B CN 105574113B
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big data
data resource
user
resource
dimension code
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CN105574113A (en
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许驰
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Shandong Hanxin Technology Co ltd
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Shandong Kingsgarden Technology 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
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/335—Filtering based on additional data, e.g. user or group profiles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/18—File system types
    • G06F16/182—Distributed file systems
    • G06F16/1824—Distributed file systems implemented using Network-attached Storage [NAS] architecture
    • G06F16/183—Provision of network file services by network file servers, e.g. by using NFS, CIFS
    • 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
    • G06F16/23—Updating
    • G06F16/2365—Ensuring data consistency and integrity
    • 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
    • G06F16/24534—Query rewriting; Transformation
    • G06F16/24537—Query rewriting; Transformation of operators
    • 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
    • G06F16/2462—Approximate or statistical queries
    • 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
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • 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
    • G06F21/10—Protecting distributed programs or content, e.g. vending or licensing of copyrighted material ; Digital rights management [DRM]
    • G06F21/16—Program or content traceability, e.g. by watermarking
    • 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
    • G06F21/60—Protecting data
    • G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures

Abstract

The step of present invention provides the data managing method under a kind of big data environment, this method is as follows: the big data that Analysis server statisticallys analyze each user in network accesses historical record, generates user feature database；The resource acquisition request of user is sent to big data Resource Server by user terminal；Big data Resource Server is requested according to the resource acquisition of user, obtains the set of one group of big data resource；And the set of this group of big data resource is sent to Analysis server；Analysis server filters out the highest big data resource of matching degree according to user feature database in the set of this group of big data resource；Big data resource after screening is sent to safety filtering server；And update the user feature database；Safety filtering server carries out safety filtering to the big data resource after the screening, and the filtered big data resource of safety is sent to the user terminal.

Description

A kind of data managing method under big data environment
Technical field
The present invention relates to the data managing methods under big data field more particularly to a kind of big data environment.
Background technique
Big data is a kind of strategic resource, and the data management under optimization big data environment can bring huge for enterprise etc. Economic benefit.After obtaining big data resource, how aggregation of data analysis, Yi Jian are carried out to the big data resource of acquisition Full property filtering is all currently to face a major issue, and current project urgently to be solved.
Summary of the invention
The present invention provides the data managing method under a kind of big data environment.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of data managing method under big data environment, which is characterized in that
Step 1, the big data of each user accesses historical record in Analysis server statistical analysis network, and it is special to generate user Levy database；
Step 2, the resource acquisition request of user is sent to big data Resource Server by user terminal；
Step 3, big data Resource Server is requested according to the resource acquisition of user, obtains the collection of one group of big data resource It closes；And the set of this group of big data resource is sent to Analysis server；
Step 4, Analysis server filters out matching according to user feature database in the set of this group of big data resource Spend highest big data resource；Big data resource after screening is sent to safety filtering server；And it is special to update the user Levy database；
Step 5, safety filtering server carries out safety filtering to the big data resource after the screening, and by safety Filtered big data resource is sent to the user terminal.
Preferably, the big data access historical record of each user includes user's in the network in the step 1 History resource acquisition request and its corresponding big data resource；And in step 1, the Analysis Service implement body executes following Step:
Step 1.1, it analyzes in preset statistical time section, in network in the big data access historical record of each user All history resource acquisition requests, extract all keywords；
Step 1.2, it counts in preset statistical time section, in network in the big data access historical record of each user The frequency of occurrence of each keyword in big data resource, using the frequency of occurrence of each keyword as the weighted value of the keyword；
Step 1.3, for each user, constitute<user name, keyword, keyword weighted value>structure triple, By the triple store into the user feature database.
Preferably, big data Resource Server executes following operation in the step 3:
Step 3.1, big data Resource Server extracts several keywords in the resource acquisition request of user；
Step 3.2, several keywords of extraction are extended, obtain N number of expanded keyword；
Step 3.3, it is retrieved using N number of expanded keyword, obtains the set of one group of big data resource.
Preferably, Analysis server executes following operation in the step 4:
Step 4.1, it according to the user name for the user for proposing resource acquisition request and the N number of expanded keyword obtained, looks into The user feature database is ask, the weighted value of each expanded keyword is successively obtained, the weighted value is constituted into a N-dimensional Reference vector<K1, K2 ..., KN>；
Step 4.2, following operation is executed for each big data resource in the set of one group of big data resource of acquisition:
The frequency of occurrence of each expanded keyword in this big data resource is counted as weighted value, by the weighted value structure At the comparison vector of a N-dimensional；
Where it is assumed that sharing M big data resources, then i-th big data resource structure in the set of the big data resource At N-dimensional compare vector be<ki1, ki2 ..., kiN>, i=1,2 ..., M；
Step 4.3, the N-dimensional for calculating separately each big data resource compares between vector and the reference vector of the N-dimensional The highest N-dimensional of cosine similarity value is compared a big data resource corresponding to vector and is determined as matching by cosine similarity value Spend highest big data resource.
Preferably, in the step 5, described that the filtered big data resource of safety is sent to user terminal is specific The following steps are included:
The safety filtering server executes following operation:
Step 5a.1, by the data block for the prescribed form that the big data division of resources is regular length；
Step 5a.2 will be converted to two-dimension code image after the encryption of blocks of data of each prescribed form；
Step 5a.3 is embedded in digital watermark information in each two-dimension code image；
The two-dimension code image for being embedded in digital watermark information is successively sent to the user terminal by step 5a.4；
The user terminal executes following operation:
Step 5b.1 receives the two-dimension code image of insertion digital watermark information, and by all two-dimension code images by regulation Sequence arrangement；
Step 5b.2 extracts the digital watermark information in each two-dimension code image, verifies the integrality of the two-dimension code image；
If there is not by the two-dimension code image of integrity verification, then do not pass through the two dimensional code of integrity verification for described Picture filters out, and executes step 5b.3；
If there is no by the two-dimension code image of integrity verification, not thening follow the steps 5b.4；
Step 5b.3 requests the safety filtering server to retransmit all two dimensional code figures for not passing through integrity verification Piece, until obtaining all two-dimension code images for passing through integrity verification；
Step 5b.4 is converted to the number of prescribed form after decrypting by all two-dimension code images of the integrity verification According to block；
Step 5b.5 is combined the data block of all prescribed forms by defined sequence, obtains the big data resource.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention is carried out below further It is described in detail.It should be appreciated that described herein, specific examples are only used to explain the present invention, is not intended to limit the present invention.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of data managing method under big data environment, which is characterized in that
Step 1, the big data of each user accesses historical record in Analysis server statistical analysis network, and it is special to generate user Levy database；
Step 2, the resource acquisition request of user is sent to big data Resource Server by user terminal；
Step 3, big data Resource Server is requested according to the resource acquisition of user, obtains the collection of one group of big data resource It closes；And the set of this group of big data resource is sent to Analysis server；
Step 4, Analysis server filters out matching according to user feature database in the set of this group of big data resource Spend highest big data resource；Big data resource after screening is sent to safety filtering server；And it is special to update the user Levy database；
Step 5, safety filtering server carries out safety filtering to the big data resource after the screening, and by safety Filtered big data resource is sent to the user terminal.
Preferably, the big data access historical record of each user includes user's in the network in the step 1 History resource acquisition request and its corresponding big data resource；And in step 1, the Analysis Service implement body executes following Step:
Step 1.1, it analyzes in preset statistical time section, in network in the big data access historical record of each user All history resource acquisition requests, extract all keywords；
Step 1.2, it counts in preset statistical time section, in network in the big data access historical record of each user The frequency of occurrence of each keyword in big data resource, using the frequency of occurrence of each keyword as the weighted value of the keyword；
Step 1.3, for each user, constitute<user name, keyword, keyword weighted value>structure triple, By the triple store into the user feature database.
Preferably, big data Resource Server executes following operation in the step 3:
Step 3.1, big data Resource Server extracts several keywords in the resource acquisition request of user；
Step 3.2, several keywords of extraction are extended, obtain N number of expanded keyword；
Step 3.3, it is retrieved using N number of expanded keyword, obtains the set of one group of big data resource.
Preferably, Analysis server executes following operation in the step 4:
Step 4.1, it according to the user name for the user for proposing resource acquisition request and the N number of expanded keyword obtained, looks into The user feature database is ask, the weighted value of each expanded keyword is successively obtained, the weighted value is constituted into a N-dimensional Reference vector<K1, K2 ..., KN>；
Step 4.2, following operation is executed for each big data resource in the set of one group of big data resource of acquisition:
The frequency of occurrence of each expanded keyword in this big data resource is counted as weighted value, by the weighted value structure At the comparison vector of a N-dimensional；
Where it is assumed that sharing M big data resources, then i-th big data resource structure in the set of the big data resource At N-dimensional compare vector be<ki1, ki2 ..., kiN>, i=1,2 ..., M；
Step 4.3, the N-dimensional for calculating separately each big data resource compares between vector and the reference vector of the N-dimensional The highest N-dimensional of cosine similarity value is compared a big data resource corresponding to vector and is determined as matching by cosine similarity value Spend highest big data resource.
Preferably, in the step 5, described that the filtered big data resource of safety is sent to user terminal is specific The following steps are included:
The safety filtering server executes following operation:
Step 5a.1, by the data block for the prescribed form that the big data division of resources is regular length；
Step 5a.2 will be converted to two-dimension code image after the encryption of blocks of data of each prescribed form；
Step 5a.3 is embedded in digital watermark information in each two-dimension code image；
The two-dimension code image for being embedded in digital watermark information is successively sent to the user terminal by step 5a.4；
The user terminal executes following operation:
Step 5b.1 receives the two-dimension code image of insertion digital watermark information, and by all two-dimension code images by regulation Sequence arrangement；
Step 5b.2 extracts the digital watermark information in each two-dimension code image, verifies the integrality of the two-dimension code image；
If there is not by the two-dimension code image of integrity verification, then do not pass through the two dimensional code of integrity verification for described Picture filters out, and executes step 5b.3；
If there is no by the two-dimension code image of integrity verification, not thening follow the steps 5b.4；
Step 5b.3 requests the safety filtering server to retransmit all two dimensional code figures for not passing through integrity verification Piece, until obtaining all two-dimension code images for passing through integrity verification；
Step 5b.4 is converted to the number of prescribed form after decrypting by all two-dimension code images of the integrity verification According to block；
Step 5b.5 is combined the data block of all prescribed forms by defined sequence, obtains the big data resource.
It should be understood that for those of ordinary skills, it can be modified or changed according to the above description, And all these modifications and variations should all belong to the protection domain of appended claims of the present invention.

Claims (1)

1. the data managing method under a kind of big data environment, which is characterized in that
Step 1, the big data of each user accesses historical record in Analysis server statistical analysis network, generates user characteristics number According to library；
Step 2, the resource acquisition request of user is sent to big data Resource Server by user terminal；
Step 3, big data Resource Server is requested according to the resource acquisition of user, obtains the set of one group of big data resource；And The set of this group of big data resource is sent to Analysis server；
Step 4, Analysis server filters out matching degree most according to user feature database in the set of this group of big data resource High big data resource；Big data resource after screening is sent to safety filtering server；And update the user characteristics number According to library；
Step 5, safety filtering server carries out safety filtering to the big data resource after the screening, and safety is filtered Big data resource afterwards is sent to the user terminal；
In the step 1, the big data access historical record of each user includes the history resource acquisition of user in the network Request and its corresponding big data resource；And in step 1, the Analysis Service implement body executes following steps:
Step 1.1, it analyzes in preset statistical time section, it is all in the big data access historical record of each user in network History resource acquisition request, extracts all keywords；
Step 1.2, it counts in preset statistical time section, the big number in network in the big data access historical record of each user According to the frequency of occurrence of keyword each in resource, using the frequency of occurrence of each keyword as the weighted value of the keyword；
Step 1.3, for each user, constitute<user name, keyword, keyword weighted value>structure triple, by institute Triple store is stated into the user feature database；
In the step 3, big data Resource Server executes following operation:
Step 3.1, big data Resource Server extracts several keywords in the resource acquisition request of user；
Step 3.2, several keywords of extraction are extended, obtain N number of expanded keyword；
Step 3.3, it is retrieved using N number of expanded keyword, obtains the set of one group of big data resource；
In the step 4, Analysis server executes following operation:
Step 4.1, according to the user name for the user for proposing resource acquisition request and the N number of expanded keyword obtained, institute is inquired User feature database is stated, the weighted value of each expanded keyword is successively obtained, the weighted value is constituted to the reference of a N-dimensional Vector<K1, K2 ..., KN>；
Step 4.2, following operation is executed for each big data resource in the set of one group of big data resource of acquisition:
The frequency of occurrence of each expanded keyword in this big data resource is counted as weighted value, the weighted value is constituted one The comparison vector of a N-dimensional；
Where it is assumed that sharing M big data resources, the then N of i-th big data resource composition in the set of the big data resource Dimension compare vector be<ki1, ki2 ..., kiN>, i=1,2 ..., M；
Step 4.3, the cosine between the N-dimensional comparison vector of each big data resource and the reference vector of the N-dimensional is calculated separately The highest N-dimensional of cosine similarity value is compared a big data resource corresponding to vector and is determined as matching degree most by similarity value High big data resource；
In the step 5, it is described the filtered big data resource of safety is sent to user terminal specifically includes the following steps:
The safety filtering server executes following operation:
Step 5a.1, by the data block for the prescribed form that the big data division of resources is regular length；
Step 5a.2 will be converted to two-dimension code image after the encryption of blocks of data of each prescribed form；
Step 5a.3 is embedded in digital watermark information in each two-dimension code image；
The two-dimension code image for being embedded in digital watermark information is successively sent to the user terminal by step 5a.4；
The user terminal executes following operation:
Step 5b.1 receives the two-dimension code image of insertion digital watermark information, and by all two-dimension code images by defined suitable Sequence arrangement；
Step 5b.2 extracts the digital watermark information in each two-dimension code image, verifies the integrality of the two-dimension code image；
If there is not by the two-dimension code image of integrity verification, then do not pass through the two-dimension code image of integrity verification for described It filters out, executes step 5b.3；
If there is no by the two-dimension code image of integrity verification, not thening follow the steps 5b.4；
Step 5b.3 requests the safety filtering server to retransmit all not by the two-dimension code image of integrity verification, Until obtaining all two-dimension code images for passing through integrity verification；
Step 5b.4 is converted to the data of prescribed form after decrypting by all two-dimension code images of the integrity verification Block；
Step 5b.5 is combined the data block of all prescribed forms by defined sequence, obtains the big data resource.
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CN109669978B (en) * 2018-12-13 2021-02-19 中国联合网络通信集团有限公司 Data resource service generation method, device and system
CN110032680A (en) * 2019-04-16 2019-07-19 北京网聘咨询有限公司 Big data analysis method and system
CN112258009B (en) * 2020-06-12 2021-10-26 新疆新创高科企业管理有限公司 Intelligent government affair request processing method
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