CN101984433A - Convexity based multiple spots far and near querying method - Google Patents
Convexity based multiple spots far and near querying method Download PDFInfo
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Abstract
The invention discloses a convexity based multiple spots far and near querying method. A spatial database system is created by combining a traditional DBMS platform and a spatial database index and a spatial database indexing technique is reasonably selected, and then a method which is used for calculating a distance required by a multiple spots far and near querying method provided by an engine function is developed. A convexity based multiple spots far and near querying processing engine is developed, which comprises the minimum defined multiple spots far and near querying method, preferably a multiple spots far and near querying method and a convex based multiple spots far and near querying method. A multiple spots far and near querying optimizing engine is developed, which can intelligently query by selecting suitable multiple spots far and near querying method according to the querying features. Through the method of the invention, the DBMS platform, the spatial database index and the spatial database index technical result are utilized to develop the convexity based multiple spots far and near querying processing engine based on the existing system shortcut, so as to select the optimum querying method through the multiple spots far and near querying optimizing engine and provide the beast property.
Description
Technical field
The present invention relates to spatial database index technology and inquiring technology, particularly relate to a kind of multiple spot adjacent querying method far away based on convexity.
Background technology
Spatial database makes up based on generalized database management system (DBMS) usually.DBMS platform, operating system platform that different spatial database systems chooses when realizing are not quite similar.Spatial database engine (Spatial Data Engine, SDE) be to be purpose with masking operation system and data base management system (DBMS) difference, being structured between application layer and the database layer based on the data access middleware of efficient spatial search function usage space data, is the collection of programs that data storage, efficient retrieval, data management, network service, issued transaction and simple data processing capacity are provided to application layer.By increase the SDE layer between database layer and application layer, spatial data can be shared and interoperability efficiently for different clients, satisfies specific area spatial data management, visit and application requirements.At present, aspect spatial database engine, further investigate and form the ArcSDE that ESRI is arranged of software product both at home and abroad, the SpatialWare of Maplnfo, the Spatial of Oracle, the Spatial Extender of IBM DB2 and the Data-Blade of IBM Imfonnix, the Spatial Extensions of the SuperMapSDX+ of hypergraph and MySQL etc.
In all spatial database index technology, R tree index and its mutation are because be simple and easy to being most widely used with validity.Directly perceived going up, R tree index is the expansion of B-tree at higher dimensional space.In minimum bounding box, these minimum bounding boxs are again according to the cluster of carrying out of spatial locality recurrence, up to arriving root node by cluster for the close data point in position on the space.There are three distances often to be used on the R tree index: minor increment, ultimate range and minimum ultimate range.
Querying method on the spatial database is varied, and wherein modal is exactly neighbour's inquiry.The neighbour inquires about usually and according to depth-first or preferably preferential strategy R tree index is conducted interviews, and wherein preferably preferential strategy is optimum in IO consumption.On the basis of neighbour's inquiry, derived multiple spot neighbour inquiry recently, and be applied to above the road network.
Though the spatial database system development is gradually improved, the existing space data base query method can not be handled multiple spot adjacent query demand far away.
Summary of the invention
The object of the present invention is to provide a kind of multiple spot adjacent method of inquiring about far away based on convexity.
The present invention solve its technical matters adopt technical scheme step as follows:
Step 1) is selected a DBMS platform according to the user for use to the requirement of building index performance, query performance and real-time property;
Step 2) spatial database engine of exploitation is realized and above-mentioned steps 1) in the DBMS platform selected for use mutual, and select the spatial database index technology for use;
Convexity distance function engine that is applicable to multiple spot adjacent querying method far away of step 3) exploitation;
Step 4) is in step 2) realize multiple spot adjacent query processing engine far away on the convexity distance function engine that makes up of the spatial database that makes up and step 3), multiple spot inquiry far away adjacent comprises the multiple spot adjacent querying method far away that minimum defines, preferably preferential multiple spot adjacent querying method far away and based on the multiple spot of convex closure adjacent querying method far away;
Multiple spot adjacent query optimization engine far away of step 5) exploitation, multiple spot adjacent querying method far away suitable in the selection step 4) according to query characteristics intelligence carries out query processing.
The DBMS platform of selecting for use in the step 1) should be supported basic SQL query, and existing most of DBMS platforms all satisfy this demand, therefrom choose according to application demand, as:
1) if the high query performance of application requirements is selected Oracle for use;
2) if application requirements is simple and easy to usefulness, select MySQL for use.
Step 2) spatial database engine in is the data access middleware that is structured in usage space data between application layer and the database layer, it must cooperatively interact with the DBMS platform selected for use in the step 1), accept the order of upper strata query engine and be converted into SQL statement in DBMS, to move, the spatial database index technology is generally selected R tree index for use, as:
1) if the DBMS platform of selecting is Oracle, selects the Spatial of Oracle for use;
2) if the DBMS platform of selecting is MySQL, select the Spatial Extensions of MySQL for use.
Convexity distance function engine in the step 3) provides with minor function:
1) ultimate range function: the ultimate range function comprises following two kinds of situations: a) single-point is to the ultimate range of minimum bounding box; B) ultimate range between two minimum bounding boxs; Draw by the convexity that to the single-point distance is the curve of definite value, single-point to the ultimate range of minimum bounding box must be single-point to each summit of minimum bounding box apart from maximal value, between each summit that further to draw two ultimate ranges between the minimum bounding box thus are two minimum bounding boxs apart from maximal value;
2) multiple spot distance and function: multiple spot distance and function handle single-point to the distance of multiple spot with;
3) multiple spot ultimate range and function: multiple spot ultimate range and function comprise following two kinds of situations: a) multiple spot to the ultimate range of minimum bounding box and; B) ultimate range between the minimum bounding box of multiple spot and another minimum bounding box and; Both of these case is respectively the multiple spot situation of ultimate range function, sums up for multiple spot to get final product;
4) maximum multiple spot distance and function: maximum multiple spot distance and function handle multiple spot in the minimum bounding box any possible ultimate range with; Drawn by the convexity that to the multiple spot distance is the curve of definite value, multiple spot must be distance and the maximal value of multiple spot to each summit of minimum bounding box to the ultimate range of minimum bounding box.
The multiple spot that minimum in the step 4) defines adjacent querying method concrete steps far away are:
1) answer set of a sky of initialization, the root node that R sets index is set to present node;
2) if present node is a leaf node, jump to 3); Otherwise be intermediate node, jump to 4);
3) for the data point in all leaf nodes, if the multiple spot ultimate range of the minimum bounding box of it and query point and greatly than k answer distance in the answer set, and the multiple spot distance of it and query point and greatly than k answer distance in the answer set, so this data point is inserted in the answer set, and safeguards the no more than k of number of answer set; Finish;
4) node listing of a sky of initialization; For the child node in all intermediate nodes, if the multiple spot ultimate range between the minimum bounding box of its minimum bounding box and query point and bigger than k answer distance in the answer set, and the multiple spot ultimate range of its minimum bounding box and query point and bigger than k answer distance in the answer set is inserted into this child node in the node listing so;
5) to the node in the node listing according to multiple spot ultimate range and descending sort, for each node in the node listing,, jump to 2 if multiple spot ultimate range and bigger than the distance of k answer in the answer set is made as present node to this node).
Preferably preferential multiple spot in the step 4) adjacent querying method concrete steps far away are:
1) Priority Queues of initialization, the inside have only the root node of a R tree index;
2) answer set of a sky of initialization;
3) if Priority Queues is not empty and the answer set number less than k, jump to 4); Otherwise, finish;
4) in Priority Queues, propose a node,, then data point is inserted in the answer set if this node is a data point; If this is an intermediate node, hereto each child node of node calculate maximum multiple spot distance and, all child nodes are inserted in the Priority Queues.
The multiple spot based on convex closure in the step 4) adjacent querying method concrete steps far away are:
1) answer set of a sky of initialization;
2) convex closure of computational data collection;
3) summit of traversal convex closure, to each summit calculate multiple spot distance and, the summit of the value of finding maximum is inserted in the answer set; If the answer set number arrives k, then finish; Otherwise jump to 4);
4) in convex closure, find the left and right sides neighbours on answer summit, the answer summit is removed in convex closure; In the triangle that answer summit and left and right sides neighbours form, find a data point farthest to the distance of left and right sides neighbours' line; This data point is inserted in the convex closure, and recurrence about carry out the reconstruction of convex closure in two triangles; Jump to 3).
Multiple spot in the step 5) adjacent query optimization engine far away need be considered following query characteristics:
1) Cha Xun query point number;
2) Cha Xun query point distributions situation;
3) Cha Xun answer set size;
4) Cha Xun data set size.
The beneficial effect that the present invention has is:
The present invention has made full use of the existing research of DBMS platform, spatial database engine and spatial database index technology and has realized achievement, develop efficiently based on the multiple spot of convexity adjacent query processing engine far away based on existed system is very convenient, the user is according to selecting only querying method by multiple spot adjacent query optimization engine far away, and performance offers the best.
Description of drawings
Fig. 1 is the invention process flow chart of steps.
Fig. 2 is a multiple spot adjacent inquiry system principle of work synoptic diagram far away.
Fig. 3 is that minimum defines and preferably preferential multiple spot adjacent query processing engine principle of work synoptic diagram far away.
Fig. 4 is based on the multiple spot adjacent query processing engine principle of work synoptic diagram far away of convex closure.
Embodiment
Now with specific embodiment technical scheme of the present invention is described further in conjunction with the accompanying drawings.
As Fig. 1, shown in Figure 2, specific implementation process of the present invention and principle of work are as follows:
Step 1) is selected a DBMS platform according to the user to the requirement of building index performance, query performance and real-time property;
Step 2) spatial database engine of exploitation is realized and above-mentioned steps 1) in the DBMS platform chosen mutual, and select suitable spatial database index technology;
Convexity distance function engine that is applicable to multiple spot adjacent querying method far away of step 3) exploitation;
Step 4) is in step 2) realize multiple spot adjacent query processing engine far away on the distance function engine that makes up of the spatial database that makes up and step 3), comprise the multiple spot adjacent querying method far away that minimum defines, preferably preferential multiple spot adjacent querying method far away and based on the multiple spot of convex closure adjacent querying method far away;
Multiple spot adjacent query optimization engine far away of step 5) exploitation, multiple spot adjacent querying method far away suitable in the selection step 4) according to query characteristics intelligence carries out query processing.
The DBMS platform of selecting for use in the step 1) should be supported basic SQL query; Existing most of DBMS platforms all satisfy this demand, can therefrom choose according to application demand, as:
1) if the high query performance of application requirements is selected Oracle for use;
2) if application requirements is simple and easy to usefulness, select MySQL for use.
Step 2) spatial database engine in is the data access middleware that is structured in usage space data between application layer and the database layer, it must cooperatively interact with the DBMS platform chosen in the step 1), accepts the order of upper strata query engine and be converted into SQL statement to move in DBMS; The spatial database index technology is generally selected R tree index for use; As:
1) if the DBMS platform of selecting is Oracle, selects the Spatial of Oracle for use;
2) if the DBMS platform of selecting is MySQL, select the Spatial Extensions of MySQL for use.
Convexity distance function engine in the step 3) provides with minor function:
1) ultimate range function; The ultimate range function comprises following two kinds of situations: a) single-point is to the ultimate range of minimum bounding box; B) ultimate range between two minimum bounding boxs.Can draw by the convexity that to single-point distance is the curve of definite value, single-point to the ultimate range of minimum bounding box must be single-point to each summit of minimum bounding box apart from maximal value.Between each summit that can further to draw two ultimate ranges between the minimum bounding box thus are two minimum bounding boxs apart from maximal value;
2) multiple spot distance and function; Multiple spot distance and function handle single-point to the distance of multiple spot with;
3) multiple spot ultimate range and function; Multiple spot ultimate range and function comprise following two kinds of situations: a) multiple spot to the ultimate range of minimum bounding box and; B) ultimate range between the minimum bounding box of multiple spot and another minimum bounding box and.Both of these case is respectively the multiple spot situation of ultimate range function, sums up for multiple spot to get final product;
4) maximum multiple spot distance and function; Maximum multiple spot distance and function handle multiple spot in the minimum bounding box any possible ultimate range with; Can be drawn by the convexity that to the multiple spot distance is the curve of definite value, multiple spot must be distance and the maximal value of multiple spot to each summit of minimum bounding box to the ultimate range of minimum bounding box.
The multiple spot that minimum in the step 4) defines adjacent querying method concrete steps far away are:
1) answer set of a sky of initialization, the root node that R sets index is set to present node;
2) if present node is a leaf node, jump to 3); Otherwise be intermediate node, jump to 4);
3) for the data point in all leaf nodes, if the multiple spot ultimate range of the minimum bounding box of it and query point and greatly than k answer distance in the answer set, and the multiple spot distance of it and query point and greatly than k answer distance in the answer set, so this data point is inserted in the answer set, and safeguards the no more than k of number of answer set; Finish;
4) node listing of a sky of initialization; For the child node in all intermediate nodes, if the multiple spot ultimate range between the minimum bounding box of its minimum bounding box and query point and bigger than k answer distance in the answer set, and the multiple spot ultimate range of its minimum bounding box and query point and bigger than k answer distance in the answer set is inserted into this child node in the node listing so;
5) to the node in the node listing according to multiple spot ultimate range and descending sort, for each node in the node listing,, jump to 2 if multiple spot ultimate range and bigger than the distance of k answer in the answer set is made as present node to this node).
Looking for 2 neighbours far away with Fig. 3 is example, and the multiple spot that the minimum in the step 4) defines adjacent querying method embodiment far away is as follows:
1) q
1And q
2It is query point; Begin visit from root node, answer set is empty; M
1To M
6It is minimum bounding box; M
1And M
2Be inserted in the node listing; Because M
1Multiple spot ultimate range and bigger, M
1It is the node that the next one will be visited;
2) and 1) similar, M
3And M
4Be inserted in the node listing M
3It is the node that the next one will be visited;
3) p
1To p
10It is data point; p
1And p
2Be M
3In data point, calculate the multiple spot distance and be inserted in the answer set, answer set has become { p
1, p
2;
4) querying method is got back to M
4, M
4Can not be by beta pruning, the data point of its inside all will be handled, and last answer set has become { p
3, p
1;
5) querying method can road M
2, M
2Can not be by beta pruning, but the child node M of its inside
5And M
6All because multiple spot ultimate range and compare p
1Fallen by beta pruning greatly, the final result collection is exactly { p
3, p
1.
Preferably preferential multiple spot in the step 4) adjacent querying method concrete steps far away are:
1) Priority Queues of initialization, the inside have only the root node of a R tree index;
2) answer set of a sky of initialization;
3) if Priority Queues is not empty and the answer set number less than k, jump to 4); Otherwise, finish;
4) in Priority Queues, propose a node,, then data point is inserted in the answer set if this node is a data point; If this is an intermediate node, hereto each child node of node calculate maximum multiple spot distance and, all child nodes are inserted in the Priority Queues.
Looking for 2 neighbours far away with Fig. 3 is example, and the adjacent querying method embodiment far away of the preferably preferential multiple spot in the step 4) is as follows:
1) as node unique in the Priority Queues, root node is accessed; M
1And M
2Maximum multiple spot distance and process are calculated and are inserted in the Priority Queues; Priority Queues is { M
1, M
2;
2) M
1Two sub-node M
3And M
4Be inserted in the Priority Queues; Priority Queues becomes { M
3, M
4, M
2;
3) as M
3In data point, p
1And p
2The multiple spot distance and calculate after be inserted into Priority Queues, M
4The also similar processing of child node; Priority Queues becomes { p
3, M
2, p
1, p
2, p
4, p
5;
4) p
3At first be inserted in the answer set; M then
2In data point also be inserted into Priority Queues; Priority Queues is { p
1, M
5, p
2, M
6, p
4, p
5, answer set is { p
3;
5) p on the Priority Queues head
1Be inserted in the answer set, obtain final result collection { p
3, p
1.
The multiple spot based on convex closure in the step 4) adjacent querying method concrete steps far away are:
1) answer set of a sky of initialization;
2) convex closure of computational data collection;
3) summit of traversal convex closure, to each summit calculate multiple spot distance and, the summit of the value of finding maximum is inserted in the answer set; If the answer set number arrives k, then finish; Otherwise jump to 4);
4) in convex closure, find the left and right sides neighbours on answer summit, the answer summit is removed in convex closure; In the triangle that answer top and left and right sides neighbours form, find a data point farthest to the distance of left and right sides neighbours' line; This data point is inserted in the convex closure, and recurrence about carry out the reconstruction of convex closure in two triangles; Jump to 3).
Looking for 2 neighbours far away with Fig. 4 is example, and the adjacent querying method embodiment far away of the multiple spot based on convex closure in the step 4) is as follows:
1) convex closure is p
9p
3p
1p
2p
7p
8p
9, calculated the multiple spot distance on all summits and obtained p afterwards
3The value maximum, answer set becomes { p
3;
2) deletion p
3After, convex closure becomes p
9p
5p
4p
1p
2p
7p
8p
9, p
1Become next far away adjacent and be inserted in the answer set, obtain final result collection { p
3, p
1.
Multiple spot in the step 5) adjacent query optimization engine far away need be considered following query characteristics:
1) Cha Xun query point number;
2) Cha Xun query point distributions situation;
3) Cha Xun answer set size;
4) Cha Xun data set size.
Claims (8)
1. adjacent querying method far away of the multiple spot based on convexity is characterized in that the step of this method is as follows:
Step 1) is selected a DBMS platform according to the user for use to the requirement of building index performance, query performance and real-time property;
Step 2) spatial database engine of exploitation is realized and above-mentioned steps 1) in the DBMS platform selected for use mutual, and select the spatial database index technology for use;
Convexity distance function engine that is applicable to multiple spot adjacent querying method far away of step 3) exploitation;
Step 4) is in step 2) realize multiple spot adjacent query processing engine far away on the convexity distance function engine that makes up of the spatial database that makes up and step 3), multiple spot inquiry far away adjacent comprises the multiple spot adjacent querying method far away that minimum defines, preferably preferential multiple spot adjacent querying method far away and based on the multiple spot of convex closure adjacent querying method far away;
Multiple spot adjacent query optimization engine far away of step 5) exploitation, multiple spot adjacent querying method far away suitable in the selection step 4) according to query characteristics intelligence carries out query processing.
2. a kind of multiple spot according to claim 1 adjacent querying method far away based on convexity, it is characterized in that: the DBMS platform of selecting for use in the step 1) should be supported basic SQL query, existing most of DBMS platforms all satisfy this demand, therefrom choose according to application demand, as:
1) if the high query performance of application requirements is selected Oracle for use;
2) if application requirements is simple and easy to usefulness, select MySQL for use.
3. a kind of multiple spot according to claim 1 adjacent querying method far away based on convexity, it is characterized in that: step 2) in spatial database engine be the data access middleware that is structured in usage space data between application layer and the database layer, it must cooperatively interact with the DBMS platform selected for use in the step 1), accept the order of upper strata query engine and be converted into SQL statement in DBMS, to move, the spatial database index technology is generally selected R tree index for use, as:
1) if the DBMS platform of selecting is Oracle, selects the Spatial of Oracle for use;
2) if the DBMS platform of selecting is MySQL, select the Spatial Extensions of MySQL for use.
4. a kind of multiple spot based on convexity according to claim 1 adjacent querying method far away is characterized in that: the convexity distance function engine in the step 3) provides with minor function:
1) ultimate range function: the ultimate range function comprises following two kinds of situations: a) single-point is to the ultimate range of minimum bounding box; B) ultimate range between two minimum bounding boxs; Draw by the convexity that to the single-point distance is the curve of definite value, single-point to the ultimate range of minimum bounding box must be single-point to each summit of minimum bounding box apart from maximal value, between each summit that further to draw two ultimate ranges between the minimum bounding box thus are two minimum bounding boxs apart from maximal value;
2) multiple spot distance and function: multiple spot distance and function handle single-point to the distance of multiple spot with;
3) multiple spot ultimate range and function: multiple spot ultimate range and function comprise following two kinds of situations: a) multiple spot to the ultimate range of minimum bounding box and; B) ultimate range between the minimum bounding box of multiple spot and another minimum bounding box and; Both of these case is respectively the multiple spot situation of ultimate range function, sums up for multiple spot to get final product;
4) maximum multiple spot distance and function: maximum multiple spot distance and function handle multiple spot in the minimum bounding box any possible ultimate range with; Drawn by the convexity that to the multiple spot distance is the curve of definite value, multiple spot must be distance and the maximal value of multiple spot to each summit of minimum bounding box to the ultimate range of minimum bounding box.
5. a kind of multiple spot based on convexity according to claim 1 adjacent querying method far away is characterized in that: the multiple spot that the minimum in the step 4) defines adjacent querying method concrete steps far away are:
1) answer set of a sky of initialization, the root node that R sets index is set to present node;
2) if present node is a leaf node, jump to 3); Otherwise be intermediate node, jump to 4);
3) k is the size of inquiry answer set; For the data point in all leaf nodes, if the multiple spot ultimate range of the minimum bounding box of it and query point and greatly than k answer distance in the answer set, and the multiple spot distance of it and query point and greatly than k answer distance in the answer set, so this data point is inserted in the answer set, and safeguards the no more than k of number of answer set; Finish;
4) node listing of a sky of initialization; For the child node in all intermediate nodes, if the multiple spot ultimate range between the minimum bounding box of its minimum bounding box and query point and bigger than k answer distance in the answer set, and the multiple spot ultimate range of its minimum bounding box and query point and bigger than k answer distance in the answer set is inserted into this child node in the node listing so;
5) to the node in the node listing according to multiple spot ultimate range and descending sort, for each node in the node listing,, jump to 2 if multiple spot ultimate range and bigger than the distance of k answer in the answer set is made as present node to this node).
6. a kind of multiple spot based on convexity according to claim 1 adjacent querying method far away is characterized in that: the adjacent querying method concrete steps far away of the preferably preferential multiple spot in the step 4) are:
1) Priority Queues of initialization, the inside have only the root node of a R tree index;
2) answer set of a sky of initialization;
3) if Priority Queues is not empty and the answer set number less than k, jump to 4); Otherwise, finish;
4) in Priority Queues, propose a node,, then data point is inserted in the answer set if this node is a data point; If this is an intermediate node, hereto each child node of node calculate maximum multiple spot distance and, all child nodes are inserted in the Priority Queues.
7. a kind of multiple spot based on convexity according to claim 1 adjacent querying method far away is characterized in that: the adjacent querying method concrete steps far away of the multiple spot based on convex closure in the step 4) are:
1) answer set of a sky of initialization;
2) convex closure of computational data collection;
3) summit of traversal convex closure, to each summit calculate multiple spot distance and, the summit of the value of finding maximum is inserted in the answer set; If the answer set number arrives k, then finish; Otherwise jump to 4);
4) in convex closure, find the left and right sides neighbours on answer summit, the answer summit is removed in convex closure; In the triangle that answer summit and left and right sides neighbours form, find a data point farthest to the distance of left and right sides neighbours' line; This data point is inserted in the convex closure, and recurrence about carry out the reconstruction of convex closure in two triangles; Jump to 3).
8. a kind of multiple spot based on convexity according to claim 1 adjacent querying method far away is characterized in that: the adjacent query optimization engine far away of the multiple spot in the step 5) need be considered following query characteristics:
1) Cha Xun query point number;
2) Cha Xun query point distributions situation;
3) Cha Xun answer set size;
4) Cha Xun data set size.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102270233A (en) * | 2011-07-29 | 2011-12-07 | 中国航天科技集团公司第五研究院第五一三研究所 | Searching method for convex hull |
| CN105528955A (en) * | 2014-09-30 | 2016-04-27 | 国际商业机器公司 | Method and device for generating road network |
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| US20090164182A1 (en) * | 2007-12-21 | 2009-06-25 | Schlumberger Technology Corporation | Multipoint geostatistics method using branch runlength compression and local grid transformation |
| CN101692230A (en) * | 2009-07-28 | 2010-04-07 | 武汉大学 | Three-dimensional R tree spacial index method considering levels of detail |
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| CN101093501A (en) * | 2007-07-31 | 2007-12-26 | 武汉大学 | Method for querying high performance, transparent distributed spatial database |
| US20090164182A1 (en) * | 2007-12-21 | 2009-06-25 | Schlumberger Technology Corporation | Multipoint geostatistics method using branch runlength compression and local grid transformation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102270233A (en) * | 2011-07-29 | 2011-12-07 | 中国航天科技集团公司第五研究院第五一三研究所 | Searching method for convex hull |
| CN102270233B (en) * | 2011-07-29 | 2013-03-27 | 中国航天科技集团公司第五研究院第五一三研究所 | Searching method for convex hull |
| CN105528955A (en) * | 2014-09-30 | 2016-04-27 | 国际商业机器公司 | Method and device for generating road network |
| CN105528955B (en) * | 2014-09-30 | 2018-02-06 | 国际商业机器公司 | Generate the method and its device of road network |
| US10452810B2 (en) | 2014-09-30 | 2019-10-22 | International Business Machines Corporation | Road network generation |
| US11270039B2 (en) | 2014-09-30 | 2022-03-08 | International Business Machines Corporation | Road network generation |
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| CN101984433B (en) | 2012-11-21 |
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