CN113656447B - Skyline-like query method in three-dimensional obstacle space - Google Patents

Skyline-like query method in three-dimensional obstacle space Download PDF

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CN113656447B
CN113656447B CN202111021415.3A CN202111021415A CN113656447B CN 113656447 B CN113656447 B CN 113656447B CN 202111021415 A CN202111021415 A CN 202111021415A CN 113656447 B CN113656447 B CN 113656447B
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刘永山
郝天保
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龚翔
孔德瀚
景宁
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Yanshan University
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Abstract

The invention discloses a Skyline-like query method in a three-dimensional obstacle space, which belongs to the technical field of computers and comprises the following steps of: designing a data structure of each object in the three-dimensional obstacle space; solving a Skyline point set of a non-space attribute classS 1 And is based onS 1 Constructing a three-dimensional space dominant domain; solving Skyline-like point sets independent of spatial attributes according to dominant domainsS 2 The method comprises the steps of carrying out a first treatment on the surface of the Obtaining attribute preference weightsWReturning a result set based on the attribute preference valuesS. According to the method, the Skyline-like point set which is not subjected to domination on the non-spatial attribute is solved through the BBS algorithm, the barrier distance is calculated through the Dijkstra algorithm, the data point with advantages on the spatial attribute is solved, and the Skyline-like result set based on the attribute preference value is returned according to the preference value of the user on the attribute, so that the result set has higher accuracy, higher practical reference value and practical significance.

Description

Skyline-like query method in three-dimensional obstacle space
Technical Field
The invention relates to the technical field of computers, in particular to a Skyline-like query method in a three-dimensional obstacle space.
Background
Among many queries, skyline query, as a technique for solving a typical multi-objective problem, has wide application in knowledge mining, market analysis, decision making, and the like.
To meet higher user requirements, more and more methods for solving the practical problems are proposed. Various query methods provide support for spatial knowledge extraction and spatial data analysis, so that spatial database technology plays an increasingly important role in life. However, with the rapid development of computer technology in recent years, the study of spatial databases has gradually shifted from low dimensions to three dimensions and even to high dimensions. The three-dimensional space is used as an objective reaction of the real world, has more practical significance for researching the three-dimensional space, but the increase of the dimension inevitably leads to the increase of the data volume, and the problem to be considered is more complex, so that the mining of useful information in a plurality of data points of a data set becomes a difficult problem of a plurality of queries of a space database.
Assuming that there is a scenario where the user first needs to pay attention to non-spatial attributes of the data set, such as price, score, etc., and second, pay attention to the fact that the locations of the query points in the query set are closer, and finally, based on the degree of emphasis of the user on each attribute, data points meeting the user's needs are returned in sequence. Under the scene, the traditional Skyline query and the result set of the space Skyline query can not meet the condition, so that researchers are prompted to improve the Skyline query algorithm, and the result set after the query can meet the requirements. The increase in dimension inevitably leads to an increase in data volume, and the problem to be considered is more complex, so that useful information is mined in a plurality of data points of a data set, and the problem of a plurality of queries of a spatial database is solved. Therefore, the improved Skyline query is applied to the three-dimensional obstacle space, so that the result set has higher accuracy and the query has practical significance.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a Skyline-like query method in a three-dimensional obstacle space, which realizes that data points meeting the requirements of users are returned in sequence based on the degree of the importance of the users on each attribute, so that a result set obtained after query has higher practical reference value.
In order to solve the technical problems, the invention adopts the following technical scheme:
a Skyline-like query method in a three-dimensional obstacle space comprises the following steps:
s1: designing a data structure of each object in the data set, and respectively storing a non-spatial attribute value and a spatial attribute value of each data point in the data set P by using R tree1 and R tree 2;
s2: solving a Skyline point set S of the non-spatial attribute class according to the object data structure designed in the S1 1 And constructing a dominant domain B based on the result set;
s3: solving Skyline-like point set S with non-dominant spatial attribute according to dominant domain B obtained in S2 2 ；
S4: and obtaining attribute preference weight W, and returning to the Skyline-like point set S based on the attribute preference value.
The technical scheme of the invention is further improved as follows: in S1, each object in the design dataset includes a data object P, a query object Q, and an obstacle O.
The technical scheme of the invention is further improved as follows: in S1, the ids of the same data points in rTree1 and rTree2 are the same.
The technical scheme of the invention is further improved as follows: s2, comprising the following specific steps:
s201: solving a Skyline-like point set S not subject to non-spatial attributes in a data set 1 In the process of acquiring the object which is not subject to the dominance on the basis of the non-space attribute in the data set, solving by means of a traditional algorithm BBS, and assisting the query by means of an R tree;
s202: data point set S that is not subject to non-spatial attributes in BBS algorithm output data set P 1 Then, constructing a dominant domain B;
s203: calculating each query point Q and Skyline-like data point set S in the query set Q 1 Constructing a sphere Si (q, s) with the q as a circle center and the calculated distance as a radius;
s204: find the minimum envelope B of the sphere S, where B is the data point set S 1 Is a dominant domain.
The technical scheme of the invention is further improved as follows: s3, comprising the following specific steps:
s301: nodes in rTree2 that do not intersect B and are included in result set S 1 Filtering the data points in the data points;
s302: calculating the barrier distance from each query point to the data point in the three-dimensional space by using Dijkstra algorithm;
s303: finding a set of points S that are not subject to spatial properties 2 。
The technical scheme of the invention is further improved as follows: s4, the method comprises the following specific steps:
s401: according to the weight of each attribute of the user, weight distribution is carried out on different attributes, and the weight degree of each attribute of the user, namely RankOfAtt1, rankOfAtt2 and RankOfAtt3 is input;
s402: collecting the weight degree values of the user on each attribute, adding the weight degree values, and normalizing the weight degree values; normalizing the degree of the opinion according to the proportion of each attribute into the weight value of the attribute, namely
Figure BDA0003242096570000031
S403: s in result set 1 And S is 2 And sorting according to the attribute preference values, and returning the sorted Skyline-like point set S to the user.
The technical scheme of the invention is further improved as follows: in S401, the degree of emphasis is divided into 3 levels {1,2,3}, and a value of 1 indicates that the degree of emphasis on the attribute is the largest; and defaulting to be indistinguishable in the initial state of the importance degree.
The technical scheme of the invention is further improved as follows: the data points in the set S satisfy at least one of the following conditions:
(1)p i ≤p′ i (1.ltoreq.i.ltoreq.d) and
Figure BDA0003242096570000033
(2)
Figure BDA0003242096570000032
and->
Figure BDA0003242096570000034
By adopting the technical scheme, the invention has the following technical progress:
1. the Skyline point query method and the Skyline point query device solve the Skyline point query problem of the three-dimensional space by increasing the definition of the Skyline of the three-dimensional space on the distance.
2. The invention utilizes BBS algorithm to realize effective solution of Skyline-like point set which is not subject to space attribute.
3. According to the invention, the constructed three-dimensional barrier space three-dimensional visual view is used for calculating the barrier distance by using Dijkstra algorithm, so that the accurate solution of the data points with advantages on the spatial attribute is realized.
4. According to the invention, the three-dimensional space dominant domain is constructed, the data are pruned, and the time complexity and the space complexity of algorithm inquiry are effectively reduced under the conditions of dimensional rising and data volume increasing.
5. According to the method, the non-spatial attributes of the data set are considered, the preference of the user to the attributes is quantized, and the Skyline-like result set based on the attribute preference value is returned, so that the method is closer to practical application.
6. Compared with the traditional Skyline query method, the result set obtained by the Skyline-like query method in the three-dimensional obstacle space has higher accuracy and higher actual reference value and actual significance.
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Fig. 1 is a schematic flow chart of a Skyline-like query method in a three-dimensional obstacle space according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
as shown in FIG. 1, the Skyline-like query method in the three-dimensional obstacle space specifically comprises the following steps:
s1: the data structure of each object in the data set is designed, and the R tree rTree1 and the R tree rTree2 are used for respectively storing the non-space attribute value and the space attribute value of each data point in the data set P.
The data structure of each object in the data set is designed, including the structure of each data object P, query object Q and obstacle O used in the design query. Wherein each data object has a spatial attribute and a non-spatial attribute. The spatial attribute is position information (x, y, z), the non-spatial attribute includes price, score rank, etc., the query object only contains the spatial attribute information (x, y, z), in addition, for the obstacle, the reference is expressed by using O (C, R, θ), C represents the center point of the obstacle, R represents the distance of the center point from each coordinate plane of the cuboid, and θ is the rotation angle of the spatial object on the horizontal plane. And the R tree rTree1 and the R tree rTree2 are used for respectively storing the non-space attribute value and the space attribute value of each data point P in the data set P, and the ids of the same data point P in the rTree1 and the rTree2 correspond to each other.
S2: solving a Skyline point set S of the non-spatial attribute class according to the object data structure designed in the S1 1 And constructing a dominant domain B based on the result set, comprising the following specific steps:
s201: solving a Skyline-like point set S not subject to non-spatial attributes in a data set 1 . In acquiring the non-spatially attribute-based non-dominated objects in the dataset, a solution is performed by means of a conventional algorithm BBS (branch and bound skyline). In the process, the query is assisted by the R tree, and the comparison times are reduced and the query efficiency is improved by utilizing the special property of the R tree. And the nodes in the R tree are sequentially processed to obtain data points with advantages of the data value in the data set P.
S202: data point set S that is not subject to non-spatial attributes in BBS algorithm output data set P 1 Thereafter, dominant domain B is constructed.
S203: calculating each query point Q and Skyline-like data point set S in the query set Q 1 And constructing a sphere Si (q, s) with the q as a center and the calculated distance as a radius. The distance used in this step is the obstacle distance, and the calculation of the obstacle distance is seen in step S302.
S204: and according to the distance information of the balls based on each query point, the minimum outer packing box B of the group of balls S is obtained. B is a data point set S 1 Is a dominant domain.
S3: solving Skyline-like point set S with non-dominant spatial attribute according to dominant domain B obtained in S2 2 The method comprises the following specific steps:
s301: nodes in rTree2 that do not intersect B and are included in result set S 1 The data points in (a) are filtered.
S302: the obstacle distance between each inquiry point and the data point in the three-dimensional space is calculated.
S3021: before calculating the barrier distance between the p and q points, the intersecting barrier set after connecting the p and q points needs to be calculated, because the barrier has random rotation angles in space, the line segment pq needs to be projected onto three axes where each barrier is located, the intersection point corresponding to the projected line segment pq is calculated according to the boundary value, if the line segment is parallel to a certain coordinate axis, the comparison process is relatively simple, only the line segment is required to be compared to determine whether the line segment is located in an area in the dimension, otherwise, whether the t value corresponding to each coordinate axis direction determined by each intersection point is overlapped or not is judged, and if the overlap represents that the barrier is intersected with the line segment pq. All intersecting obstacle sets are sorted out.
The intersection of the line segment between the two points of the later comparison with the entire obstacle set can be reduced by filtering the obstacle before the candidate obstacle is calculated. For the process of acquiring a candidate obstacle set of a certain data point p based on a query point q, q is mainly combined with each obstacle o i Comparing the distance of q with the Euclidean distance of p, if smaller, indicating the obstacle o i May affect the obstacle distance between q and p by adding it to the candidate obstacle set, otherwise, by adding the obstacle o i And directly deleting.
S3022: the method comprises the steps of constructing a visible view VG based on a three-dimensional obstacle space between two points, wherein the visible view VG comprises a vertex set V and an edge set E, the V comprises vertexes intersecting the obstacle set besides the p point and the q point, the E is a square matrix with symmetry, and when the edge set information E is constructed, the visibility among the vertexes is required to be detected, and the communication information among the vertexes is counted.
In the process of calculating E, each vertex in V is traversed, and then each distance information is calculated. Since the distance information of the E-set has symmetry, only the lower half of the E-set is calculated, p is used 0 And p is as follows 1 And traversing the vertexes in the vertex set respectively, and assigning the calculated distance between the vertexes to the positions corresponding to the E set. In the calculation process, the default distance is the point p 0 To point p 1 Is full of Euclidean distance of (2)The distance is modified enough for the following case.
(1) When p is 0 And p is as follows 1 When the points are identical, namely the points are the same, no path exists between the points defaulted to be identical in the invention, the N is directly given, and the N represents the infinite distance;
(2) When p is 0 And p 1 The distance is the length, width and height of a bounding box representing the obstacle, but if two points are exactly positioned on the body diagonal of the bounding box, the distance between the two points is N;
(3) When p is 0 And p 1 When the two points are positioned on different barriers, the intersection rule of the line segment and the OBB bounding box is needed to be used when the distance between the two points is calculated, whether the barrier shielding exists between the two points is judged, and if yes, the numerical value in the matrix is modified to be N.
If the special condition is not satisfied, the distance of the corresponding position in the matrix is not modified, and the distance is Euclidean distance between two points.
Finally, the edge set E is returned.
S3023: on the basis of the visual view, the Dijkstra algorithm is used for selecting an edge in the E set as a path selection, and solving the shortest path between two points, wherein the path is the shortest barrier distance between the two points.
S303: finding a set of points S that are not subject to spatial properties 2 . Hypothesis set S 2 Storing the result set, traversing nodes in rTree2, storing the result set in an ordered heap H, sequentially processing a heap top element head, and if the head is in a dominant domain B and is not subjected to S 2 When the data object space is supported, the corresponding processing is carried out by judging the node type of the head. When the head is judged to be a leaf node, the head is added to S 2 If not, the head is an intermediate node, and the child node is added into H. When the heap is empty, S 2 The method is the method.
In this process, the following pruning strategy was found to exist: giving the Euclidean distance from the intermediate node entry of the R tree to the query point q as EurDis, if
Figure BDA0003242096570000071
All data points under the intermediate node entry are filtered out directly, wherein ObsDis is the obstacle distance between the solving query point q and a certain type of Skyline point s. In the process of traversing the R tree nodes, the R tree nodes are processed according to the pruning strategy, so that the number of access nodes in the execution process of the method can be reduced. />
S4: and obtaining attribute preference weight W, and returning to the Skyline-like point set S based on the attribute preference value.
S401: and according to the weight situation of the user on each attribute, weight distribution is carried out on different attributes, and the weight distribution result can accurately reflect the preference of the user as much as possible. And inputting the importance degree of each attribute of the user, namely RankOfAtt1, rankOfAtt2 and RankOfAtt3. The degree of emphasis is divided into 3 levels {1,2,3}, with a value of 1 indicating the greatest degree of emphasis on the attribute.
For the degree of emphasis, the initial state defaults to not distinguish, i.e., the class Skyline returns directly to the data object in the dataset that is not subject to the other data point classes, and is not ordered by attribute preference value.
S402: after collecting the weight degree value of each attribute, the user adds the weight degree values, and normalizes the weight degree values. Normalizing the degree of the opinion according to the proportion of each attribute into the weight value of the attribute, namely
Figure BDA0003242096570000072
S403: s in result set 1 And S is 2 And sorting according to the attribute preference values, and returning the sorted Skyline-like point set S to the user.
Wherein the data points in set S satisfy at least one of the following conditions:
(1)p i ≤p′ i (1.ltoreq.i.ltoreq.d) and
Figure BDA0003242096570000074
(2)
Figure BDA0003242096570000073
and->
Figure BDA0003242096570000075
In summary, the Skyline point query problem of the three-dimensional space is solved by increasing the definition of the Skyline of the three-dimensional space with respect to the distance; utilizing a BBS algorithm to realize effective solution of the Skyline-like point set which is not subject to the non-spatial attribute; calculating the obstacle distance through the constructed three-dimensional obstacle space three-dimensional visible view and using Dijkstra algorithm, so as to realize accurate solving of data points with advantages on space attributes; according to the invention, the three-dimensional space dominant domain is constructed, the data are pruned, and the time complexity and the space complexity of algorithm inquiry are effectively reduced under the conditions of dimensional rising and data volume increasing; taking non-spatial attributes of the data set into consideration, quantifying the preference of the user to the attributes, and returning a Skyline-like result set based on attribute preference values, so that the method is closer to practical application; compared with the traditional Skyline query method, the result set obtained by the Skyline-like query method in the three-dimensional obstacle space has higher accuracy and higher actual reference value and actual significance.

Claims (7)

1. A Skyline-like query method in a three-dimensional obstacle space is characterized by comprising the following steps of: the method comprises the following steps:
s1: designing a data structure of each object in the data set, and respectively storing a non-spatial attribute value and a spatial attribute value of each data point in the data set P by using R tree1 and R tree 2;
s2: solving a Skyline point set S of the non-spatial attribute class according to the object data structure designed in the S1 1 And constructing a dominant domain B based on the result set;
s2, comprising the following specific steps:
s201: solving a Skyline-like point set S not subject to non-spatial attributes in a data set 1 In the process of acquiring the object which is not subject to the dominance on the basis of the non-space attribute in the data set, solving by means of a traditional algorithm BBS, and assisting the query by means of an R tree;
s202: data point set S that is not subject to non-spatial attributes in BBS algorithm output data set P 1 Then, constructing a dominant domain B;
s203: calculating each query point Q and Skyline-like data point set S in the query set Q 1 Constructing a sphere Si (q, s) with the q as a circle center and the calculated distance as a radius;
s204: find the minimum envelope B of the sphere S, where B is the data point set S 1 Is a dominant domain of (2);
s3: solving Skyline-like point set S with non-dominant spatial attribute according to dominant domain B obtained in S2 2 ；
S4: and obtaining attribute preference weight W, and returning to the Skyline-like point set S based on the attribute preference value.
2. The method for querying Skyline in three-dimensional obstacle space according to claim 1, wherein the method comprises the following steps: in S1, each object in the design dataset includes a data object P, a query object Q, and an obstacle O.
3. The method for querying Skyline in three-dimensional obstacle space according to claim 1, wherein the method comprises the following steps: in S1, the ids of the same data points in rTree1 and rTree2 are the same.
4. The method for querying Skyline in three-dimensional obstacle space according to claim 1, wherein the method comprises the following steps: s3, comprising the following specific steps:
s301: nodes in rTree2 that do not intersect B and are included in result set S 1 Filtering the data points in the data points;
s302: calculating the barrier distance from each query point to the data point in the three-dimensional space by using Dijkstra algorithm;
s303: finding a set of points S that are not subject to spatial properties 2 。
5. The method for querying Skyline in three-dimensional obstacle space according to claim 1, wherein the method comprises the following steps: s4, the method comprises the following specific steps:
s401: according to the weight of each attribute of the user, weight distribution is carried out on different attributes, and the weight degree of each attribute of the user, namely RankOfAtt1, rankOfAtt2 and RankOfAtt3 is input;
s402: collecting the weight degree values of the user on each attribute, adding the weight degree values, and normalizing the weight degree values; normalizing the degree of the opinion according to the proportion of each attribute into the weight value of the attribute, namely
Figure FDA0004168279060000021
S403: s in result set 1 And S is 2 And sorting according to the attribute preference values, and returning the sorted Skyline-like point set S to the user.
6. The method for querying Skyline in three-dimensional obstacle space according to claim 5, wherein the method comprises the following steps: in S401, the degree of emphasis is divided into 3 levels {1,2,3}, and a value of 1 indicates that the degree of emphasis on the attribute is the largest; and defaulting to be indistinguishable in the initial state of the importance degree.
7. The method for querying Skyline in three-dimensional obstacle space according to claim 1, wherein the method comprises the following steps: the data points in the set S satisfy at least one of the following conditions: (1) P is p i ≤p i ' (1.ltoreq.i.ltoreq.d) and
Figure FDA0004168279060000031
/>
(2)
Figure FDA0004168279060000032
and->
Figure FDA0004168279060000033
/>
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CN105005584A (en) * 2015-06-17 2015-10-28 南京航空航天大学 Multi-subspace Skyline query computation method
CN107046557A (en) * 2016-12-14 2017-08-15 大连大学 The intelligent medical calling inquiry system that dynamic Skyline is inquired about under mobile cloud computing environment
CN109947904A (en) * 2019-03-22 2019-06-28 东北大学 A kind of preference space S kyline inquiry processing method based on Spark environment

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基于高维空间的在线高效子空间Skyline算法――CSky;周红福;宫学庆;郑凯;周傲英;;计算机学报(08);全文 *

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