CN109344212A - A kind of geographical big data of subject-oriented feature excavates the method and system of recommendation - Google Patents
A kind of geographical big data of subject-oriented feature excavates the method and system of recommendation Download PDFInfo
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Abstract
The present invention relates to geographical big data excavation applications, more particularly to a kind of geographical big data of subject-oriented feature excavates recommended method and system, the difference is that: the multi-threaded geographical big data of polymorphic type is carried out Text Feature Extraction to the method for the present invention and fragmentation processing forms the spatial entities with relating attribute, and assign corresponding subject identification, realize data mining, receive the spatial entities with subject categories mark of data mining unit output, it is associated retrieval in the database, search result is classified, it is analyzed and determined, result is exported again, recommend user；Present system includes database, data mining unit, data recommendation unit.The present invention realizes the quick excavation and parallel processing of geographical big data, and the accuracy of search and the recommendation of geographical big data is high, high-efficient.
Description
Technical field
The present invention relates to a kind of excavations of the geographical big data of geographical big data excavation applications more particularly to subject-oriented feature
Recommended method and system.
Background technique
Big data era, data mining are the work of most critical；Data mining is also known as the Knowledge Discovery in database, is mesh
The hot issue of preceding artificial intelligence and database area research, so-called data mining refers to disclose from the mass data of database
The non-trivial process of information that is implying out, not previously known and having potential value；Data mining is a kind of decision support processes,
It is based primarily upon artificial intelligence, machine learning, pattern-recognition, statistics, database, visualization technique etc., increasingly automatedly
The data for analyzing enterprise, make the reasoning of inductive, therefrom excavate potential mode, and aid decision making person adjusts market strategy,
Reduce risks, makes correct decision.
Data mining (Data Mining) is exactly from a large amount of, incomplete, noisy, fuzzy, random reality
Using in data, it is ignorant in advance but be the information of potentially useful and the mistake of knowledge that extraction lies in therein, people
Journey；This definition includes several layers meaning: data source must be true, a large amount of, Noise；It is found out that user's sense is emerging
The knowledge of interest；It was found that knowledge to be subjected to, be appreciated that, can use；It is not required for the knowledge for the four seas all standards that discovery is put, is only propped up
It holds and specifically finds the problem.The predecessor of data discovery is Knowledge Discovery (KDD), belongs to the scope of machine learning, and database
Develop the product combined with artificial intelligence technology；Knowledge Discovery is to identify effective, novel, potential to have from data set
, and the non-trivial process of final intelligible mode；Information is become knowledge by Knowledge Discovery, is found from data mine
The knowledge gold bullion contained will contribute for the development of knowledge innovation and kownledge economy；The purpose of Knowledge Discovery and Data Mining
It is that a large amount of initial data is converted into valuable knowledge, the information for being different from data base management system retrieval and inquiring,
It is to imply, refine and high-caliber by the knowledge that Data Mining is found；
Data recommendation is calculated excavated by mass data based on, according to the information requirement of user, recommend corresponding data for user
Result.
Mahout is a set of machine learning class libraries with extendible ability, and Mahout data mining is a kind of data mining
Library, using most common data mining algorithm in cluster, regression test and statistical modeling, and it is real using MapReduce model
Apply these algorithms；
Mahout there is presently provided the algorithm of 4 kinds of usage scenarios:
1) report that user's most probable is liked recommended engine algorithm: is calculated by the historical record of the usage behavior of analysis user
Announcement, spatial data, report related accessories；(it can be recommended when realization based on the recommendation of user by searching for similar user
Project) or based on report recommendation (calculate report between similarity and make recommendation)；
2) a series of relevant reports etc. clustering algorithm: are divided by group similar in correlation by analysis；It is real by cluster
Now to the subdivision of sample, so that more similar with the sample characteristics in group, the sample characteristics of difference group differ greatly；
3) sorting algorithm: by analyzing one group of categorized report, other reports of unassorted are returned by same rule
Enter corresponding classification；
4) a series of article groups often occurred together relative article parser: are identified；
Scene handled by Mahout algorithm, often along with the user of magnanimity use data the case where；By by Mahout
Algorithm is implemented on MapReduce frame, and the input, output and intermediate result of algorithm are implemented in HDFS distributed field system
On system so that Mahout have the characteristics that height handle up, high concurrent, high reliability；Finally, make geography information operation system can be with
Efficiently and rapidly obtain analysis result.
Not subdivisible minimum unit phenomenon is known as spatial entities in spatial entities GIS-Geographic Information System, and attribute is space reality
The defined feature of body (mean DBH increment of forest in such as size of population, forest land).
Geographical big data industry is in the ascendant in China, manages the value of big data to find concurrently to pick up, the present invention borrows
Above-mentioned technology is helped, the geographical big data for providing a kind of subject-oriented feature excavates recommended method and system.
Summary of the invention
The purpose of the present invention is to overcome the shortcomings of the existing technology, and the geographical big data for providing a kind of subject-oriented feature is dug
Recommended method and system are dug, realizes the quick excavation and parallel processing of geographical big data, and the search and recommendation of geographical big data
Accuracy it is high, high-efficient.
In order to solve the above technical problems, the technical solution of the present invention is as follows:
A kind of geographical big data excavation recommended method of subject-oriented feature, the difference is that, the method includes as follows
Step:
Step A: carrying out data mining to geographical big data, the specific steps are that:
Step A1: the multi-threaded geographical big data of polymorphic type is received, is classified according to spatial data and Non-spatial Data, text is carried out
It extracts and fragmentation processing, formation has the spatial entities of relating attribute；
Step A2: to spatial entities cluster calculation and assigning corresponding subject categories mark, complete the spatialization of Non-spatial Data,
Obtain geographical big data Result；
Step B: discriminatory analysis is carried out to the geographical big data Result that step A is obtained and recommends user, specific steps
Are as follows:
Step B1: the spatial entities with subject categories mark are inputted；
Step B2: in the database retrieval and the associated whole spatial informations of spatial entities and according to title, paragraph, table, figure into
Row classification, obtains sorted spatial information；
Step B3: successively judging whether there is spatial classification information in spatial entities, real to the space containing spatial classification information
Body is intercepted；
Step B4: extracting subject categories mark from the step A data mining results, by different themes classification logotype to intercepting out
As a result paragraph is screened, and the result as data recommendation is exported to user.
By above scheme, the step A1 specifically:
Step A11: geographical big data unit, Non-spatial Data and multi-threaded space including polymorphic type are obtained from database
Data；
Step A12: the crucial words and phrases that user has been constructed and arranged, search key sentence and extraction include to close in Non-spatial Data
The text information of keyword sentence；
Step A13: fragmentation processing is carried out to the text information comprising crucial words and phrases；
Step A14: Chinese word segmentation processing is carried out to text information using segmentation methods, obtains word segmentation result；
Step A15: comparing word segmentation result and be associated with the step A11 spatial data obtained, and being formed has relating attribute
Spatial entities.
By above scheme, the step A2 specifically:
Step A21: the feature of spatial entities is subjected to vector quantities operation, obtains vector；
Step A22: similarity calculation is carried out to vector；
Step A23: cluster calculation is carried out to the result of similarity calculation, forms new theme；
Step A24: assigning corresponding subject categories mark respectively to spatial entities, complete the spatialization of Non-spatial Data, as
The result output that geographical big data is excavated.
By above scheme, the spatial classification information in the step B2 includes title, paragraph, table, four category information of figure.
By above scheme, the step B3 specifically:
Step B31: judging whether to be heading message, and judging result is including being heading message, not being two kinds of heading message；
Step B32: judge whether containing crucial words and phrases；If without containing crucial words and phrases, return step B1；If containing crucial words and phrases,
It performs the next step；
Step B33: if containing crucial words and phrases while title, then to being title and data containing crucial words and phrases intercept
The operation of text paragraph under title；If not contain crucial words and phrases while title, then to not being title and contain keyword
The data of sentence intercept the operation of upper and lower paragraph.
By above scheme, the step B4 specifically: in extraction step A subject categories mark, by different themes to section
It takes paragraph to be screened, filters out result paragraph, paragraph content is classified, sorted paragraph is defeated as the result of data recommendation
Out to user.
By above scheme, the step A23 carries out similarity calculation using cosine similarity method.
A kind of geographical big data excavation recommender system of subject-oriented feature, the difference is that, the system comprises:
Database, for accommodating the multi-threaded geographical big data of polymorphic type；Data mining unit, for receiving the multi-threaded ground of polymorphic type
Big data is managed, is classified according to spatial data and Non-spatial Data, carries out Text Feature Extraction and fragmentation processing, being formed, there is association to belong to
Property spatial entities, and to spatial entities cluster calculation and assign corresponding subject categories and identify, complete the sky of Non-spatial Data
Between change, obtain geographical big data Result；Data recommendation unit, that excavates unit output for receiving data has theme class
The spatial entities not identified are associated retrieval in the database, search result are classified, analyzed and determined, then by result
Output, recommends user.
By above scheme, the multi-threaded geographical big data of polymorphic type is carried out Text Feature Extraction to the present invention and fragmentation handles shape
At the spatial entities with relating attribute, and corresponding subject identification is assigned, data mining and spatialization is realized, then to process
The geographical long data block of data discovery and spatialization is classified, and successively judges whether each data block meets recommendation,
Meet recommendation recommend, recommend again after carrying out cluster calculation to the data block for not meeting recommendation；This hair
Bright excavation and recommendation to associated data, carries out that geographical big data is recommended, cluster etc. calculates, efficiently managed for geographical big data and
Analysis processing provides technological means, the quick excavation and parallel processing of geographical big data is realized, for the efficient pipe of geographical big data
It manages and using technical foundation is provided, increases substantially the accuracy of search and the recommendation of geographical big data, there is high reliability, height
The advantages of efficiency.
Detailed description of the invention
Fig. 1 is the structural schematic diagram that geography of embodiment of the present invention big data excavates recommender system；
Fig. 2 is that geography of embodiment of the present invention big data excavates data mining flow chart of steps in recommended method；
Fig. 3 is that geography of embodiment of the present invention big data excavates data recommendation flow chart of steps in recommended method；
Wherein: 1- database, 2- data mining unit, 3- data recommendation unit.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawing and specific implementation
Invention is further described in detail for example.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention,
It is not intended to limit the present invention.
Hereinafter, many aspects of the invention will be more fully understood with reference to attached drawing.Component in attached drawing may not be according to
Ratio is drawn.Alternatively, it is preferred that emphasis is clearly demonstrate component of the invention.In addition, in several views in the accompanying drawings, it is identical
Appended drawing reference indicate corresponding part.
Word " exemplary " as used herein or " illustrative " expression are used as example, example or explanation.It retouches herein
Stating any embodiment for " exemplary " or " illustrative " to be not necessarily to be construed as is preferred relative to other embodiment or has
Benefit.All embodiments described below are illustrative embodiments, and providing these illustrative embodiments is to make
Those skilled in the art are obtained to make and use embodiment of the disclosure and be expected to be not intended to limit the scope of the present disclosure, the disclosure
Range is defined by the claims.In other embodiments, well known feature and method is described in detail so as not to obscure this
Invention.For purpose described herein, term " on ", "lower", "left", "right", "front", "rear", "vertical", "horizontal" and its spread out
New word is related by the invention oriented with such as Fig. 1.Moreover, having no intent to by technical field, background technique, summary of the invention above
Or any theoretical limitation expressed or implied provided in detailed description below.It should also be clear that being shown in the accompanying drawings and below
Specification described in specific device and process be the inventive concept limited in the following claims simple examples it is real
Apply example.Therefore, specific size relevant to presently disclosed embodiment and other physical features are understood not to restricted
, unless claims are separately clearly stated.
Referring to FIG. 1, the geographical big data that present system is a kind of subject-oriented feature excavates recommender system comprising
Database 1, data mining unit 2 and data recommendation unit 3, in which: it is big that database 1 is used to accommodate the multi-threaded geography of polymorphic type
Data；Data mining unit 2 receives the big number of the multi-threaded geography of polymorphic type for receiving the multi-threaded geographical big data of polymorphic type
According to, classify according to spatial data and Non-spatial Data, carries out Text Feature Extraction and fragmentation is handled, sky of the formation with relating attribute
Between entity, and to spatial entities cluster calculation and assign corresponding subject categories and identify, complete the spatialization of Non-spatial Data, obtain
To geographical big data Result；Data recommendation unit 3 excavates identifying with subject categories for unit output for receiving data
Spatial entities, be associated with, analyzed and determined with the spatial information in database, then result is exported, recommend user.
It please refers to Fig. 1 and combines Fig. 2, Fig. 3, the method for the present invention is that a kind of geographical big data excavation of subject-oriented feature pushes away
Recommend method, this method step are as follows:
Step A: data mining unit 2 carries out data mining to the geographical big data in database 1, the specific steps are that:
Step A1: the multi-threaded geographical big data of polymorphic type is received, is classified according to spatial data and Non-spatial Data, text is carried out
It extracts and fragmentation processing, formation has the spatial entities of relating attribute；
Step A2: to spatial entities cluster calculation and assigning corresponding subject categories mark, complete the spatialization of Non-spatial Data,
Obtain geographical big data Result；
Step B: spatial information in geographical big data Result that data recommendation unit 3 obtains step A and database into
Row associative search carries out discriminatory analysis to all kinds of search results and recommends user, the specific steps are that:
Step B1: the spatial entities with subject categories mark are inputted；
Step B2: in the database retrieval and the associated whole spatial informations of spatial entities and according to title, paragraph, table, figure into
Row classification, obtains sorted spatial information；
Step B3: successively judging whether there is spatial classification information in spatial entities, real to the space containing spatial classification information
Body is intercepted；
Step B4: extracting subject categories mark from the step A data mining results, by different themes classification logotype to intercepting out
As a result paragraph is screened, and the result as data recommendation is exported to user.
It is below a specific embodiment of the method for the present invention；
Step A: carrying out data mining to geographical big data, the specific steps are that:
Step A11: it obtains geographical big data: obtaining geographical big data unit, the non-space number including polymorphic type from database
According to and multi-threaded spatial data；
Step A12: extract text information: user constructs specific crucial words and phrases, for non-according to previous working experience
It is scanned in spatial data unit, the spatial information for finding out user's needs is being obtained according to the good crucial words and phrases of user setting
To Non-spatial Data unit in search for these crucial words and phrases, extract these text informations, input step step A13；
Step A13: fragmentation processing, input step A14 fragmentation processing: are carried out to the text information comprising crucial words and phrases；
Step A14: word segmentation processing: Chinese word segmentation processing is carried out to text information using segmentation methods, word segmentation result is obtained, inputs
Step A15；
Step A15: spatial contrast and association process: word segmentation result is compared and is closed with the step A11 spatial data obtained
Connection forms the spatial entities with relating attribute；
Step A21: vectorization: carrying out vector quantities operation for the feature of the obtained spatial entities with relating attribute of step A15,
Obtain vector, input step A22；
Step A22: similarity calculation: similarity calculation is carried out to vector, is mainly carried out using cosine similarity method similar
Degree calculates, by similarity calculation result input step A23；
Step A23: cluster: cluster calculation is carried out to the result of similarity calculation, forms new theme；
Step A24: spatial entities result output: are assigned with corresponding subject categories respectively according to the new theme that step A23 is obtained
Mark, completes the spatialization of Non-spatial Data, the result output excavated as geographical big data；
Step B: being associated retrieval for geographical big data Result and the spatial information in database that step A is obtained, right
All kinds of search results carry out discriminatory analysis and recommend user, the specific steps are that:
Step B1: it obtains spatial information unit: obtaining geographical big data Result, i.e., with all empty of subject categories mark
Between entity, input step B2；
Step B2: classification obtains: retrieval and the associated whole spatial informations of spatial entities in the database, and according to title, section
Fall, table, figure are classified, by sorted spatial information input step B31；
Step B31: judging whether to be heading message, and judging result judges to tie including being heading message, not being two kinds of heading message
B32 is entered step after beam；
Step B32: judged according to previous step B31 is heading message, be not two kinds of heading message as a result, respectively again into
Whether row contains the judgement of crucial words and phrases；If completing this time to judge, jumping back to step B1, carry out down without containing crucial words and phrases
The acquisition and judgement of one spatial information unit；If executing step B33 containing crucial words and phrases；
Step B33: if containing crucial words and phrases while title, then to previous step conveying come be title and containing key
The data of words and phrases carry out the operation of the text paragraph under interception title；If not containing crucial words and phrases while title, then to upper
What the conveying of one step came is not the operation that title and the data containing crucial words and phrases intercept upper and lower paragraph；
Step B4: from subject categories mark is extracted in step A in data mining results, interception paragraph is sieved by different themes
Choosing, filters out result paragraph, paragraph content is classified, sorted paragraph is exported as the result of data recommendation to user.
The geographical big data progress text that the data of above-mentioned subject-oriented feature are found and spatialization is multi-threaded by polymorphic type
It extracts and fragmentation is handled, including spatial data and Non-spatial Data, form the spatial entities with relating attribute, and assign phase
The mark answered realizes data discovery and spatialization；Above-mentioned geography big data is excavated and calculating is recommended to refer to and finds to by data
And the geographical long data block of spatialization is classified, and successively judges whether each data block meets recommendation, meets recommendation
Content recommend, and recommends again after carrying out cluster calculation to the data block for not meeting recommendation.
Excavation recommended method of the invention be based on Mahout big data technology, by with Apache Hadoop distribution frame
Frame combines, and distributed system is efficiently used to realize the high-performance calculation of geographical big data management system, to geographical big number
It according to sufficiently being analyzed, excavates and extracts related information useful in geographical big data, show user by related rule, realize root
It is associated the excavation and recommendation of data according to features such as position, contents, carries out geographical big data recommendation, cluster etc. and calculates, for ground
Reason big data efficiently manages and analyzes processing and provides technological means, realizes some expansible machine learning field classic algorithms,
It can be convenient and quickly create intelligent application and have many advantages, such as high reliability, high efficiency；Further, present system
Realize the quick excavation and parallel processing of geographical big data, spatial information knowledge needed for therefrom obtaining user simultaneously carries out result
Spatialization provides decision support for the spatial information integrated application based on geographical big data Environmental support；Geographical big data is excavated
Recommended method can increase substantially searching for geographical big data for the efficient management of geographical big data and using technical foundation is provided
The accuracy of rope and recommendation further promotes the ability of geographical big data socialized service system, opens up the new way of spatial data services
Diameter improves the utilization rate of the geographical big data of distributed storage in the whole country.
The above content is specific embodiment is combined, further detailed description of the invention, and it cannot be said that this hair
Bright specific implementation is only limited to these instructions.For those of ordinary skill in the art to which the present invention belongs, it is not taking off
Under the premise of from present inventive concept, a number of simple deductions or replacements can also be made, all shall be regarded as belonging to protection of the invention
Range.
Claims (7)
1. a kind of geographical big data of subject-oriented feature excavates recommended method, it is characterised in that: the method includes walking as follows
It is rapid:
Step A: carrying out data mining to geographical big data, the specific steps are that:
Step A1: the multi-threaded geographical big data of polymorphic type is received, is classified according to spatial data and Non-spatial Data, text is carried out
It extracts and fragmentation processing, formation has the spatial entities of relating attribute；
Step A2: to spatial entities cluster calculation and assigning corresponding subject categories mark, complete the spatialization of Non-spatial Data,
Obtain geographical big data Result；
Step B: discriminatory analysis is carried out to the geographical big data Result that step A is obtained and recommends user, specific steps
Are as follows:
Step B1: the spatial entities with subject categories mark are inputted；
Step B2: in the database retrieval and the associated whole spatial informations of spatial entities and according to title, paragraph, table, figure into
Row classification, obtains sorted spatial information；
Step B3: successively judging whether there is spatial classification information in spatial entities, real to the space containing spatial classification information
Body is intercepted；
Step B4: extracting subject categories mark from the step A data mining results, by different themes classification logotype to intercepting out
As a result paragraph is screened, and the result as data recommendation is exported to user.
2. the geographical big data of subject-oriented feature according to claim 1 excavates recommended method, it is characterised in that: described
Step A1 specifically:
Step A11: geographical big data unit, Non-spatial Data and multi-threaded space including polymorphic type are obtained from database
Data；
Step A12: the crucial words and phrases that user has been constructed and arranged, search key sentence and extraction include to close in Non-spatial Data
The text information of keyword sentence；
Step A13: fragmentation processing is carried out to the text information comprising crucial words and phrases；
Step A14: Chinese word segmentation processing is carried out to text information using segmentation methods, obtains word segmentation result；
Step A15: comparing word segmentation result and be associated with the step A11 spatial data obtained, and being formed has relating attribute
Spatial entities.
3. the geographical big data of subject-oriented feature according to claim 2 excavates recommended method, it is characterised in that: described
Step A2 specifically:
Step A21: the feature of spatial entities is subjected to vector quantities operation, obtains vector；
Step A22: similarity calculation is carried out to vector；
Step A23: cluster calculation is carried out to the result of similarity calculation, forms new theme；
Step A24: assigning corresponding subject categories mark respectively to spatial entities, complete the spatialization of Non-spatial Data, as
The result output that geographical big data is excavated.
4. the geographical big data of subject-oriented feature according to claim 1 excavates recommended method, it is characterised in that: described
Step B3 specifically:
Step B31: judging whether to be heading message, and judging result is including being heading message, not being two kinds of heading message；
Step B32: judge whether containing crucial words and phrases；If without containing crucial words and phrases, return step B1；If containing crucial words and phrases,
It performs the next step；
Step B33: if containing crucial words and phrases while title, then to being title and data containing crucial words and phrases intercept
The operation of text paragraph under title；If not contain crucial words and phrases while title, then to not being title and contain keyword
The data of sentence intercept the operation of upper and lower paragraph.
5. the geographical big data of subject-oriented feature according to claim 4 excavates recommended method, it is characterised in that: described
Step B4 specifically: the subject categories mark in extraction step A screens interception paragraph by different themes, filters out knot
Fruit paragraph, paragraph content is classified, and sorted paragraph is exported as the result of data recommendation to user.
6. the geographical big data of subject-oriented feature according to claim 3 excavates recommended method, it is characterised in that: described
Step A23 carries out similarity calculation using cosine similarity method.
7. a kind of geographical big data of subject-oriented feature excavates recommender system, which is characterized in that the system comprises:
Database, for accommodating the multi-threaded geographical big data of polymorphic type；
Data mining unit receives the big number of the multi-threaded geography of polymorphic type for receiving the multi-threaded geographical big data of polymorphic type
According to, classify according to spatial data and Non-spatial Data, carries out Text Feature Extraction and fragmentation is handled, sky of the formation with relating attribute
Between entity, and to spatial entities cluster calculation and assign corresponding subject categories and identify, complete the spatialization of Non-spatial Data, obtain
To geographical big data Result；
Data recommendation unit excavates the spatial entities with subject categories mark of unit output, with data for receiving data
Spatial information association in library, is analyzed and determined, then result is exported, recommends user.
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| CN110096529A (en) * | 2019-04-16 | 2019-08-06 | 中科金联（北京）科技有限公司 | Network data mining method and system based on multidimensional vector data |
| CN110096529B (en) * | 2019-04-16 | 2021-07-16 | 中科金联（北京）科技有限公司 | Network data mining method and system based on multidimensional vector data |
| CN112860759A (en) * | 2021-01-26 | 2021-05-28 | 周应凤 | Big data mining method based on block chain security authentication and cloud authentication service system |
| CN113515558A (en) * | 2021-06-29 | 2021-10-19 | 上海硕恩网络科技股份有限公司 | Data mining method based on reasoning technology |
| CN113515558B (en) * | 2021-06-29 | 2024-03-22 | 上海硕恩网络科技股份有限公司 | Data mining method based on reasoning technology |
| CN113449195A (en) * | 2021-07-15 | 2021-09-28 | 安徽商信政通信息技术股份有限公司 | Intelligent knowledge pushing method and system |
| CN113449195B (en) * | 2021-07-15 | 2023-09-19 | 安徽商信政通信息技术股份有限公司 | Intelligent knowledge pushing method and system |
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