KR20190079354A - Partitioned space based spatial data object query processing apparatus and method, storage media storing the same - Google Patents

Abstract

The present invention relates to an apparatus for processing a spatial data object based on partition spaces, and to a method thereof. The apparatus comprises: a partition space generation unit generating a plurality of partition spaces by partitioning a data space including at least one spatial data object; and an index tree generation unit generating an index tree based on minimum boundary rectangle (MBR) information including all information on the plurality of partition spaces and the spatial data objects included in the corresponding partition spaces. Accordingly, the apparatus can efficiently partition the data space and perform efficient query processing using an appropriate table schema.

Description

TECHNICAL FIELD [0001] The present invention relates to an apparatus and method for processing a spatial data object query based on a divided space, and a recording medium on which the spatial data object query processing apparatus and method are recorded.

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a spatial data object query processing technique, and more particularly, to an apparatus and method for processing a spatial data object query based on a divided space capable of efficient partitioning of a data space and efficient query processing using an appropriate table schema .

The key to effective similarity query processing for multidimensional data such as spatial data is to store spatially adjacent objects physically close to each other and to access only data belonging to the area required for the search. However, cloud computing-based frameworks, such as Hadoop, which are typically used to handle large data, do not take into account spatial proximity between objects, so data that contains a large number of false positives .

Spatial data object queries can include range queries and kNN queries, and indexing methods using NoSQL database management systems such as HBase can provide fast data random access and efficient data access in distributed file systems. An effective framework for updating can be provided. A range query takes as input the query point q and the query radius r, and returns a set of data objects whose distance from the query point q is less than r. The kNN query receives the query point q and the number k of the closest neighbors as inputs and returns the closest set of k data objects from query point q as a result.

The problem with indexing in HBase is that it is designed to support only one-dimensional row keys. Thus, linearization techniques are generally used to store spatial data in an HBase system. This method subdivides the data space into cells of a grid shape and sequentially aligns the cells using the z-order. The row key of the spatial data can be generated using the z-sequence number of the cell.

4 is an exemplary diagram illustrating a process of processing a range query using a linearization technique. Referring to FIG. 4, when a range query 410 is executed, the minimum and maximum z-order values in the query range can be calculated. The query can then be processed by retrieving the rows whose row keys are within the z-order range. However, since the spatial proximity can not be fully guaranteed, the result of the linearization technique can often include many false positives (False Positives) 430.

5 is a diagram illustrating a process of processing a range query using a multidimensional indexing layer. Referring to FIG. 5, when the range query 510 is executed, it is possible to search for the index layer 530 first and reduce the positive error by accessing only rows having a row key associated with the related region. However, since this method has a large unit of spatial division, it may still require access to a large amount of positive errors in query processing.

Korean Patent Laid-Open No. 10-2016-0004781 (2016.01.13) relates to a method of processing an ontology query using a Big Data Framework, in which each node stored in a table stores a class type as a prefix and class Classes and properties are defined in the respective table column families. The database is analyzed based on the improved table schema of the HBase-based RDF repository, and the amount of data to be retrieved by applying the HBase filter To efficiently process the query, and to handle the overlapping triple pattern query that can not be processed by the existing query processor.

Korean Patent No. 10-1117709 (Feb. 20, 2012) discloses a multi-dimensional histogram method using a minimum data-uneven cover of a space division tree and a recording medium storing a program for executing the method. Unequal coverage on the basis of the unevenness of the data objects in each divided space in the space division tree formed by dividing the space data into the plurality of data segments, and generating a bucket of the histogram based thereon, The accuracy of the calculation of the estimated value of the selectivity of the region query can be secured even in a situation where it is not distributed.

Korean Patent Publication No. 10-2016-0004781 (Jan. 01, 2013) Korean Registered Patent No. 10-1117709 (Feb. 10, 2012)

An embodiment of the present invention seeks to provide an apparatus and method for processing a spatial data object query based on a divided space that can effectively partition a data space and efficiently process a query using an appropriate table schema.

An embodiment of the present invention is an apparatus and method for processing a spatial data object based on a divided space capable of generating an index tree based on a minimum bounding rectangle of a plurality of divided spaces and spatial data objects divided through recursive division .

An embodiment of the present invention is to provide an apparatus and method for processing a spatial data object query based on a divided space, which can effectively reduce positive errors by processing similar queries using an efficient index tree.

Among the embodiments, a divided space-based spatial data object query processing apparatus includes a divided space generating unit for generating a plurality of divided spaces by dividing a data space including at least one spatial data object, And an index tree generating unit for generating an index tree based on Minimum Boundary Rectangle (MBR) information including all spatial data objects included in the divided space.

The divided space generating unit may recursively subdivide the divided space with respect to the center point of the divided space until the density of the corresponding spatial data object becomes less than a specific standard for each of the plurality of divided spaces.

Wherein the index tree generating unit generates the index tree based on the key value generated based on the information on the divided space and the table including the value data generated based on the minimum bounding rectangle information, You can create a tree.

Wherein the index tree generating unit displays 0 for the divided space in the direction closer to the origin and 1 for the divisional space in the far direction with respect to each axis for each axis when the divided space is generated, The key value can be generated.

The index tree generating unit may display the previous partition information and the information about the current partition generated when the recursive partitioning of the partition space occurs.

The index tree generating unit may generate the index tree by generating an index table storing the row data related to the internal node and a data table storing the row data related to the leaf node.

The apparatus for processing a spatial data object based on the divided space may further include a query processing unit for processing a spatial data object query using the index tree.

The query processor may process the kNN query using the index tree, including a range query or query point including a query point and a query radius, and a nearest neighbor number.

Among the embodiments, the divided space-based spatial data object query processing method is a spatial data object query processing method performed in an apparatus for processing a spatial data object query based on a divided space, the method comprising: (a) (B) generating minimum boundary rectangle (MBR) information including both information on the plurality of divided spaces and spatial data objects included in the divided space, And generating an index tree based on the index tree.

Wherein the step (b) comprises the steps of: based on a table composed of a key value generated based on information on the divided space and row data including a value generated based on the minimum bounding rectangle information, An index tree may be generated.

In the step (b), each time the divided space is generated, the divided space in the direction closer to the origin relative to the respective axes is denoted by 0, the divided space in the far direction is denoted by 1, and the bits for each axis are concatenated And generating the key value.

In the step (b), when the recursive re-division of the divided space occurs, the step of displaying the previous divided information and the information about the re-divided space currently generated may be displayed.

The step (b) may be a step of generating the index tree by generating an index table storing the row data related to the internal node and a data table storing the row data related to the leaf node.

The divided space-based spatial data object query processing method may further include (c) processing the spatial data object query using the index tree.

The step (c) may be a step of processing a kNN query including the range query or the query point including the query point and the query radius using the index tree and the closest neighbor number.

In a preferred embodiment of the present invention, a recording medium is a computer-executable recording medium for recording a spatial data object query processing method performed in an apparatus for processing a spatial data object query based on a divided space, Generating an index tree based on minimum boundary rectangle (MBR) information including both information on the plurality of divided spaces and spatial data objects included in the divided space; .

The disclosed technique may have the following effects. It is to be understood, however, that the scope of the disclosed technology is not to be construed as limited thereby, as it is not meant to imply that a particular embodiment should include all of the following effects or only the following effects.

An apparatus and method for processing a spatial data object query based on a divided space according to an embodiment of the present invention can generate an index tree based on a minimum bounding rectangle of a plurality of divided spaces and spatial data objects divided through recursive quadrants have.

The apparatus and method for processing a spatial data object based on a divided space according to an embodiment of the present invention can effectively reduce false positives by processing similar queries using an efficient index tree.

1 is a view for explaining a spatial data object query processing system based on a divided space according to an embodiment of the present invention.
2 is a block diagram illustrating the spatial data object query processing apparatus shown in FIG.
3 is a flowchart illustrating a process of processing a spatial data object query in the spatial data object query processing apparatus shown in FIG.
4 is an exemplary diagram illustrating a process of processing a range query using a linearization technique.
5 is a diagram illustrating a process of processing a range query using a multidimensional indexing layer.
6 is an exemplary diagram illustrating a spatial division process performed by the spatial data object query processing apparatus.
FIG. 7 is an exemplary diagram illustrating a structure of an index tree node generated by the index tree generation unit shown in FIG. 2. FIG.
FIG. 8 is an exemplary diagram illustrating a table structure for an index tree node generated by the index tree generating unit shown in FIG. 2. FIG.
9 is a diagram illustrating an example of a range query process performed by a spatial data object query processing apparatus.

The description of the present invention is merely an example for structural or functional explanation, and the scope of the present invention should not be construed as being limited by the embodiments described in the text. That is, the embodiments are to be construed as being variously embodied and having various forms, so that the scope of the present invention should be understood to include equivalents capable of realizing technical ideas. Also, the purpose or effect of the present invention should not be construed as limiting the scope of the present invention, since it does not mean that a specific embodiment should include all or only such effect.

Meanwhile, the meaning of the terms described in the present application should be understood as follows.

The terms "first "," second ", and the like are intended to distinguish one element from another, and the scope of the right should not be limited by these terms. For example, the first component may be referred to as a second component, and similarly, the second component may also be referred to as a first component.

It is to be understood that when an element is referred to as being "connected" to another element, it may be directly connected to the other element, but there may be other elements in between. On the other hand, when an element is referred to as being "directly connected" to another element, it should be understood that there are no other elements in between. On the other hand, other expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.

It is to be understood that the singular " include " or "have" are to be construed as including the stated feature, number, step, operation, It is to be understood that the combination is intended to specify that it does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

In each step, the identification code (e.g., a, b, c, etc.) is used for convenience of explanation, the identification code does not describe the order of each step, Unless otherwise stated, it may occur differently from the stated order. That is, each step may occur in the same order as described, may be performed substantially concurrently, or may be performed in reverse order.

The present invention can be embodied as computer-readable code on a computer-readable recording medium, and the computer-readable recording medium includes all kinds of recording devices for storing data that can be read by a computer system . Examples of the computer-readable recording medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, and the like. In addition, the computer-readable recording medium may be distributed over network-connected computer systems so that computer readable codes can be stored and executed in a distributed manner.

All terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined. Commonly used predefined terms should be interpreted to be consistent with the meanings in the context of the related art and can not be interpreted as having ideal or overly formal meaning unless explicitly defined in the present application.

1 is a view for explaining a spatial data object query processing system based on a divided space according to an embodiment of the present invention.

Referring to FIG. 1, a spatial data object query processing system (hereinafter, referred to as a spatial data object query processing system) 100 includes a user terminal 110, a spatial data object query processing unit 130, (150).

The user terminal 110 may correspond to a computing device capable of inputting a spatial data object query and confirming a related response, and may be implemented as a smart phone, a notebook computer, or a computer, . ≪ / RTI > The user terminal 110 may be connected to the spatial data object query processing device 130 through a network and a plurality of user terminals 110 including the user terminal 1 10a to the user terminal n 110c may be connected to the spatial data object And may be connected to the query processing device 130 at the same time.

The spatial data object query processing unit 130 may be implemented as a computer or a server corresponding to a program capable of receiving and processing a spatial data object query using an index tree generated through spatial division. The spatial data object query processing device 130 can be wirelessly connected to the user terminal 110 via Bluetooth, WiFi, or the like, and can exchange data with the user terminal 110 via the network.

The spatial data object query processing apparatus 130 may be embodied including a database 150, and may be implemented independently of the database 150. When implemented independently of the database 150, the spatial data object query processing device 130 can be connected to the database 150 in a wired or wireless manner to exchange data.

The database 150 is a storage device capable of storing various information necessary for processing a spatial data object query. The database 150 may store information about at least one range query or kNN query received from the user terminal 110 and the result obtained by processing the query, and is not necessarily limited to a spatial data object query processor 130 may process the spatial data object query and store the collected or processed information in various forms.

In one embodiment, the database 150 may correspond to an HBase capable of storing and managing spatial data. Here, HBase is Apache HBase (Apache HBase), which may be an open, non-relational distributed database for the Hadoop platform. HBase can provide real-time random read / write functionality for data in the Hadoop Distributed File System (HDFS), with a data model similar to Google's Big Table, which provides fast, random access to large amounts of structured data.

The database 150 may include at least one independent sub-database that stores information belonging to a specific range, and at least one independent sub-database may be configured as an integrated database integrated into one. In the case of at least one independent sub-database, each of the sub-databases may be wirelessly connected via Bluetooth, WiFi, or the like and may exchange data with each other via the network. The database 150 may include a control unit that integrates each of the sub-databases into one unit and manages data exchange and control flow between the sub-databases.

2 is a block diagram illustrating the spatial data object query processing apparatus shown in FIG.

Referring to FIG. 2, the spatial data object query processing apparatus 130 may include a divided space generation unit 210, an index tree generation unit 230, a query processing unit 250, and a control unit 270.

The divided space generating unit 210 may generate a plurality of divided spaces by dividing a data space including at least one spatial data object. Here, the data space may correspond to a multidimensional space in which the spatial data object is defined. The spatial data object may be defined to include an attribute value corresponding to each dimension, and the divided space generating unit 210 may define a data space using a range of each attribute value that the spatial data object can have.

For example, when the data space has a two-dimensional coordinate system and the length of each axis corresponds to a square space having a length of 100 from the origin, the divided space generating unit 210 generates a divided space having a center position (50, 50) Can be divided into four divided spaces based on a straight line parallel to each axis passing through the corresponding center point. Each quadrant divided space may correspond to a square and may have the same size.

In one embodiment, the divided space generation unit 210 recursively divides the divided space into a plurality of divided spaces based on the center point of the corresponding divided space until the density of the corresponding spatial data object becomes less than a specific criterion You can redistribute. Here, the density of a spatial data object may correspond to the number of spatial data objects existing in a specific divided space. For example, when re-division is performed until the number of the spatial data objects existing in the divided space becomes equal to or less than 4, the divided space creating unit 210 creates a corresponding divided space, The divided space can be re-divided into four divided spaces, and if there is a divided space including four or more spatial data objects among the re-divided divided space, the re-divided process can be performed again for the divided space.

The index tree generating unit 230 may generate an index tree based on the information on the divided space and the Minimum Boundary Rectangle (MBR) information including the corresponding divided spatial data object. Here, the minimum bounding rectangle may correspond to a rectangle having the smallest width among the rectangles including all the divided spatial data objects. The index tree generating unit 230 may generate an index tree using information on each of the minimum bounding rectangles including both the specific divided space and the spatial data objects existing in the divided space.

In one embodiment, the index tree generating unit 230 generates a key value based on the information on the divided space and row data including a value generated based on the minimum bounding rectangle information You can create an index tree based on a table. For example, the index tree generating unit 230 may generate row data composed of (key, value) values and store the row data in one row of the HBase table.

In one embodiment, the index tree generating unit 230 displays 0 for the divided space in the direction closer to the origin and 1 for the far direction divided space for each axis for each axis when the divided space is generated, (bit) may be concatenated to generate a key value. For example, if there is a divided space generated through division, the index tree generating unit 230 generates '1' if the divided space exists in a direction far from the origin with respect to the x-axis and closer to the origin with respect to the y- , '0', and they can be concatenated and finally displayed as '10'.

In one embodiment, the index tree generating unit 230 may display the previous partition information and the information about the current partitioned space when the recursive partitioning of the partition space occurs. For example, if the index tree generation unit 230 generates the partitioned space indicated by '10' generated by partitioning, the divided partitioned spaces are divided into '00', '01', '10' And '11', and may be displayed as '1000', '1001', '1010', and '1011', respectively, connected to the previous division information '10'.

In one embodiment, the index tree generation unit 230 may generate an index tree by generating an index table storing row data related to internal nodes and a data table storing row data related to leaf nodes. The index table can store row data related to the internal node, and the row data related to the internal node is a key value generated based on the information on the partition space generated by the partition space generating unit 210, The number of spatial data objects in the tree, the child node information of the corresponding internal node, and the MBR information associated with each child node. The data table may store row data related to a leaf node, and the row data associated with the leaf node may include a key value generated based on information about the partition space associated with each leaf node, a space existing in the partition space associated with the leaf node And may include information about the data object.

The query processing unit 250 may process the spatial data object query using the index tree generated through the index tree generating unit 230. Here, the index tree may correspond to a Quadrand-based MBR (Q-MBR) tree. The Q-MBR and the Q-BMR tree using it will be described in more detail in FIGS.

In one embodiment, the query processing unit 250 uses the index tree generated through the index tree generating unit 230 to generate a kNN query including a query range or query point including a query point and a query radius and a nearest neighbor number Lt; / RTI > More specifically, the query processing unit 250 searches a Q-MBR tree according to Breadth First Search (BFS) while searching for an index, and calculates a row key for loading the next iteration .

In one embodiment, the query processor 250 may use three sets of N, I, and D for an index search for range query processing. Here, N may store the loaded node for the search in the current iteration. I may correspond to a set of row keys to be loaded in the index table in the next iteration. D may correspond to a set of row keys to be loaded in the data table.

The query processing unit 250 may insert the row key of the root node into I and then start the search by loading the node in the index table. The query processing unit 250 may insert a plurality of row keys for I-level nodes into I instead of inserting only a single row key for the root node if the maximum depth of the index tree is known.

The query processing unit 250 can calculate the minimum distance between the MBR of the child node and the query point q when the current node is an internal node and insert the row key of the child whose distance is within the query radius r. The query processor 250 may search all nodes in N and prefetch additional nodes using the row key stored in I and store the result in N for the next iteration.

When the query processing unit 250 reaches the leaf node in the index search process, the leaf key of the leaf node can be stored in D, and the related spatial data object can be loaded at the end of the query processing. The query processing unit 250 may load the spatial data object stored in the leaf node in D if there is no node in I and respond to the range query by calculating the distance between the spatial data object and the query point q.

The query processing unit 250 may insert only the spatial data object whose distance from the query point q is less than or equal to the query radius r into the result set. The query processing unit 250 can provide information about the spatial data object belonging to the result set to the user terminal 110 as a query processing result.

In one embodiment, the query processor 250 may maintain two priority queues of N and Q for kNN query processing. Here, N can store the node in ascending order of the minimum distance from the query point to MBR, and Q can store the candidate set of kNN in descending order.

The query processing unit 250 can process the kNN query in two steps. In the first step, the query processing unit 250 can search the Q-MBR tree to find the leaf key of the leaf node using the approximate range r. Here, the approximate range may correspond to a minimum distance that ensures a sufficient number of spatial data objects to find the desired number of neighbors.

The query processing unit 250 may sequentially check the nodes stored in N in the index search process to determine whether the minimum distance has a child node smaller than r. The query processing unit 250 may insert the row key of the child node into the list I if the minimum distance of the child node is less than r. The query processing unit 250 can set the range r to be very large at the initial stage and can update the maximum distance of the node from N when the number of objects of the node closer to the current node exceeds k.

If the current node is a leaf node, the query processing unit 250 may store the row key of the current node in the list L and load it together. The query processing unit 250 can load the node where the row key is stored in I if there are no more nodes in N. [ The query processing unit 250 may terminate the first step when there is no node in N and there is no row key in I. The query processing unit 250 can load the spatial data object of the leaf node stored in L after the index search and evaluate the distance to identify the query result set. The query processing unit 250 can store the evaluated spatial data object in Q and return a query result set when there are no more spatial data objects to evaluate.

The control unit 270 controls the overall operation of the spatial data object query processing apparatus 130 and manages control flow or data flow between the divided space generation unit 210, the index tree generation unit 230, and the query processing unit 250 can do.

3 is a flowchart illustrating a process of processing a spatial data object query in the spatial data object query processing apparatus shown in FIG.

Referring to FIG. 3, the spatial data object query processing unit 130 may generate a plurality of divided spaces by dividing a data space including at least one spatial data object through the divided space generating unit 210 S310). In one embodiment, the divided space generation unit 210 recursively divides the divided space into a plurality of divided spaces based on the center point of the corresponding divided space until the density of the corresponding spatial data object becomes less than a specific criterion You can redistribute.

The spatial data object query processing unit 130 generates an index tree based on the minimum bounding rectangle information including both the information on the divided space and the spatial data objects included in the divided space through the index tree generating unit 230 (Step S330). In one embodiment, the index tree generating unit 230 generates a key value based on the information on the divided space and row data including a value generated based on the minimum bounding rectangle information You can create an index tree based on a table.

In one embodiment, the index tree generating unit 230 displays 0 for the divided space in the direction closer to the origin and 1 for the far direction divided space for each axis for each axis when the divided space is generated, (bit) may be concatenated to generate a key value. In one embodiment, the index tree generating unit 230 may display the previous partition information and the information about the current partitioned space when the recursive partitioning of the partition space occurs. In one embodiment, the index tree generation unit 230 may generate an index tree by generating an index table storing row data related to internal nodes and a data table storing row data related to leaf nodes.

The spatial data object query processing unit 130 may process the spatial data object query using the index tree generated by the index tree generating unit 230 through the query processing unit 250 (step S350). In one embodiment, the query processing unit 250 may use an index tree to process a kNN query that includes a range query or query point including the query point and query radius and the closest neighbor number.

6 is an exemplary diagram illustrating a spatial division process performed by the spatial data object query processing apparatus.

Referring to FIG. 6, the spatial data object query processor 130 is formed by two axes vertically intersecting the origin 611 through the divided space generation unit 210 and each having a maximum size of 80 Data space 610 can be defined. The divided space generating unit 210 may generate a plurality of divided spaces by dividing the data space 610 into four spaces based on the center points 40 and 40 of the space. The divided space generating unit 210 can recursively re-divide the divided space until the density of the corresponding spatial data object becomes less than a specific criterion. In FIG. 6, the density may correspond to 4. That is, the divided space generating unit 210 recursively recursively divides the space until the number of the space data objects existing in the single divided space becomes 4 or less.

The spatial data object query processor 130 may generate a minimum bounding rectangle (MBR) 613 for a spatial data object existing in each quadrant after dividing the data space 610. The spatial data object query processor 130 may generate a Quadrand-based MBR (Q-MBR) 630 based on the information about the partition space 631 and the MBR 633 and store the same in the HBase database . The spatial data object query processor 130 may store the Q-MBR 630 in the HBase table and use the Q-MBR 630 as a building block of the hierarchical index tree.

Information about the partition space 631 can be used as a row key to reduce the update cost. Each column value can be stored in a separate key-value pair format, and updating the row key can result in a large number of inserts in the corresponding key-value pair. Therefore, information of MBR 633 frequently updated can not be used as a row key. In the worst case, this can be the case when a new row key is created for a grouped spatial data object when an update occurs and all key-value pairs are regenerated. Thus, the MBR 633 information can be stored in columns that are relatively difficult to update and can be used for distance calculations during spatial query processing.

FIG. 7 is an exemplary diagram illustrating a structure of an index tree node generated by the index tree generation unit shown in FIG. 2. FIG.

Referring to FIG. 7, the spatial data object query processor 130 may generate an index tree for generating and managing the Q-MBR through the index tree generating unit 230. Here, the index tree may correspond to a Q-MBR tree and may be implemented as an HBase table or a memory index. The Q-MBR tree may be similar to a quadtree structure.

In one embodiment, the index tree generating unit 230 generates an index table for storing row data related to an internal node and a data table for storing row data related to a leaf node, thereby generating an index tree Can be generated. An internal node can be stored in an index table and consists of quadrant information of the node, MBRs of children of the child nodes, and number of objects of spatial data contained in the subtree . A leaf node can be stored in a data table, and can be composed of a quadrant, a number of objects of a leaf node, and a list of spatial data objects.

FIG. 8 is an exemplary diagram illustrating a table structure for an index tree node generated by the index tree generating unit shown in FIG. 2. FIG.

Referring to FIG. 8, it is possible to directly access the spatial data object stored in the data table by using the quadrant information instead of the pointer to the spatial data object existing in the leaf node area. The configuration of the table included in FIG. 8 includes an example of a hierarchical Q-MBR index structure for the spatial data object shown in FIG. Each Q-MBR may correspond to the leaf node of the index tree, and the Q-MBR of the parent node may be represented by a quadrant containing the Q-MBRs of the child node and the MBR.

The '#objects' value of the 'Meta family' entry in the index table represents the number of spatial data objects in the tree whose root node is the node. For example, since the value of '#objects' is 15 for a node whose 'Rowkey' entry value is 'Root', the number of spatial data objects in the tree is 15.

9 is a diagram illustrating an example of a range query process performed by a spatial data object query processing apparatus.

Referring to FIG. 9, the spatial data object query processing device 130 can process the range query through the query processing unit 250. [ The query processing unit 250 can insert the row key of R0 into L and start by loading it into N. [ Since the two child nodes R2 and R3 overlap the query range, the query processing unit 250 can insert the row keys of R2 and R3 into I in the first iteration and load them together from the index table. Similarly, row keys of R9 and R6 can be inserted and loaded in the second iteration. The query processing unit 250 can check the spatial data objects R9 and R6 for the response to the range query at the next iteration since R9 and R6 are leaf nodes. The result set R includes two spatial data objects p1 and p2, and the query processor 250 can terminate the search because there are no nodes to search.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the present invention as defined by the following claims It can be understood that

100: Spatial Data Object Query Processing System Based on Partition Space
110: user terminal 130: spatial data object query processing device
150: Database
210: Split Space Generation Unit 230: Index Tree Generation Unit
250: query processing unit 270:
410: Range Query 430: Positive Error
510: Range Query 530: Index Layer
610: Data space 611: Origin
613: minimum bounding rectangle 630: Q-MBR
631: partition space 633: MBR

Claims (16)

A divided space generating unit for dividing a data space including at least one spatial data object to generate a plurality of divided spaces; And
And an index tree generating unit for generating an index tree based on minimum boundary rectangle (MBR) information including both information on the plurality of divided spaces and spatial data objects included in the divided space, A spatial data object query processing unit.
2. The apparatus of claim 1,
And dividing the divided space by the center point of the divided space, until the density of the corresponding spatial data object becomes less than a specific reference with respect to each of the plurality of divided spaces. Object query processing device.
2. The apparatus of claim 1, wherein the index tree generating unit
Generating the index tree based on a table composed of a key value generated based on information on the divided space and a row value including a value generated based on the minimum bounding rectangle information A spatial data object query processing unit based on a divided space.
4. The apparatus of claim 3, wherein the index tree generating unit
Each time the divided space is generated, the divided space in the direction closer to the origin is indicated as 0 and the divided space in the far direction is indicated as 1 with respect to each axis, and the bit value for each axis is concatenated to generate the key value Wherein the spatial data object query processing unit comprises:
5. The apparatus of claim 4, wherein the index tree generating unit
Wherein when the recursive re-division of the divided space occurs, information is displayed in a manner of linking the previous partition information and the information on the re-partitioned space currently being generated.
4. The apparatus of claim 3, wherein the index tree generating unit
Wherein the index tree is generated by generating an index table for storing the row data for the internal node and a data table for storing the row data for the leaf node.
The method according to claim 1,
And a query processing unit for processing the spatial data object query using the index tree.
8. The apparatus of claim 7, wherein the query processing unit
Wherein the processing unit processes the kNN query including the range query or query point including the query point and the query radius using the index tree and the closest neighbor number.
A spatial data object query processing method performed by a spatial data object query processor based on a divided space,
(a) dividing a data space including at least one spatial data object to generate a plurality of divided spaces; And
(b) generating an index tree based on minimum boundary rectangle (MBR) information including both the information on the plurality of divided spaces and the spatial data objects included in the divided space; Based spatial data object query processing method.
10. The method of claim 9, wherein step (b)
A step of generating the index tree based on a table composed of a key value generated based on information on the divided space and a row value including a value generated based on the minimum bounding rectangle information The spatial data object query processing method comprising:
11. The method of claim 10, wherein step (b)
Each time the divided space is generated, the divided space in the direction closer to the origin is indicated as 0 and the divided space in the far direction is indicated as 1 with respect to each axis, and the bit value for each axis is concatenated to generate the key value Wherein the spatial data object query processing step comprises:
12. The method of claim 11, wherein step (b)
When the recursive re-division of the divided space occurs, displaying the previous divided information and the information about the re-divided space that is currently generated in a manner that connects the divided information.
11. The method of claim 10, wherein step (b)
And generating the index tree by storing the index table storing the row data related to the internal node and the data table storing the row data related to the leaf node. .
10. The method of claim 9,
(c) processing the spatial data object query using the index tree. < RTI ID = 0.0 > 31. < / RTI >
15. The method of claim 14, wherein step (c)
And processing a kNN query including a range query or query point including a query point and a query radius using the index tree and a closest neighbor number.
A computer-executable recording medium for recording a spatial data object query processing method performed in an apparatus for processing a spatial data object based on a divided space,
Dividing a data space including at least one spatial data object into a plurality of divided spaces; And
And generating an index tree based on minimum boundary rectangle (MBR) information including both information on the plurality of divided spaces and spatial data objects included in the divided space.

Images