CN105808745A - Data retrieval method and equipment - Google Patents
Data retrieval method and equipment Download PDFInfo
- Publication number
- CN105808745A CN105808745A CN201610141677.6A CN201610141677A CN105808745A CN 105808745 A CN105808745 A CN 105808745A CN 201610141677 A CN201610141677 A CN 201610141677A CN 105808745 A CN105808745 A CN 105808745A
- Authority
- CN
- China
- Prior art keywords
- keyword
- retrieved
- back end
- key assignments
- mark
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- 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
- G06F16/951—Indexing; Web crawling techniques
-
- 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
- G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The invention provides a data retrieval method and equipment. The data retrieval method comprises the following steps: building a data structure including a plurality of multilayer-nested data nodes; building an association relationship among different layers of data nodes; determining the data nodes in the data structure according to a key value identifier of a keyword to be retrieved, or positioning the data nodes layer by layer according to the association relationship among the different layers of data nodes in accordance with a sequence of keywords when a plurality of keywords are to be retrieved; and acquiring corresponding URLs (Uniform Resource Locators) or picture data. Through adoption of the data retrieval method, the complex problems of upward association retrieval and downward association retrieval are solved in a way of building the association relationship among the keywords through a simple data structure and a gene principle; bidirectional association retrieval can be realized through one retrieval server; the retrieval efficiency is increased; and efficient retrieval can be realized without support of a huge amount of hardware.
Description
Technical field
The present invention relates to database technical field, be specifically related to a kind of data retrieval method and equipment.
Background technology
Traditional search engine based on key search is normally based on what the mode of key word index file carried out retrieving, namely keyword and webpage URL (UniformResourceLocator are set up, URL) between corresponding relation, corresponding relation between each keyword and webpage URL is generated in the form of a list huge key word index file, and set up search engine key word data base according to key word index file, by the webpage URL that query search engine key data library searching is relevant to keyword.
Traditional storage system adopts the non-relational database such as GFS, RCF, BigTable to realize mostly, system is stored for Bigtable, Bigtable is that one sparse, distributed, the multidimensional mapping table of persistent storage, and the index of table is row keyword, row keyword and timestamp.In Bigtable, the list item of storage is all without the byte arrays resolved, its data model is as follows: (row:string, column:string, time:int64)-> string, this data model selected, is determined after having carefully analyzed the variety of applications of Bigtable system.Such as one the table Webtable storing a large amount of webpage and relevant information thereof, Webtable uses URL as row keyword, using some attribute of webpage as row name, the content of webpage is stored in contents row, and uses the timestamp obtaining this webpage to identify the different editions of same webpage.
Owing to keyword each in traditional data-storage system is independent toward each other, between onrelevant relation, rely only on large number of distributed search server to improve recall precision.
Summary of the invention
The embodiment of the present invention provides a kind of data retrieval method and equipment, the problem that the quantity in order to solve the retrieval server of existing data retrieval system cannot be taken into account with recall precision.
To achieve these goals, the embodiment of the present invention adopts techniques below means:
The embodiment of the present invention provides a kind of data retrieval method, data structure includes the back end of multiple multilayer nest, the back end of this layer associates with the back end of the back end on upper strata and/or lower floor, associates uniform resource position mark URL or the picture of same keyword with each back end of layer；Said method comprising the steps of:
Determine the key assignments mark of keyword to be retrieved；
Key assignments mark according to keyword to be retrieved, it is determined that the back end in data structure；Wherein, when keyword to be retrieved is multiple, determine the back end that first keyword to be retrieved is corresponding, and in the upper layer data node associated with described back end and/or lower data range of nodes, from second keyword to be retrieved, according to the order of keyword to be retrieved, identify according to the key assignments of keyword to be retrieved and determine back end；
Obtain URL or the picture of the back end association determined.
The embodiment of the present invention also provides for a kind of data retrieval server, including: processing module, memory module and retrieval module；
Memory module is used for storing data, data structure includes the back end of multiple multilayer nest, the back end of this layer associates with the back end of the back end on upper strata and/or lower floor, associates uniform resource position mark URL or the picture of same keyword with each back end of layer；
Processing module is used for, it is determined that the key assignments mark of keyword to be retrieved；
Retrieval module is used for, and identifies according to the key assignments of keyword to be retrieved, it is determined that the back end in data structure；Wherein, when keyword to be retrieved is multiple, determine the back end that first keyword to be retrieved is corresponding, and in the upper layer data node associated with described back end and/or lower data range of nodes, from second keyword to be retrieved, according to the order of keyword to be retrieved, identify according to the key assignments of keyword to be retrieved and determine back end；And, obtain URL or the picture of the back end association determined.
Compared with prior art, the above embodiment of the present invention has following Advantageous Effects:
The data retrieval scheme that the embodiment of the present invention provides, by setting up the data structure of the multiple back end including multilayer nest, incidence relation is set up between each layer data node, key assignments mark according to keyword to be retrieved determines the back end in data structure, when retrieving multiple keyword, according to the order of keyword, successively position back end according to the incidence relation between each layer data node, thus obtaining corresponding URL or image data；The present invention utilizes simple data structure and gene principle, by setting up the incidence relation between keyword, solve the challenge of upwards association and downward associative search, one retrieval server can realize bi-directional association retrieval, improve recall precision, it is not necessary to the hardware supported relying on substantial amounts can realize efficient retrieval.
Accompanying drawing explanation
Fig. 1 a is the data structure schematic diagram of the embodiment of the present invention；
Fig. 1 b is the schematic diagram of the back end data structure of the embodiment of the present invention；
One of data retrieval schematic flow sheet that Fig. 2 provides for the embodiment of the present invention；
The data retrieval schematic flow sheet of the keyword multiple to be retrieved that Fig. 3 provides for the embodiment of the present invention；
The two of the data retrieval schematic flow sheet that Fig. 4 provides for the embodiment of the present invention；
Fig. 5 is the structural representation of the data retrieval server of the embodiment of the present invention.
Detailed description of the invention
For the problems referred to above that prior art exists, embodiments provide a kind of data retrieval scheme, by improving the data structure of storage data, set up the data structure of the multiple back end including multilayer nest, utilize gene principle, between each layer data node, setting up incidence relation, thus realizing the upper and lower two-way retrieval of keyword, improving data search efficiency.
Below in conjunction with accompanying drawing, the embodiment of the present invention is described in detail.
As shown in Figure 1a, data structure includes the back end of multiple multilayer nest to the data structure of the present invention, and the back end of this layer associates with the back end of the back end on upper strata and/or lower floor, associates URL or the picture of same keyword with each back end of layer.
Preferably, the quantity of every layer data node can be 64*m, and m is natural number, say, that the integral multiple that quantity is 64 of every layer data node.Owing to existing operating system is usually 64, the quantity of every layer data node is set to the integral multiple of 64, for 64 bit manipulation systems of existing main flow, it is possible to the operational efficiency of process is substantially improved.
Having illustrated 3 layer data nodes in Fig. 1 a, every layer data node includes 64 back end, and ground floor back end is father node, and second layer back end is child node, and third layer back end is sub-child node, by that analogy.Each back end in 64 father nodes of ground floor can associate child node (i.e. second layer back end), and equally, 64 child nodes of the second layer can associate sub-child node (i.e. third layer back end).
Back end, as the index of data structure, plays vital effect in data retrieval, below in conjunction with Fig. 1 b, describes the structure of back end in detail.
Shown in Fig. 1 b, back end includes this property key portion, father's property key portion and data portion, this property key portion includes this attribute key assignments mark and this attribute keyword, father's property key portion includes father's attribute key assignments mark and father's attribute keyword, the key assignments mark of each back end that data portion includes associating with this back end and corresponding data (i.e. URL and/or picture).
Each back end can be made up of 132 fields, and this property key portion takies the 1-2 field, and father's attribute portion takies the 3-4 field, and front 4 fields are followed successively by: this attribute key assignments mark, this attribute keyword, father's attribute key assignments mark, father's attribute keyword.Data portion takies rear 128 fields, and the key assignments of corresponding 1-64 back end identifies the data corresponding with the 1-64 back end.
The value in father's property key portion of this layer data node is equal with the value in this property key portion of upper layer data node, namely father's attribute key assignments of this layer data node identifies this attribute key assignments identity equality with upper layer data node, as such, it is possible to set up incidence relation between each layer data node.
Below in conjunction with Fig. 2, describe the data retrieval flow process of the present invention in detail.As in figure 2 it is shown, this data retrieval flow process comprises the following steps:
Step 201, it is determined that the key assignments mark of keyword to be retrieved.
Concrete, the key assignments mark of keyword is for unique identification key, and a keyword is uniquely corresponding with the key assignments of keyword mark, and the corresponding relation that the key assignments of keyword and keyword identifies is preset in retrieval server.
After user inputs keyword to be retrieved, the corresponding relation that the key assignments of keyword and keyword that retrieval server is preset according to keyword query to be retrieved identifies, so that it is determined that the key assignments going out keyword to be retrieved identifies.
Step 202, identifies according to the key assignments of keyword to be retrieved, it is determined that the back end in data structure.
Concrete, the key assignments of keyword to be retrieved is identified and matches with this attribute key assignments mark in each layer data node by retrieval server, if the match is successful, it is determined that this attribute key assignments that the match is successful identifies corresponding back end.In the manner described above, retrieval server is capable of determining that the back end that this attribute key assignments mark is identical with the key assignments mark of keyword to be retrieved.
If keyword to be retrieved is one, then in data structure, namely can determine that corresponding back end through primary retrieval.If keyword to be retrieved is multiple, then identifying according to the key assignments of each keyword to be retrieved successively in data structure and just can determine that corresponding back end through repeatedly retrieving, the data retrieval process of multiple keywords to be retrieved is follow-up to be described in detail again.
Step 203, obtains URL or the picture of the back end association determined.
Concrete, it being, with the URL of the back end association determined or picture, the data that described keyword to be retrieved is corresponding, retrieval server obtains the URL associated by back end or picture that determine, and presents to user.
Below in conjunction with Fig. 3, describe the data retrieval flow process of multiple keyword to be retrieved in detail.Keyword to be retrieved is n, and n is the natural number of >=2, and described mark by the key assignments of keyword to be retrieved matches with this attribute key assignments mark in each layer data node, specifically includes following steps:
Step 301, identifies the key assignments of first keyword to be retrieved and matches with this attribute key assignments mark in each layer data node, it is determined that the back end of this attribute key assignments mark correspondence that the match is successful.
Concrete, the key assignments of first keyword to be retrieved is identified and matches with this attribute key assignments mark in each layer data node by data retrieval server, if the match is successful, then obtaining the back end of this attribute key assignments mark correspondence that the match is successful, described back end is the back end that first keyword to be retrieved is corresponding.If it fails to match, illustrate not retrieve the URL corresponding with keyword to be retrieved or image data, then return data retrieval failure information to user.
Step 302, in the lower data range of nodes of the back end association corresponding at the keyword to be retrieved with first, the key assignments of keyword to be retrieved for i-th identifies this attribute key assignments mark with each back end match, if the match is successful, then perform step 303；If it fails to match, then perform step 304.
Concrete, data retrieval server after determining the back end (i.e. ground floor back end) that first keyword to be retrieved is corresponding, retrieval next one keyword within the scope of the back end being associated with ground floor back end.Back end association includes associating with upper layer data node and associating with lower data node, consider that the custom of major part user entered keyword is usually according to the first big after small input of the scope of keyword, such as, first keyword is " portal website ", second keyword is " physical culture ", therefore, data retrieval server is after determining the back end (i.e. ground floor back end) that first keyword to be retrieved is corresponding, the preferential key assignments mark of the keyword that coupling next (i.e. i-th) is to be retrieved in the lower data range of nodes of ground floor back end association, i={2, ..., n}.That is data retrieval server is preferentially retrieved downwards, if retrieving downwards failure, more upwards retrieval (namely performing step 304).
It should be noted that, if the match is successful for this attribute key assignments mark of the key assignments mark of the keyword that i-th is to be retrieved and certain back end, multiple back end that this this attribute key assignments mark is corresponding can be obtained, it is required for each back end and performs step 303 respectively, if final all keyword match successes, then the retrieval result obtained is also for multiple.
Step 303, in the lower data range of nodes of the back end association corresponding at the keyword to be retrieved with i-th, matches this attribute key assignments mark of the key assignments of keyword to be retrieved for i+1 mark with each back end.
Concrete, if data retrieval server is the 2nd keyword to be retrieved of (i.e. second layer back end) successful match in the lower data range of nodes that ground floor back end associates, then continue the keyword (the 3rd keyword to be retrieved) that (i.e. third layer back end) coupling is next to be retrieved in the lower data range of nodes of second layer back end association, by that analogy, until key search all to be retrieved completes.If mating the 3rd keyword to be retrieved failure within the scope of third layer back end, it is determined that final back end is the back end of the 2nd keyword to be retrieved of successful match.
Step 304, in the upper layer data range of nodes of the back end association corresponding at the keyword to be retrieved with first, identifies the key assignments of keyword to be retrieved for i-th this attribute key assignments mark with each back end and matches.
Concrete, if data retrieval server (i.e. second layer back end) in the lower data range of nodes that ground floor back end associates mates the 2nd keyword to be retrieved failure, then the 2nd keyword to be retrieved of coupling in the upper layer data range of nodes of ground floor back end association.If the match is successful, then within the scope of the described back end that the match is successful, mate the 3rd keyword to be retrieved, by that analogy, until key search all to be retrieved completes.If it fails to match, illustrating not retrieve the URL corresponding with keyword to be retrieved or image data, namely data retrieval failure, then return data retrieval failure information to user.
Be can be seen that by step 301-304, when retrieving multiple keyword, order according to keyword to be retrieved, progressively position back end, preferential retrieval downwards, upwards retrieves when retrieving unsuccessfully downwards, by two-way retrieval again, it is not only able to improve the efficiency of data retrieval, and ensures the comprehensive and accuracy of retrieval result.
In order to improve the recall precision of repeated retrieval same keyword further, searching route can also be recorded after data retrieval success, when the keyword that later retrieval is identical, it is possible to directly invoke searching route and obtain retrieval result, it is not necessary to again retrieve data structure.
Below in conjunction with Fig. 4, describe the data retrieval flow process that another embodiment of the present invention provides in detail.As shown in Figure 4, this data retrieval flow process comprises the following steps:
Step 401, it is determined that the key assignments mark of keyword to be retrieved.
Concrete, the implementation of this step is identical with step 201, does not repeat them here.
Step 402, identifies the key assignments of keyword to be retrieved and matches with searching route, if the match is successful, then performs step 403；If it fails to match, then perform step 404.
Concrete, searching route identifies according to the key assignments of keyword to be retrieved and generates, after the success of each data retrieval, and data retrieval server memory scan path.
If the match is successful with searching route for the key assignments mark of keyword to be retrieved, illustrate that keyword to be retrieved was once successfully retrieved, therefore, it can directly obtain retrieval result (namely performing step 403 and step 405) according to searching route.If it fails to match with searching route for the key assignments mark of keyword to be retrieved, it was not retrieved before keyword to be retrieved is described, therefore, in data structure, determine back end that the keyword to be retrieved to this is relevant and obtain the data (namely performing step 404 and step 405) of association.
Step 403, determines back end according to searching route, and performs step 405.
Concrete, the key assignments mark that searching route is according to keyword to be retrieved generates, and for multiple keywords to be retrieved, its searching route is the combination of the key assignments mark of each keyword to be retrieved.Such as, keyword to be retrieved is " portal website, physical culture ", one searching route of described keyword is formed by the key assignments mark 00000001 of keyword " portal website " to be retrieved and key assignments mark 00000211 connection of keyword " physical culture " to be retrieved, for " 0000000100000211 ".If inputting same keyword when retrieval next time, data retrieval server directly can obtain this attribute key assignments mark 00000001 and 00000211 of back end according to this searching route, can position accurately, rapidly back end according to this this attribute key assignments mark in data structure.
Step 404, identifies according to the key assignments of keyword to be retrieved, it is determined that the back end in data structure.
Concrete, the implementation of this step is identical with step 202, does not repeat them here.
Step 405, obtains URL or the picture of the back end association determined.
Concrete, the implementation of this step is identical with step 203, does not repeat them here.
Be can be seen that by step 401-405, during according to key search data, key assignments first with keyword to be retrieved identifies and searching route matches, if the match is successful, can directly position back end, it is possible to the data search efficiency of repeated retrieval same keyword is substantially improved.
Further, after step 202 and step 404, also perform following steps:
Key assignments mark according to keyword to be retrieved generates searching route, and stores described searching route.Wherein, when keyword to be retrieved is multiple, according to the sequence of keyword to be retrieved, integrating the key assignments mark of each keyword to be retrieved, described searching route is the key assignments mark after integrating.
It should be noted that, in data retrieval process, it it is this attribute key assignments mark realization being identified matched data node by the key assignments of keyword to be retrieved, therefore, the searching route of keyword to be retrieved is the combination of this attribute key assignments mark of the back end with keyword association to be retrieved.
The data retrieval scheme of the present invention goes for the retrieval of single keyword, it is also possible to suitable in the retrieval of multiple keywords.When retrieving single keyword, the key assignments of keyword to be retrieved is identified and matches with this attribute key assignments mark in each layer data node by data retrieval server, can obtain retrieval result.When retrieving multiple keyword, data retrieval server then needs the order according to keyword to be retrieved, progressively positions back end up and/or down.
The demand of the equally possible adaptation magnanimity retrieval of data retrieval scheme of the present invention, although every layer data node only includes 64*m back end, but, distributed data searching system can be passed through, utilize multiple stage data retrieval server parallel data processing retrieval tasks, realizing the parallel processing of magnanimity retrieval, the fragmentation in units of separate unit data retrieval server concurrently can be substantially improved data search efficiency, it is achieved retrieval process cloud computing.
The data retrieval protocol index of the present invention takes up room little, data retrieval principle is easy to use, there is no magnificent algorithm, but operational efficiency is high, can be applied not only to internet search engine, be also used as intelligent terminal search engine use, for instance, smart mobile phone, intelligent watch miniature search engine.
Based on identical technology design, the embodiment of the present invention also provides for a kind of data retrieval server, as it is shown in figure 5, this data retrieval server may include that processing module 52, memory module 51 and retrieval module 53.
Memory module 51 is used for storing data, data structure includes the back end of multiple multilayer nest, the back end of this layer associates with the back end of the back end on upper strata and/or lower floor, associates uniform resource position mark URL or the picture of same keyword with each back end of layer.
Processing module 52 is used for, it is determined that the key assignments mark of keyword to be retrieved.
Retrieval module 53 is used for, and identifies according to the key assignments of keyword to be retrieved, it is determined that the back end in data structure；Wherein, when keyword to be retrieved is multiple, determine the back end that first keyword to be retrieved is corresponding, and in the upper layer data node associated with described back end and/or lower data range of nodes, from second keyword to be retrieved, according to the order of keyword to be retrieved, identify according to the key assignments of keyword to be retrieved and determine back end；And, obtain URL or the picture of the back end association determined.
Back end includes this property key portion and father's property key portion, and the value in father's property key portion of this layer data node is equal with the value in this property key portion of upper layer data node；This property key portion includes this attribute key assignments mark, and father's property key portion includes father's attribute key assignments mark；
Retrieval module 53 specifically for, the key assignments of keyword to be retrieved is identified and matches with this attribute key assignments mark in each layer data node, if the match is successful, it is determined that the back end that this attribute key assignments mark that the match is successful is corresponding.
Preferably, retrieval module 53 specifically for, when keyword to be retrieved is n, n is the natural number of >=2, the key assignments of first keyword to be retrieved is identified and matches with this attribute key assignments mark in each layer data node, determining the back end of this attribute key assignments mark correspondence that the match is successful, described back end is the back end that first keyword to be retrieved is corresponding；And, in the lower data range of nodes of the back end association corresponding at the keyword to be retrieved with first, the key assignments of keyword to be retrieved for i-th is identified this attribute key assignments mark with each back end and matches；If it fails to match, then in the upper layer data range of nodes of corresponding at the keyword to be retrieved with first back end association, the key assignments of keyword to be retrieved for i-th is identified this attribute key assignments mark with each back end and matches；If the match is successful, then, in the lower data range of nodes of corresponding at the keyword to be retrieved with i-th back end association, this attribute key assignments mark of the key assignments of keyword to be retrieved for i+1 mark with each back end is matched；Wherein, i={2 ..., n}.
Processing module 52 is additionally operable to, and is identified by the key assignments of keyword to be retrieved and matches with searching route, and wherein, described searching route is storage after the success of each data retrieval.
Retrieval module 53 specifically for, when the key assignments of keyword to be retrieved identify with searching route it fails to match time, identify the back end determining in data structure according to the key assignments of keyword to be retrieved.
Retrieval module 53 is additionally operable to, and when the match is successful for key assignments mark and the searching route of keyword to be retrieved, determines back end according to searching route, and obtains URL or the picture of the association of described back end.
Further, described data retrieval server also includes searching route generation module 54, and searching route generation module 54 is used for, and identifies according to the key assignments of keyword to be retrieved and generates searching route, and stores described searching route；Wherein, when keyword to be retrieved is multiple, according to the sequence of keyword to be retrieved, integrating the key assignments mark of each keyword to be retrieved, described searching route is the key assignments mark after integrating.
It is understood that the principle that is intended to be merely illustrative of the present of embodiment of above and the illustrative embodiments that adopts, but the invention is not limited in this.For those skilled in the art, without departing from the spirit and substance in the present invention, it is possible to make various modification and improvement, these modification and improvement are also considered as protection scope of the present invention.
Claims (10)
1. a data retrieval method, it is characterized in that, data structure includes the back end of multiple multilayer nest, and the back end of this layer associates with the back end of the back end on upper strata and/or lower floor, associates uniform resource position mark URL or the picture of same keyword with each back end of layer；Said method comprising the steps of:
Determine the key assignments mark of keyword to be retrieved；
Key assignments mark according to keyword to be retrieved, it is determined that the back end in data structure；Wherein, when keyword to be retrieved is multiple, determine the back end that first keyword to be retrieved is corresponding, and in the upper layer data node associated with described back end and/or lower data range of nodes, from second keyword to be retrieved, according to the order of keyword to be retrieved, identify according to the key assignments of keyword to be retrieved and determine back end；
Obtain URL or the picture of the back end association determined.
2. the method for claim 1, it is characterised in that back end includes this property key portion and father's property key portion, the value in father's property key portion of this layer data node is equal with the value in this property key portion of upper layer data node；This property key portion includes this attribute key assignments mark, and father's property key portion includes father's attribute key assignments mark；
The described key assignments according to keyword to be retrieved identifies the back end determining in data structure, specifically includes:
The key assignments of keyword to be retrieved is identified and matches with this attribute key assignments mark in each layer data node, if the match is successful, it is determined that the back end of this attribute key assignments mark correspondence that the match is successful.
3. method as claimed in claim 2, it is characterised in that when keyword to be retrieved is n, n is the natural number of >=2, described mark by the key assignments of keyword to be retrieved matches with this attribute key assignments mark in each layer data node, specifically includes:
The key assignments of first keyword to be retrieved is identified and matches with this attribute key assignments mark in each layer data node, determining the back end of this attribute key assignments mark correspondence that the match is successful, described back end is the back end that first keyword to be retrieved is corresponding；
In the lower data range of nodes of the back end association corresponding at the keyword to be retrieved with first, the key assignments of keyword to be retrieved for i-th is identified this attribute key assignments mark with each back end and matches；
If it fails to match, then in the upper layer data range of nodes of corresponding at the keyword to be retrieved with first back end association, the key assignments of keyword to be retrieved for i-th is identified this attribute key assignments mark with each back end and matches；
If the match is successful, then, in the lower data range of nodes of corresponding at the keyword to be retrieved with i-th back end association, this attribute key assignments mark of the key assignments of keyword to be retrieved for i+1 mark with each back end is matched；Wherein, i={2 ..., n}.
4. method as claimed in claim 2, it is characterized in that, before identifying, after the key assignments determining keyword to be retrieved identifies, according to the key assignments of keyword to be retrieved, the back end determining in data structure, described method also includes: is identified by the key assignments of keyword to be retrieved and matches with searching route, wherein, described searching route is storage after the success of each data retrieval；
Key assignments mark according to keyword to be retrieved determines the back end in data structure, particularly as follows: if the key assignments of keyword to be retrieved identifies, it fails to match with searching route, then identify the back end determining in data structure according to the key assignments of keyword to be retrieved；
Described method also includes:
If the match is successful with searching route for the key assignments mark of keyword to be retrieved, then determine back end according to searching route, and obtain URL or the picture of the association of described back end.
5. the method as described in any one of claim 1-4, it is characterised in that after the back end in determining data structure, described method also includes:
Key assignments mark according to keyword to be retrieved generates searching route, and stores described searching route；
Wherein, when keyword to be retrieved is multiple, according to the sequence of keyword to be retrieved, integrating the key assignments mark of each keyword to be retrieved, described searching route is the key assignments mark after integrating.
6. a data retrieval server, it is characterised in that including: processing module, memory module and retrieval module；
Memory module is used for storing data, data structure includes the back end of multiple multilayer nest, the back end of this layer associates with the back end of the back end on upper strata and/or lower floor, associates uniform resource position mark URL or the picture of same keyword with each back end of layer；
Processing module is used for, it is determined that the key assignments mark of keyword to be retrieved；
Retrieval module is used for, and identifies according to the key assignments of keyword to be retrieved, it is determined that the back end in data structure；Wherein, when keyword to be retrieved is multiple, determine the back end that first keyword to be retrieved is corresponding, and in the upper layer data node associated with described back end and/or lower data range of nodes, from second keyword to be retrieved, according to the order of keyword to be retrieved, identify according to the key assignments of keyword to be retrieved and determine back end；And, obtain URL or the picture of the back end association determined.
7. data retrieval server as claimed in claim 6, it is characterised in that back end includes this property key portion and father's property key portion, and the value in father's property key portion of this layer data node is equal with the value in this property key portion of upper layer data node；This property key portion includes this attribute key assignments mark, and father's property key portion includes father's attribute key assignments mark；
Described retrieval module specifically for, the key assignments of keyword to be retrieved is identified and matches with this attribute key assignments mark in each layer data node, if the match is successful, it is determined that the back end that this attribute key assignments mark that the match is successful is corresponding.
8. data retrieval server as claimed in claim 7, it is characterized in that, described retrieval module specifically for, when keyword to be retrieved is n, n is the natural number of >=2, the key assignments of first keyword to be retrieved is identified and matches with this attribute key assignments mark in each layer data node, it is determined that the back end of this attribute key assignments mark correspondence that the match is successful, described back end is the back end that first keyword to be retrieved is corresponding；And, in the lower data range of nodes of the back end association corresponding at the keyword to be retrieved with first, the key assignments of keyword to be retrieved for i-th is identified this attribute key assignments mark with each back end and matches；If it fails to match, then in the upper layer data range of nodes of corresponding at the keyword to be retrieved with first back end association, the key assignments of keyword to be retrieved for i-th is identified this attribute key assignments mark with each back end and matches；If the match is successful, then, in the lower data range of nodes of corresponding at the keyword to be retrieved with i-th back end association, this attribute key assignments mark of the key assignments of keyword to be retrieved for i+1 mark with each back end is matched；Wherein, i={2 ..., n}.
9. data retrieval server as claimed in claim 7, it is characterised in that described processing module is additionally operable to, identifies the key assignments of keyword to be retrieved and matches with searching route, and wherein, described searching route is storage after the success of each data retrieval；
Described retrieval module specifically for, when the key assignments of keyword to be retrieved identify with searching route it fails to match time, identify the back end determining in data structure according to the key assignments of keyword to be retrieved；
Described retrieval module is additionally operable to, and when the match is successful for key assignments mark and the searching route of keyword to be retrieved, determines back end according to searching route, and obtains URL or the picture of the association of described back end.
10. the data retrieval server as described in any one of claim 6-9, it is characterised in that also including searching route generation module, searching route generation module is used for, identifies according to the key assignments of keyword to be retrieved and generates searching route, and store described searching route；Wherein, when keyword to be retrieved is multiple, according to the sequence of keyword to be retrieved, integrating the key assignments mark of each keyword to be retrieved, described searching route is the key assignments mark after integrating.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610141677.6A CN105808745B (en) | 2016-03-11 | 2016-03-11 | A kind of data retrieval method and server |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610141677.6A CN105808745B (en) | 2016-03-11 | 2016-03-11 | A kind of data retrieval method and server |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105808745A true CN105808745A (en) | 2016-07-27 |
| CN105808745B CN105808745B (en) | 2019-06-28 |
Family
ID=56467351
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610141677.6A Active CN105808745B (en) | 2016-03-11 | 2016-03-11 | A kind of data retrieval method and server |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105808745B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113515544A (en) * | 2021-06-23 | 2021-10-19 | 金蝶软件（中国）有限公司 | Data attribute query method and data attribute query device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521375A (en) * | 2011-12-19 | 2012-06-27 | 山东中创软件商用中间件股份有限公司 | Directory service data retrieval method and directory service data retrieval system |
| CN103390015A (en) * | 2013-01-16 | 2013-11-13 | 华北电力大学 | Mass data united storage method based on unified indexing and search method |
| US20140317141A1 (en) * | 2009-01-30 | 2014-10-23 | International Business Machines Corporation | Selective expansion of a tree heirarchy |
| US9143449B2 (en) * | 2012-07-31 | 2015-09-22 | Cisco Technology, Inc. | Methods and apparatuses for improving database search performance |
-
2016
- 2016-03-11 CN CN201610141677.6A patent/CN105808745B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140317141A1 (en) * | 2009-01-30 | 2014-10-23 | International Business Machines Corporation | Selective expansion of a tree heirarchy |
| CN102521375A (en) * | 2011-12-19 | 2012-06-27 | 山东中创软件商用中间件股份有限公司 | Directory service data retrieval method and directory service data retrieval system |
| US9143449B2 (en) * | 2012-07-31 | 2015-09-22 | Cisco Technology, Inc. | Methods and apparatuses for improving database search performance |
| CN103390015A (en) * | 2013-01-16 | 2013-11-13 | 华北电力大学 | Mass data united storage method based on unified indexing and search method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113515544A (en) * | 2021-06-23 | 2021-10-19 | 金蝶软件（中国）有限公司 | Data attribute query method and data attribute query device |
| CN113515544B (en) * | 2021-06-23 | 2022-02-15 | 金蝶软件（中国）有限公司 | Data attribute query method and data attribute query device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105808745B (en) | 2019-06-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110941612B (en) | Autonomous data lake construction system and method based on associated data | |
| US9805079B2 (en) | Executing constant time relational queries against structured and semi-structured data | |
| CN102063476B (en) | Video searching method and system | |
| CN107038207A (en) | A kind of data query method, data processing method and device | |
| CN111259006A (en) | Universal distributed heterogeneous data integrated physical aggregation, organization, release and service method and system | |
| CN103810224B (en) | information persistence and query method and device | |
| CN103116657B (en) | A kind of individuation search method of network teaching resource | |
| CN102968465B (en) | Network information service platform and the search service method based on this platform thereof | |
| CN105404634B (en) | Data managing method and system based on Key-Value data block | |
| CN105468605A (en) | Entity information map generation method and device | |
| CN104391908B (en) | Multiple key indexing means based on local sensitivity Hash on a kind of figure | |
| CN107092639A (en) | A kind of search engine system | |
| CN102332030A (en) | Data storing, managing and inquiring method and system for distributed key-value storage system | |
| CN105209858B (en) | The uncertainty of business location's data disappears qi and matching | |
| CN101984422A (en) | Fault-tolerant text query method and equipment | |
| CN104112005B (en) | Distributed mass fingerprint identification method | |
| CN107766433A (en) | A kind of range query method and device based on Geo BTree | |
| CN103412903B (en) | The Internet of Things real-time searching method and system predicted based on object of interest | |
| CN101639776A (en) | Database access and integration method and system thereof | |
| WO2018218184A1 (en) | Content access and storage | |
| CN104484478B (en) | A kind of association cluster data method for reducing body weight and system | |
| CN108509545A (en) | A kind of comment processing method and system of article | |
| CN104536984A (en) | Verification method and system for space text Top-k query in outsourced database | |
| CN108388606A (en) | A kind of method and computer equipment verifying library literary name section name in Sql sentences | |
| CN103177046B (en) | A kind of data processing method based on row storage data base and equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |